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1. A randomized clinical trial assessing the effect of automated medication-targeted alerts on acute kidney injury outcomes

Author: F. Perry Wilson, Yu Yamamoto, Melissa Martin, Claudia Coronel-Moreno, Fan Li, Chao Cheng, Abinet Aklilu, Lama Ghazi, Jason H. Greenberg, Stephen Latham, Hannah Melchinger, Sherry G. Mansour, Dennis G. Moledina, Chirag R. Parikh, Caitlin Partridge, Jeffrey M. Testani, and Ugochukwu Ugwuowo
Subjects: Science
Abstract: Abstract Acute kidney injury is common among hospitalized individuals, particularly those exposed to certain medications, and is associated with substantial morbidity and mortality. In a pragmatic, open-label, National Institutes of Health-funded, parallel group randomized controlled trial (clinicaltrials.gov NCT02771977), we investigate whether an automated clinical decision support system affects discontinuation rates of potentially nephrotoxic medications and improves outcomes in patients with AKI. Participants included 5060 hospitalized adults with AKI and an active order for any of three classes of medications of interest: non-steroidal anti-inflammatory drugs, renin-angiotensin-aldosterone system inhibitors, or proton pump inhibitors. Within 24 hours of randomization, a medication of interest was discontinued in 61.1% of the alert group versus 55.9% of the usual care group (relative risk 1.08, 1.04 – 1.14, p = 0.0003). The primary outcome – a composite of progression of acute kidney injury, dialysis, or death within 14 days - occurred in 585 (23.1%) of individuals in the alert group and 639 (25.3%) of patients in the usual care group (RR 0.92, 0.83 – 1.01, p = 0.09). Trial Registration Clinicaltrials.gov NCT02771977.
Published: 2023
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2. Modulation of the Association Between Age and Death by Risk Factor Burden in Critically Ill Patients With COVID-19

Author: Ashwin Sunderraj, BS, Chloe Cho, Xuan Cai, MS, Shruti Gupta, MD, MPH, Rupal Mehta, MD, MSCI, Tamara Isakova, MD, MMSc, David E. Leaf, MD, MMSc, Anand Srivastava, MD, MPH, STOP-COVID Investigators, Carl P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, Thuy-Duyen Nguyen, Shahzad Shaefi, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Shristi Upadhyay, Ishaan Vohra, Ajiboye Oyintayo, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Mehta, Aquino Williams, Samantha K. Brenner, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Miguel A. Hernán, Amy M. Zhou, Ethan C. Kim, Rebecca Lisk, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily A. Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Pattharawin Pattharanitima, Emily J. Gallagher, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Katherine J. Kelly, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Carmen Elena Cervantes, Samir C. Gautam, Mary C. Mallappallil, Jie Ouyang, Sabu John, Ernie Yap, Yohannes Melaku, Ibrahim Mohamed, Siddhartha Bajracharya, Isha Puri, Mariah Thaxton, Jyotsna Bhattacharya, John Wagner, Leon Boudourakis, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Dheeraj Reddy Sirganagari, Pramod K. Guru, Kianoush Kashani, Shahrzad Tehranian, Yan Zhou, Paul A. Bergl, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Michal L. Melamed, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Neelja Kumar, Michael Chang, Jyotsana Thakkar, Ritesh Raichoudhury, Akshay Athreya, Mohamed Farag, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Sobaata Chaudhry, Benjamin Wu, Frank Modersitzki, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner MB. Mohamed, Rupali S. Avasare, David Zonies, David E. Leaf, Shruti Gupta, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Ariel L. Mueller, Roberta E. Redfern, Nicholas S. Cairl, Gabriel Naimy, Abeer Abu-Saif, Danyell Hall, Laura Bickley, Chris Rowan, Farah Madhani-Lovely, Vivian S. Cruz, Kristen M. Hess, Alanna L. Jacobs, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Peter Cangialosi, Mark Liotta, Jared Radbel, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth S. Vatson, Shuchi Anand, Joseph E. Levitt, Pablo Garcia, Suzanne M. Boyle, Rui Song, Jingjing Zhang, Sang Hoon Woo, Xiaoying Deng, Goni Katz-Greenberg, Katharine Senter, Moh’d A. Sharshir, Vadym V. Rusnak, Muhammad Imran Ali, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, David J. Douin, Arash Rashidi, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Carl E. Schulze, Etienne Macedo, Harin Rhee, Kathleen D. Liu, Vasantha K. Jotwani, Jay L. Koyner, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Madona Elias, Yahya Ahmad, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Hayley B. Gershengorn, Bhavarth Shukla, Alessia Fornoni, Tanira Ferreira, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’ Hayer, Chelsea Meloche, Rafey Feroze, Rayan Kaakati, Danny Perry, Abbas Bitar, Elizabeth Anderson, Kishan J. Padalia, John P. Donnelly, Andrew J. Admon, Jennifer E. Flythe, Matthew J. Tugman, Emily H. Chang, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Sanjana Kapoor, Siddharth Verma, Huiwen Chen, Csaba P. Kovesdy, Miklos Z. Molnar, Ambreen Azhar, S. Susan Hedayati, Mridula V. Nadamuni, Shani Shastri, Duwayne L. Willett, Samuel A.P. Short, Amanda D. Renaghan, Kyle B. Enfield, Pavan K. Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Nitender Goyal, Anthony J. Faugno, Caroline M. Hsu, Asma Tariq, Leah Meyer, Ravi K. Kshirsagar, Daniel E. Weiner, Aju Jose, Marta Christov, Jennifer Griffiths, Sanjeev Gupta, Aromma Kapoor, Perry Wilson, Tanima Arora, and Ugochukwu Ugwuowo
Subjects: Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract: OBJECTIVES:. Older age is a key risk factor for adverse outcomes in critically ill patients with COVID-19. However, few studies have investigated whether preexisting comorbidities and acute physiologic ICU factors modify the association between age and death. DESIGN:. Multicenter cohort study. SETTING:. ICUs at 68 hospitals across the United States. PATIENTS:. A total of 5,037 critically ill adults with COVID-19 admitted to ICUs between March 1, 2020, and July 1, 2020. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. The primary exposure was age, modeled as a continuous variable. The primary outcome was 28-day inhospital mortality. Multivariable logistic regression tested the association between age and death. Effect modification by the number of risk factors was assessed through a multiplicative interaction term in the logistic regression model. Among the 5,037 patients included (mean age, 60.9 yr [± 14.7], 3,179 [63.1%] male), 1,786 (35.4%) died within 28 days. Age had a nonlinear association with 28-day mortality (p for nonlinearity
Published: 2022
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3. Performance of crisis standards of care guidelines in a cohort of critically ill COVID-19 patients in the United States

Author: Julia L. Jezmir, Maheetha Bharadwaj, Alexander Chaitoff, Bradford Diephuis, Conor P. Crowley, Sandeep P. Kishore, Eric Goralnick, Louis T. Merriam, Aimee Milliken, Chanu Rhee, Nicholas Sadovnikoff, Sejal B. Shah, Shruti Gupta, David E. Leaf, William B. Feldman, Edy Y. Kim, Carl P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, Thuy-Duyen Nguyen, Shahzad Shaefi, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Shristi Upadhyay, Ishaan Vohra, Ajiboye Oyintayo, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Mehta, Aquino Williams, Samantha K. Brenner, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Miguel A. Hernán, Amy M. Zhou, Ethan C. Kim, Rebecca Lisk, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily A. Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Pattharawin Pattharanitima, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Katherine J. Kelly, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Elena Cervantes, Samir Gautam, Mary C. Mallappallil, Jie Ouyang, Sabu John, Ernie Yap, Yohannes Melaku, Ibrahim Mohamed, Siddartha Bajracharya, Isha Puri, Mariah Thaxton, Jyotsna Bhattacharya, John Wagner, Leon Boudourakis, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Dheeraj Reddy Sirganagari, Pramod K. Guru, Kianoush Kashani, Yan Zhou, Paul A. Bergl, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Michal L. Melamed, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Neelja Kumar, Michael Chang, Ritesh Raichoudhury, Akshay Athreya, Mohamed Farag, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Sobaata Chaudhry, Benjamin Wu, Frank Modersitzki, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner MB. Mohamed, Rupali S. Avasare, David Zonies, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Ariel L. Mueller, Roberta Redfern, Nicholas S. Cairl, Gabriel Naimy, Abeer Abu-Saif, Danyell Hall, Laura Bickley, Chris Rowan, Farah Madhani-Lovely, Vivian S. Cruz, Kristen M. Hess, Alanna L. Jacobs, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Peter Cangialosi, Mark Liotta, Jared Radbel, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth S. Vatson, Shuchi Anand, Joseph E. Levitt, Suzanne M. Boyle, Rui Song, Jingjing Zhang, Sang Hoon Woo, Xiaoying Deng, Goni Katz-Greenberg, Katharine Senter, Moh’d A. Sharshir, Vadym V. Rusnak, Muhammad Imran Ali, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Arash Rashidi, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Etienne Macedo, Harin Rhee, Kathleen D. Liu, Vasantha K. Jotwani, Jay L. Koyner, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Madona Elias, Yahya Ahmad, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Hayley B. Gershengorn, Bhavarth Shukla, Alessia Fornoni, Tanira Ferreira, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’ Hayer, Chelsea Meloche, Rafey Feroze, Rayan Kaakati, Danny Perry, Abbas Bitar, Elizabeth Anderson, Kishan J. Padalia, John P. Donnelly, Andrew J. Admon, Jennifer E. Flythe, Matthew J. Tugman, Emily H. Chang, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Sanjana Kapoor, Siddharth Verma, Csaba P. Kovesdy, Miklos Z. Molnar, Ambreen Azhar, S. Susan Hedayati, Mridula V. Nadamuni, Shani Shastri, Duwayne L. Willett, Samuel A.P. Short, Amanda D. Renaghan, Kyle B. Enfield, Pavan K. Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Nitender Goyal, Anthony J. Faugno, Caroline M. Hsu, Asma Tariq, Leah Meyer, Ravi K. Kshirsagar, Daniel E. Weiner, Aju Jose, Marta Christov, Jennifer Griffiths, Sanjeev Gupta, Aromma Kapoor, Perry Wilson, Tanima Arora, and Ugochukwu Ugwuowo
Subjects: medical ethics, crisis standards of care, triage, critical care, intensive care, COVID-19, Medicine (General), R5-920
Abstract: Summary: Many US states published crisis standards of care (CSC) guidelines for allocating scarce critical care resources during the COVID-19 pandemic. However, the performance of these guidelines in maximizing their population benefit has not been well tested. In 2,272 adults with COVID-19 requiring mechanical ventilation drawn from the Study of the Treatment and Outcomes in Critically Ill Patients with COVID-19 (STOP-COVID) multicenter cohort, we test the following three approaches to CSC algorithms: Sequential Organ Failure Assessment (SOFA) scores grouped into ranges, SOFA score ranges plus comorbidities, and a hypothetical approach using raw SOFA scores not grouped into ranges. We find that area under receiver operating characteristic (AUROC) curves for all three algorithms demonstrate only modest discrimination for 28-day mortality. Adding comorbidity scoring modestly improves algorithm performance over SOFA scores alone. The algorithm incorporating comorbidities has modestly worse predictive performance for Black compared to white patients. CSC algorithms should be empirically examined to refine approaches to the allocation of scarce resources during pandemics and to avoid potential exacerbation of racial inequities.
Published: 2021
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4. Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019

Author: Matthew M. Churpek, MD, MPH, PhD, Shruti Gupta, MD, MPH, Alexandra B. Spicer, MS, Salim S. Hayek, MD, Anand Srivastava, MD, MPH, Lili Chan, MD, MSCR, Michal L. Melamed, MD, MHS, Samantha K. Brenner, MD, MPH, Jared Radbel, MD, Farah Madhani-Lovely, MD, Pavan K. Bhatraju, MD, MSc, Anip Bansal, MD, Adam Green, MD, MBA, Nitender Goyal, MD, Shahzad Shaefi, MD, MPH, Chirag R. Parikh, MD, PhD, Matthew W. Semler, MD, David E. Leaf, MD, MMSc, Carl P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, ThuyDuyen Nguyen, Shahzad Shaefi, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Oyintayo Ajiboye, Matthew Itteera, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Metha, Aquino Williams, Samantha K. Brenner, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Miguel A. Hernán, Amy M. Zhou, Ethan C. Kim, Rebecca Lisk, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Carmen Elena Cervantes, Samir C. Gautam, Crystal Chang, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Pramod K. Guru, Paul A. Bergl, Yan Zhou, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Michal L. Melamed, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Jyotsana Thakkar, Neelja Kumar, Michael J. Ross, Michael Chang, Ritesh Raichoudhury, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Daniel W. Ross, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner MB. Mohamed, Rupali S. Avasare, David Zonies, David E. Leaf, Shruti Gupta, Rebecca M. Baron, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa Gaviria, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Saif A. Muhsin, Ernest I. Mandel, Ariel L. Mueller, Nicholas S. Cairl, Farah Madhani-Lovely, Chris Rowan, Farah Madhai-Lovely, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Jared Radbel, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth Vatson, Shuchi Anand, Joseph E. Levitt, Pablo Garcia, Suzanne M. Boyle, Rui Song, Jingjing Zhang, Moh’d A. Sharshir, Vadym V. Rusnak, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Arash Rashidi, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Carl E. Schulze, Etienne Macedo, Harin Rhee, Kathleen D. Liu, Vasantha K. Jotwani, Jay L. Koyner, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Hayley B. Gershengorn, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’ Hayer, Chelsea Meloche, Rafey Feroze, Kishan J. Padalia, Jeff Leya, John P. Donnelly, Andrew J. Admon, Jennifer E. Flythe, Matthew J. Tugman, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Csaba P. Kovesdy, Miklos Z. Molnar, S. Susan Hedayati, Mridula V. Nadamuni, Sadaf S. Khan, Duwayne L. Willett, Samuel A.P. Short, Amanda D. Renaghan, Pavan Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Nitender Goyal, Anthony J. Faugno, Caroline M. Hsu, Asma Tariq, Leah Meyer, Marta Christov, Francis P. Wilson, Tanima Arora, and Ugochukwu Ugwuowo
Subjects: Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract: OBJECTIVES:. Critically ill patients with coronavirus disease 2019 have variable mortality. Risk scores could improve care and be used for prognostic enrichment in trials. We aimed to compare machine learning algorithms and develop a simple tool for predicting 28-day mortality in ICU patients with coronavirus disease 2019. DESIGN:. This was an observational study of adult patients with coronavirus disease 2019. The primary outcome was 28-day inhospital mortality. Machine learning models and a simple tool were derived using variables from the first 48 hours of ICU admission and validated externally in independent sites and temporally with more recent admissions. Models were compared with a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 using the area under the receiver operating characteristic curve and calibration. SETTING:. Sixty-eight U.S. ICUs. PATIENTS:. Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. The study included 5,075 patients, 1,846 (36.4%) of whom died by day 28. eXtreme Gradient Boosting had the highest area under the receiver operating characteristic curve in external validation (0.81) and was well-calibrated, while k-nearest neighbors were the lowest performing machine learning algorithm (area under the receiver operating characteristic curve 0.69). Findings were similar with temporal validation. The simple tool, which was created using the most important features from the eXtreme Gradient Boosting model, had a significantly higher area under the receiver operating characteristic curve in external validation (0.78) than the Sequential Organ Failure Assessment score (0.69), National Early Warning Score (0.60), and CURB-65 (0.65; p < 0.05 for all comparisons). Age, number of ICU beds, creatinine, lactate, arterial pH, and Pao2/Fio2 ratio were the most important predictors in the eXtreme Gradient Boosting model. CONCLUSIONS:. eXtreme Gradient Boosting had the highest discrimination overall, and our simple tool had higher discrimination than a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 on external validation. These models could be used to improve triage decisions and clinical trial enrichment.
Published: 2021
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5. A simple real-time model for predicting acute kidney injury in hospitalized patients in the US: A descriptive modeling study.

Author: Michael Simonov, Ugochukwu Ugwuowo, Erica Moreira, Yu Yamamoto, Aditya Biswas, Melissa Martin, Jeffrey Testani, and F Perry Wilson
Subjects: Medicine
Abstract: BackgroundAcute kidney injury (AKI) is an adverse event that carries significant morbidity. Given that interventions after AKI occurrence have poor performance, there is substantial interest in prediction of AKI prior to its diagnosis. However, integration of real-time prognostic modeling into the electronic health record (EHR) has been challenging, as complex models increase the risk of error and complicate deployment. Our goal in this study was to create an implementable predictive model to accurately predict AKI in hospitalized patients and could be easily integrated within an existing EHR system.Methods and findingsWe performed a retrospective analysis looking at data of 169,859 hospitalized adults admitted to one of three study hospitals in the United States (in New Haven and Bridgeport, Connecticut) from December 2012 to February 2016. Demographics, medical comorbidities, hospital procedures, medications, and laboratory data were used to develop a model to predict AKI within 24 hours of a given observation. Outcomes of AKI severity, requirement for renal replacement therapy, and mortality were also measured and predicted. Models were trained using discrete-time logistic regression in a subset of Hospital 1, internally validated in the remainder of Hospital 1, and externally validated in Hospital 2 and Hospital 3. Model performance was assessed via the area under the receiver-operator characteristic (ROC) curve (AUC). The training set cohort contained 60,701 patients, and the internal validation set contained 30,599 patients. External validation data sets contained 43,534 and 35,025 patients. Patients in the overall cohort were generally older (median age ranging from 61 to 68 across hospitals); 44%-49% were male, 16%-20% were black, and 23%-29% were admitted to surgical wards. In the training set and external validation set, 19.1% and 18.9% of patients, respectively, developed AKI. The full model, including all covariates, had good ability to predict imminent AKI for the validation set, sustained AKI, dialysis, and death with AUCs of 0.74 (95% CI 0.73-0.74), 0.77 (95% CI 0.76-0.78), 0.79 (95% CI 0.73-0.85), and 0.69 (95% CI 0.67-0.72), respectively. A simple model using only readily available, time-updated laboratory values had very similar predictive performance to the complete model. The main limitation of this study is that it is observational in nature; thus, we are unable to conclude a causal relationship between covariates and AKI and do not provide an optimal treatment strategy for those predicted to develop AKI.ConclusionsIn this study, we observed that a simple model using readily available laboratory data could be developed to predict imminent AKI with good discrimination. This model may lend itself well to integration into the EHR without sacrificing the performance seen in more complex models.
Published: 2019
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6. 3002 Effect of Long-Term NSAID Use on Opioid Abuse and Health Outcomes among Breast Cancer Patients

Author: Nnaemeka E Onyeakusi, Semiu Gbadamosi, Fahad Mukhtar, Chinelo Orji, Ugochukwu Ugwuowo, Onyenikewe Igbeta, and Adeyinka Adejumo
Subjects: Medicine
Abstract: OBJECTIVES/SPECIFIC AIMS: Cancer related pain presents a significant risk for opioid abuse among cancer survivors and contributes to the current opioid crisis. Nearly 90% of breast cancer patients have been reported to have cancer-related pain requiring treatment. Opioids, in combination with NSAIDs, have been widely used for pain management in this population despite the risk of abuse. Long-term NSAID use due to their antineoplastic and neuroprotective effects may offer additional protective effects against opioid abuse. Here, we assess the relationship between NSAID use and opioid abuse among breast cancer patients. METHODS/STUDY POPULATION: Using ICD-9-CM codes, we identified and selected women aged >18 years with breast cancer from the National Inpatient Sample (NIS). Our primary predictor was a history of long-term NSAID use. Opioid abuse was the primary outcome of interest. Secondary outcomes were inpatient mortality and length of stay. Multivariable regression models were employed in assessing the association between predictors and outcomes while adjusting for relevant covariates. RESULTS/ANTICIPATED RESULTS: Among 170,644 women with breast cancer, 7,838 (4.6%) reported a history of long-term NSAID use. Patients with a history of long-term NSAID use had lower odds of opioid abuse (aOR 0.53; 95% CI [0.32-0.88]) and in-hospital mortality (aOR 0.52; 95% CI [0.45-0.60]) and were likely to have shorter hospital stay (7.12 vs. 8.11 days) compared to women with no history of long-term NSAID use. DISCUSSION/SIGNIFICANCE OF IMPACT: Long-term NSAID use may offer a protective effect against opioid abuse and improve in-hospital outcomes translating to better quality of life and healthcare utilization indices among breast cancer patients.
Published: 2019
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7. Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19

Author: Steven Menez, Steven G. Coca, Dennis G. Moledina, Yumeng Wen, Lili Chan, Heather Thiessen-Philbrook, Wassim Obeid, Brian T. Garibaldi, Evren U. Azeloglu, Ugochukwu Ugwuowo, C. John Sperati, Lois J. Arend, Avi Z. Rosenberg, Madhurima Kaushal, Sanjay Jain, F. Perry Wilson, Chirag R. Parikh, Jie Deng, Mo Atta, Serena M. Bagnasco, Albert Ko, Akiko Iwasaki, Shelli Farhadian, Allison Nelson, Arnau Casanovas-Massana, Elizabeth B. White, Wade Schulz, Andreas Coppi, Patrick Young, Angela Nunez, Denise Shepard, Irene Matos, Yvette Strong, Kelly Anastasio, Kristina Brower, Maxine Kuang, Michael Chiorazzi, Santos Bermejo, Pavithra Vijayakumar, Bertie Geng, John Fournier, Maksym Minasyan, M. Catherine Muenker, Adam J. Moore, and Girish Nadkarni
Subjects: Nephrology
Published: 2023
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8. Integrated single-cell sequencing and histopathological analyses reveal diverse injury and repair responses in a participant with acute kidney injury: a clinical-molecular-pathologic correlation

Author: Rajasree Menon, Andrew S. Bomback, Blue B. Lake, Christy Stutzke, Stephanie M. Grewenow, Steven Menez, Vivette D. D’Agati, Sanjay Jain, Richard Knight, Stewart H. Lecker, Isaac Stillman, Steve Bogen, Laurence H. Beck, Sushrut Waikar, Gearoid M. McMahon, Astrid Weins, Mia R. Colona, Nir Hacohen, Paul J. Hoover, Mark Aulisio, William S. Bush, Dana C. Crawford, John O'toole, Emilio Poggio, John Sedor, Leslie Cooperman, Stacey Jolly, Leal Herlitz, Jane Nguyen, Agustin Gonzalez-Vicente, Ellen Palmer, Dianna Sendrey, Carissa Vinovskis, Petter M. Bjornstad, Paul Appelbaum, Jonathan M. Barasch, Vivette D. D'Agati, Krzysztof Kiryluk, Karla Mehl, Pietro A. Canetta, Ning Shang, Olivia Balderes, Satoru Kudose, Shweta Bansal, Theodore Alexandrov, Helmut Rennke, Tarek M. El-Achkar, Yinghua Cheng, Pierre C. Dagher, Michael T. Eadon, Kenneth W. Dunn, Katherine J. Kelly, Timothy A. Sutton, Daria Barwinska, Michael J. Ferkowicz, Seth Winfree, Sharon Bledsoe, Marcelino Rivera, James C. Williams, Ricardo Melo Ferreira, Chirag R. Parikh, Celia P. Corona-Villalobos, Avi Rosenberg, Sylvia E. Rosas, Neil Roy, Mark Williams, Evren U. Azeloglu, Cijang He, Ravi Iyengar, Jens Hansen, Yuguang Xiong, Brad Rovin, Samir Parikh, John P. Shapiro, Christopher R. Anderton, Ljiljana Pasa-Tolic, Dusan Velickovic, Jessica Lukowski, George Oliver, Joseph Ardayfio, Jack Bebiak, Keith Brown, Catherine E. Campbell, John Saul, Anna Shpigel, Robert Koewler, Taneisha Campbell, Lynda Hayashi, Nichole Jefferson, Glenda V. Roberts, Roy Pinkeney, Olga Troyanskaya, Rachel Sealfon, Katherine R. Tuttle, Yury Goltsev, Kun Zhang, Zoltan G. Laszik, Garry Nolan, Patrick Boada, Minnie Sarwal, Tara Sigdel, Paul J. Lee, Rita R. Alloway, E. Steve Woodle, Heather Ascani, Ulysses G.J. Balis, Jeffrey B. Hodgin, Matthias Kretzler, Chrysta Lienczewski, Laura H. Mariani, Becky Steck, Yougqun He, Edgar Otto, Jennifer Schaub, Victoria M. Blanc, Sean Eddy, Ninive C. Conser, Jinghui Luo, Paul M. Palevsky, Matthew Rosengart, John A. Kellum, Daniel E. Hall, Parmjeet Randhawa, Mitchell Tublin, Raghavan Murugan, Michele M. Elder, James Winters, Charles E. Alpers, Kristina N. Blank, Jonas Carson, Ian H. De Boer, Ashveena L. Dighe, Jonathan Himmelfarb, Sean D. Mooney, Stuart Shankland, Kayleen Williams, Christopher Park, Frederick Dowd, Robyn L. McClelland, Stephen Daniel, Andrew N. Hoofnagle, Adam Wilcox, Kumar Sharma, Manjeri Venkatachalam, Guanshi Zhang, Annapurna Pamreddy, Hongping Ye, Richard Montellano, Robert D. Toto, Miguel Vazquez, Simon C. Lee, R. Tyler Miller, Orson W. Moe, Jose Torrealba, Nancy Wang, Asra Kermani, Kamalanathan Sambandam, Harold Park, S. Susan Hedayati, Christopher Y. Lu, Anitha Vijayan, Joseph P. Gaut, Dennis Moledina, Francis P. Wilson, Ugochukwu Ugwuowo, and Tanima Arora
Subjects: Pathology, Clinical, Nephrology, Humans, Acute Kidney Injury, Kidney, Article
Published: 2022
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9. Cadherin-11, Sparc-related modular calcium binding protein-2, and Pigment epithelium-derived factor are promising non-invasive biomarkers of kidney fibrosis

Author: Mark E. Williams, Katherine R. Tuttle, Jing Liu, Jinghui Luo, Yougqun He, Laura Pyle, Blue B. Lake, Brad H. Rovin, Lynda Hayashi, Yuguang Xiong, Dennis G. Moledina, Andreas Bueckle, Steven Menez, Glenda V. Roberts, Anand Srivastava, Paul Appelbaum, Heather Ascani, Catherine Campbell, Stephanie M. Grewenow, Mark Aulisio, Jennifer Sun, Christopher R. Anderton, Jamie L. Marshall, Sharon Bledso, John P. Shapiro, Theodore Alexandrov, Richard M. Caprioli, Michele Elder, Leslie Cooperman, Shweta Bansal, Lakeshia Bush, Krzysztof Kiryluk, Mitchell Tublin, Olga G. Troyanskaya, Emilio D. Poggio, Kristina N. Blank, Andrew Janowczyk, Paul Hoover, Sabine M. Diettman, R. Tyler Miller, Katy Borner, Leonidas G. Alexopoulos, James Winters, Anant Madabhushi, Haojia Wu, Chirag R. Parikh, Yumeng Wen, Avi Z. Rosenberg, Agustin Gonzalez-Vicente, Leal Herlitz, Keith Brown, Matthew Gilliam, Joseph P. Gaut, Vidya S. Viswanathan, Karla Mehl, Stewart H. Lecker, Pierre C. Dagher, Dana C. Crawford, Camille Johansen, Anna Greka, Tiffany Shi, Ari Pollack, Renee Frey, Kavya Sharman, Isaac E. Stillman, Stuart J. Shankland, Ricardo Melo Ferreira, Jack Bebiak, Jing Su, Matthias Kretzler, Ellen Palmer, Yury Goltsev, Aaron K. Wong, Matthew R. Rosengart, Taneisha Campbell, Tina Vita, Helmut G. Rennke, Nir Hacohen, Satoru Kudose, Christine Limonte, Kun Zhang, Robyn L. McClelland, Ulysses J. Balis, Katherine J. Kelly, Simon Lee, Ninive C. Conser, Adele Rike, Frederick Dowd, Timothy A. Sutton, Steve Bogen, Petter M. Bjornstad, Zoltan Laszik, Dianbo Zhang, Benjamin D. Humphreys, Pinaki Sarder, Jeffrey M. Spraggins, Ravi Iyengar, Marcelino Rivera, Roy Pinkeney, James C. Williams, Tarek M. El-Achkar, Laura H. Mariani, Richard J. Knight, Manjeri A. Venkatachalam, Pietro A. Canetta, Lloyd G. Cantley, Kayleen Williams, Catherine P. Jayapandian, Edgar A. Otto, Jessica Lukowski, Kassandra Spates-Harden, Ashish Verma, John Saul, Tariq Mukatash, Mia R. Colona, Shana Maikhor, Laurence H. Beck, Titlayo Ilori, Charles E. Alpers, Ellen M. Quardokus, Mujeeb Basit, Dušan Veličković, Raf Van de Plas, Jonathan Himmelfarb, Michael T. Eadon, Chrysta Lienczewski, Christopher Y. Lu, Yijiang M. Chen, Kasra Rezaei, Richard Montellano, Pottumarthi V. Prasad, Francis P. Wilson, Christy Stutzke, Jane Nguyen, Kamalanathan K. Sambandam, Miguel A. Vazquez, Vishal S. Vaidya, Vivette D. D'Agati, Patrick Boada, Adam Wilcox, Astrid Weins, Jennifer A. Schaub, Harold Park, Kumar Sharma, M. Todd Valerius, Stephen Daniel, Sean Eddy, Bruce W. Herr, Kenneth W. Dunn, Jamie Snyder, E. Steve Woodle, Dianna Sendrey, Ljiljana Paša-Tolić, Raghavan Murugan, Brandon Ginley, Bryan Kestenbaum, Celia P. Corona-Villalobos, Olivia Balderes, Sushrut Waikar, Carissa Vinovskis, Brooke Berry, Parmjeet Randhawa, Seth Winfree, Jose R. Torrealba, Ning Shang, Rachel Sealfon, Michael J. Ferkowicz, William S. Bush, Jonas Carson, Robert Koewler, Guanshi Zhang, Robert D. Toto, Ian H. de Boer, Gearoid M. McMahon, Andrew N. Hoofnagle, Vijaykumar R. Kakade, Brendon Lutnick, Melissa M. Shaw, Rita R. Alloway, Rajasree Menon, Afolarin Amodu, Jeanine Basta, Paul J. Lee, Ingrid Onul, Sylvia E. Rosas, Cijang (John) He, Andrew S. Bomback, Yinghua Cheng, Jeffrey B. Hodgin, Samir M. Parikh, Garry Nolan, John A. Kellum, Anil Pillai, Annapurna Pamreddy, Orson W. Moe, Jiten Patel, Jonathan J. Taliercio, S. Susan Hedayati, Anitha Vijayan, Tanima Arora, Evren U. Azeloglu, Paul M. Palevsky, Nathan Heath Patterson, Asra Kermani, Becky Steck, Kavya Anjani, Ashley Berglund, Yashvardhan Jain, Stacey E. Jolly, John R. Sedor, George (Holt) Oliver, Natasha Wen, Nancy Wang, Ruikang Wang, Joseph Ardayfio, Michael Rauchman, Ashley R. Burg, Victoria Blanc, Minnie M. Sarwal, Daniel Hall, Sethu M. Madhavan, Sean D. Mooney, Sushrut S. Waikar, Daria Barwinska, Christopher Y. Park, Tara K. Sigdel, Ugochukwu Ugwuowo, John F. O'Toole, Ragnar Palsson, Insa M. Schmidt, Joel M. Henderson, Hongping Ye, Jens Hansen, Jonathan Barasch, Neil Roy, Nicholas Lucarelli, Anna Shpigel, Ashveena Dighe, Elizabeth Record, Sanjay Jain, and Nichole Jefferson
Subjects: 0301 basic medicine, Pathology, medicine.medical_specialty, Urinary system, 030232 urology & nephrology, Kidney, Article, 03 medical and health sciences, 0302 clinical medicine, PEDF, Fibrosis, Biopsy, Humans, Medicine, Osteonectin, Nerve Growth Factors, Prospective Studies, Renal Insufficiency, Chronic, Eye Proteins, Serpins, medicine.diagnostic_test, urogenital system, business.industry, Calcium-Binding Proteins, Cadherins, medicine.disease, 030104 developmental biology, medicine.anatomical_structure, Nephrology, Cohort, Disease Progression, Biomarker (medicine), business, Biomarkers, Kidney disease
Abstract: Kidney fibrosis constitutes the shared final pathway of nearly all chronic nephropathies, but biomarkers for the non-invasive assessment of kidney fibrosis are currently not available. To address this, we characterize five candidate biomarkers of kidney fibrosis: Cadherin-11 (CDH11), Sparc-related modular calcium binding protein-2 (SMOC2), Pigment epithelium-derived factor (PEDF), Matrix-Gla protein, and Thrombospondin-2. Gene expression profiles in single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) datasets from rodent models of fibrosis and human chronic kidney disease (CKD) were explored, and Luminex-based assays for each biomarker were developed. Plasma and urine biomarker levels were measured using independent prospective cohorts of CKD: the Boston Kidney Biopsy Cohort, a cohort of individuals with biopsy-confirmed semiquantitative assessment of kidney fibrosis, and the Seattle Kidney Study, a cohort of patients with common forms of CKD. Ordinal logistic regression and Cox proportional hazards regression models were used to test associations of biomarkers with interstitial fibrosis and tubular atrophy and progression to end-stage kidney disease and death, respectively. Sc/snRNA-seq data confirmed cell-specific expression of biomarker genes in fibroblasts. After multivariable adjustment, higher levels of plasma CDH11, SMOC2, and PEDF and urinary CDH11 and PEDF were significantly associated with increasing severity of interstitial fibrosis and tubular atrophy in the Boston Kidney Biopsy Cohort. In both cohorts, higher levels of plasma and urinary SMOC2 and urinary CDH11 were independently associated with progression to end-stage kidney disease. Higher levels of urinary PEDF associated with end-stage kidney disease in the Seattle Kidney Study, with a similar signal in the Boston Kidney Biopsy Cohort, although the latter narrowly missed statistical significance. Thus, we identified CDH11, SMOC2, and PEDF as promising non-invasive biomarkers of kidney fibrosis.
Published: 2021
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10. A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children

Author: Sherry G. Mansour, Dennis G. Moledina, Melissa Martin, Ibrahim Sandokji, Yu Yamamoto, Michael Simonov, Tanima Arora, F. Perry Wilson, Jason H. Greenberg, Ishan Saran, Jeffrey M. Testani, Aditya Biswas, and Ugochukwu Ugwuowo
Subjects: Male, medicine.medical_specialty, Adolescent, 030232 urology & nephrology, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Clinical Research, Electronic Health Records, Humans, Medicine, 030212 general & internal medicine, Internal validation, Stage (cooking), Child, Retrospective Studies, Receiver operating characteristic, business.industry, Medical record, Infant, General Medicine, Acute Kidney Injury, medicine.disease, Confidence interval, Nephrology, Child, Preschool, Cohort, Emergency medicine, Population study, Female, business, Child, Hospitalized, Kidney disease
Abstract: Background Timely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges. Methods We retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay. Results Among 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points. Conclusions Using various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.
Published: 2020
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11. Association of Surge Conditions with Mortality Among Critically Ill Patients with COVID-19

Author: Adam B. Keene, Andrew J. Admon, Samantha K. Brenner, Shruti Gupta, Deepa Lazarous, David E. Leaf, Hayley B. Gershengorn, Carl P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, Thuy-Duyen Nguyen, Shahzad Shaefi, Brian P. O’Gara, Megan L. Krajewski, Sean M. Baskin, Sidharth Shankar, Juan D. Valencia, Ameeka Pannu, Margaret M. Hayes, E. Wilson Grandin, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Shristi Upadhyay, Ishaan Vohra, Ajiboye Oyintayo, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Mehta, Aquino Williams, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Miguel A. Hernán, Rebecca Lisk, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily A. Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Pattharawin Pattharanitima, Emily J. Gallagher, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Katherine J. Kelly, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Carmen Elena Cervantes, Samir C. Gautam, Mary C. Mallappallil, Jie Ouyang, Sabu John, Ernie Yap, Yohannes Melaku, Ibrahim Mohamed, Siddhartha Bajracharya, Isha Puri, Mariah Thaxton, Jyotsna Bhattacharya, John Wagner, Leon Boudourakis, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Dheeraj Reddy Sirganagari, Pramod K. Guru, Kianoush Kashani, Shahrzad Tehranian, Yan Zhou, Paul A. Bergl, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Michal L. Melamed, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Neelja Kumar, Michael Chang, Jyotsana Thakkar, Ritesh Raichoudhury, Akshay Athreya, Mohamed Farag, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Sobaata Chaudhry, Benjamin Wu, Frank Modersitzki, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner MB. Mohamed, Rupali S. Avasare, David Zonies, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Ariel L. Mueller, Roberta E. Redfern, Nicholas S. Cairl, Gabriel Naimy, Abeer Abu-Saif, Danyell Hall, Laura Bickley, Chris Rowan, Farah Madhani-Lovely, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Peter Cangialosi, Mark Liotta, Jared Radbel, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth S. Vatson, Shuchi Anand, Joseph E. Levitt, Pablo Garcia, Suzanne M. Boyle, Rui Song, Ali Arif, Jingjing Zhang, Sang Hoon Woo, Xiaoying Deng, Goni Katz-Greenberg, Katharine Senter, Moh’d A. Sharshir, Vadym V. Rusnak, Muhammad Imran Ali, Terri Peters, Kathy Hughes, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Arash Rashidi, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Carl E. Schulze, Etienne Macedo, Harin Rhee, Kathleen D. Liu, Vasantha K. Jotwani, Jay L. Koyner, Alissa Kunczt, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Madona Elias, Yahya Ahmad, Rajany Dy, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Alessia Fornoni, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O' Hayer, Chelsea Meloche, Rafey Feroze, Kishan J. Padalia, Abbas Bitar, Jeff Leya, John P. Donnelly, Jennifer E. Flythe, Matthew J. Tugman, Emily H. Chang, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Sanjana Kapoor, Siddharth Verma, Huiwen Chen, Csaba P. Kovesdy, Miklos Z. Molnar, Ambreen Azhar, S. Susan Hedayati, Mridula V. Nadamuni, Shani Shastri, Duwayne L. Willett, Samuel A.P. Short, Amanda D. Renaghan, Kyle B. Enfield, Pavan K. Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Nitender Goyal, Anthony J. Faugno, Caroline M. Hsu, Asma Tariq, Leah Meyer, Ravi K. Kshirsagar, Aju Jose, Daniel E. Weiner, Marta Christov, Savneek Chugh, Jennifer Griffiths, Sanjeev Gupta, Aromma Kapoor, Perry Wilson, Tanima Arora, and Ugochukwu Ugwuowo
Subjects: Adult, Cohort Studies, Male, Intensive Care Units, SARS-CoV-2, Critical Illness, COVID-19, Humans, Female, Hospital Mortality, Middle Aged, Critical Care and Intensive Care Medicine
Abstract: Objective To determine whether surge conditions were associated with increased mortality. Design Multicenter cohort study. Setting U.S. ICUs participating in STOP-COVID. Patients Consecutive adults with COVID-19 admitted to participating ICUs between March 4 and July 1, 2020. Interventions None Measurements and Main Results The main outcome was 28-day in-hospital mortality. To assess the association between admission to an ICU during a surge period and mortality, we used two different strategies: (1) an inverse probability weighted difference-in-differences model limited to appropriately matched surge and non-surge patients and (2) a meta-regression of 50 multivariable difference-in-differences models (each based on sets of randomly matched surge- and non-surge hospitals). In the first analysis, we considered a single surge period for the cohort (March 23 – May 6). In the second, each surge hospital had its own surge period (which was compared to the same time periods in matched non-surge hospitals). Our cohort consisted of 4342 ICU patients (average age 60.8 [sd 14.8], 63.5% men) in 53 U.S. hospitals. Of these, 13 hospitals encountered surge conditions. In analysis 1, the increase in mortality seen during surge was not statistically significant (odds ratio [95% CI]: 1.30 [0.47-3.58], p = .6). In analysis 2, surge was associated with an increased odds of death (odds ratio 1.39 [95% CI, 1.34-1.43], p Conclusions Admission to an ICU with COVID-19 in a hospital that is experiencing surge conditions may be associated with an increased odds of death. Given the high incidence of COVID-19, such increases would translate into substantial excess mortality.
Published: 2021

12. A Participant-Centered Approach to Understanding Risks and Benefits of Participation in Research Informed by the Kidney Precision Medicine Project

Author: Catherine R. Butler, Paul S. Appelbaum, Heather Ascani, Mark Aulisio, Catherine E. Campbell, Ian H. de Boer, Ashveena L. Dighe, Daniel E. Hall, Jonathan Himmelfarb, Richard Knight, Karla Mehl, Raghavan Murugan, Sylvia E. Rosas, John R. Sedor, John F. O’Toole, Katherine R. Tuttle, Sushrut S. Waikar, Michael Freeman, Theodore Alexandrov, Charles E. Alpers, Christopher R. Anderton, Joseph Ardayfio, Tanima Arora, Tarek M. El-Achkar, Evren U. Azeloglu, Olivia Balderes, Ulysses G.J. Balis, Shweta Bansal, Jonathan M. Barasch, Daria Barwinska, Jack Bebiak, Victoria M. Blanc, Kristina N. Blank, Andrew S. Bomback, Keith D. Brown, William S. Bush, Taneisha Campbell, Pietro A. Canetta, Jonas Carson, Leslie Cooperman, Dana C. Crawford, Vivette D. D’Agati, Pierre C. Dagher, Stephen Daniel, Frederick Dowd, Kenneth W. Dunn, Michael T. Eadon, Sean Eddy, Michele M. Elder, Michael J. Ferkowicz, Joe P. Gaut, Yury Goltsev, Agustin Gonzalez-Vicente, Nir Hacohen, Jens Hansen, Lynda Hayashi, Oliver He, Cijang He, S. Susan Hedayati, Leal Herlitz, Jeffrey B. Hodgin, Andrew N. Hoofnagle, Paul J. Hoover, Ravi Iyengar, Sanjay Jain, Nichole Jefferson, Stacey Jolly, John A. Kellum, Katherine J. Kelly, Asra Kermani, Krzysztof Kiryluk, Robert Koewler, Matthias Kretzler, Blue B. Lake, Zoltan G. Laszik, Stewart H. Lecker, Simon C. Lee, Chrysta Lienczewski, Christopher Y. Lu, Laura H. Mariani, Robyn L. McClelland, Gearoid M. McMahon, Steven Menez, Rajasree Menon, Tyler Miller, Orson W. Moe, Dennis Moledina, Sean D. Mooney, Jane Nguyen, Garry Nolan, George Oliver, Edgar Otto, Paul M. Palevsky, Ellen Palmer, Annapurna Pamreddy, Chirag R. Parikh, Samir Parikh, Christopher Park, Harold Park, Ljiljana Pasa-Tolic, Roy Pinkeney, Emilio Poggio, Parmjeet Randhawa, Helmut Rennke, Glenda V. Roberts, Avi Rosenberg, Matthew Rosengart, Brad Rovin, Neil Roy, Kamalanathan Sambandam, Minnie Sarwal, John Saul, Jennifer Schaub, Rachel Sealfon, Ning Shang, Stuart Shankland, Kumar Sharma, Anna Shpigel, Tara Sigdel, Becky Steck, Isaac Stillman, Edith Christine Stutzke, Timothy A. Sutton, Jose Torrealba, Robert D. Toto, Olga Troyanskaya, Mitchell Tublin, Ugochukwu Ugwuowo, Miguel Vazquez, Dusan Velickovic, Manjeri Venkatachalam, Anitha Vijayan, Celia P. Corona-Villalobos, Nancy Wang, Astrid Weins, Adam Wilcox, Kayleen Williams, Mark Williams, Francis P. Wilson, Seth Winfree, Yuguang Xiong, Kun Zhang, and Guanshi Zhang
Subjects: Informed Consent, Nephrology, Humans, Precision Medicine, Kidney, Risk Assessment, Article, Research Personnel
Abstract: An understanding of the ethical underpinnings of human subjects research that involves some risk to participants without anticipated direct clinical benefit-such as the kidney biopsy procedure as part of the Kidney Precision Medicine Project (KPMP)-requires a critical examination of the risks as well as the diverse set of countervailing potential benefits to participants. This kind of deliberation has been foundational to the development and conduct of the KPMP. Herein, we use illustrative features of this research paradigm to develop a more comprehensive conceptualization of the types of benefits that may be important to research participants, including respecting pluralistic values, supporting the opportunity to act altruistically, and enhancing benefits to a participant's community. This approach may serve as a model to help researchers, ethicists, and regulators to identify opportunities to better respect and support participants in future research that entails some risk to these participants as well as to improve the quality of research for people with kidney disease.
Published: 2021

13. Kidney Recovery and Death in Critically Ill Patients With COVID-19-Associated Acute Kidney Injury Treated With Dialysis: The STOP-COVID Cohort Study

Author: Caroline M. Hsu, Shruti Gupta, Hocine Tighiouart, Nitender Goyal, Anthony J. Faugno, Asma Tariq, Ritesh Raichoudhury, Jill H. Sharma, Leah Meyer, Ravi K. Kshirsagar, Aju Jose, David E. Leaf, Daniel E. Weiner, Hsu Gupta, Goyal Faugno, Tariq Raichoudhury, Sharma Meyer, Kshirsagar Leaf, Carl P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, Thuy-Duyen Nguyen, Shahzad Shaefi, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Kenneth A. Bauer, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Shristi Upadhyay, Ishaan Vohra, Ajiboye Oyintayo, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Mehta, Aquino WilliamsSamantha K. Brenner, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Miguel A. Hernán, Amy M. Zhou, Ethan C. Kim, Rebecca Lisk, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily A. Leven, Jing G. Wang, Gohar Mosoyan, Pattharawin Pattharanitima, Emily J. Gallagher, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Katherine J. Kelly, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Carmen Elena Cervantes, Samir C. Gautam, Mary C. Mallappallil, Jie Ouyang, Sabu John, Ernie Yap, Yohannes Melaku, Ibrahim Mohamed, Siddhartha Bajracharya, Isha Puri, Mariah Thaxton, Jyotsna Bhattacharya, John Wagner, Leon Boudourakis, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Dheeraj Reddy Sirganagari, Pramod K. Guru, Kianoush Kashani, Shahrzad Tehranian, Yan Zhou, Paul A. Bergl, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Michal L. Melamed, Tanya S. Johns. Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Jyotsana Thakkar, Neelja Kumar, Michael J. Ross, Michael Chang, Akshay Athreya, Mohamed Farag, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner M.B. Mohamed, Rupali S. Avasare, David Zonies, Hanny Al-Samkari, Rebecca Karp Leaf, Rachel Rosovsky, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa, Tanveer Shaukat, Omer Kamal, Wei Wang, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Ariel L. Mueller, Roberta E. Redfern, Nicholas S. Cairl, Gabriel Naimy, Abeer Abu-Saif, Danyell Hall, Laura Bickley, Chris Rowan, Farah Madhani-Lovely, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Peter Cangialosi, Mark Liotta, Jared Radbel, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth S. Vatson, George Karp, Shuchi Anand, Joseph E. Levitt, Pablo Garcia, Suzanne M. Boyle, Rui Song, Jingjing Zhang, Sang Hoon Woo, Xiaoying Deng, Goni Katz-Greenberg, Moh’d A. Sharshir, Vadym V. Rusnak, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Arash Rashidi, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Carl E. Schulze, Etienne Macedo, Harin RheeKa, null thleen D. Liu, Vasantha K. Jotwani, Jay L. Koyner, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Madona Elias, Yahya Ahmad, Rajany Dy, Alfredo Iardino, Elizabeth H. Au, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Hayley B. Gershengorn, Alessia Fornoni, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’Hayer, Chelsea Meloche, Rafey Feroze, Kishan J. Padalia, Jeff Leya, John P. Donnelly, Andrew J. Admon, Jennifer E. Flythe, Matthew J. Tugman, Emily H. Chang, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Sanjana Kapoor, Siddharth Verma, Huiwen Chen, Csaba P. Kovesdy, Miklos Z. Molnar, Ambreen Azhar, S. Susan Hedayati, Mridula V. Nadamuni, Shani Shastri, Duwayne L. Willett, Samuel A.P. Short, Amanda D. Renaghan, Kyle B. Enfield, Pavan K. Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Marta Christov, Jennifer Griffiths, Sanjeev Gupta, Aromma Kapoor, Savneek Chugh, Perry Wilson, Tanima Arora, and Ugochukwu Ugwuowo
Subjects: Adult, urogenital system, SARS-CoV-2, Critical Illness, Aftercare, COVID-19, Original Investigations, Acute Kidney Injury, urologic and male genital diseases, Kidney, Patient Discharge, Cohort Studies, critical care, Intensive Care Units, Nephrology, Renal Dialysis, Risk Factors, urine output, Humans, dialysis, chronic kidney disease, Retrospective Studies
Abstract: Rationale & Objective Acute kidney injury treated with kidney replacement therapy (AKI-KRT) occurs frequently in critically ill patients with COVID-19. We examined the clinical factors that determine kidney recovery in this population. Study Design Multicenter cohort study. Setting & Participants 4221 adults with COVID-19 not receiving kidney replacement therapy who were admitted to intensive care units at 68 US hospitals with COVID-19 from March 1 to June 22, 2020 (the “ICU cohort”). Among these, 876 developed AKI-KRT after admission to the ICU (the “AKI-KRT subcohort”). Exposure(s) The ICU cohort was analyzed using AKI severity as the exposure. For the AKI-KRT subcohort, exposures included demographics, comorbidities, initial mode of KRT, and markers of illness severity at the time of dialysis initiation. Outcome(s) The outcome for the ICU cohort was estimated glomerular filtration rate (GFR) at hospital discharge. A three-level outcome including death, kidney nonrecovery, and kidney recovery at discharge, was analyzed for the AKI-KRT subcohort. Analytical approach The ICU cohort was characterized using descriptive analyses. The AKI-KRT subcohort was characterized with both descriptive analyses and multinomial logistic regression to assess factors associated with kidney nonrecovery while accounting for death. Results Among a total of 4221 patients in the ICU cohort, 2361 (56%) developed AKI, including 876 (21%) who received KRT. More severe AKI was associated with higher mortality. Among survivors, more severe AKI was associated with an increased rate of kidney nonrecovery and lower kidney function at discharge. Among the 876 patients with AKI-KRT, 588 (67%) died, 95 (11%) had kidney nonrecovery, and 193 (22%) had kidney recovery by the time of discharge. The odds of kidney nonrecovery was greater for lower estimated GFR with odds ratios (ORs) of 2.09 (95% CI, 1.09-4.04), 4.27 (95% CI, 1.99-9.17), and 8.69 (95% CI, 3.07-24.55) for CKD GFR categories 3, 4, and 5, respectively, compared to estimated GFR > 60 mL/min/1.73 m2. Oliguria at the time of KRT initiation was also associated with nonrecovery (OR 2.10 [95% CI, 1.14-3.88] and 4.02 [95% CI, 1.72-9.39] for patients with 50-499 and, Critically ill patients with COVID-19 often develop acute kidney injury (AKI). This study of 4221 patients demonstrated that more severe AKI was associated with greater in-hospital mortality and poorer kidney function at hospital discharge. Among the 876 patients who required dialysis for AKI, almost two-thirds died. Among those who survived to discharge, about two-thirds recovered kidney function and were discharged without the need for dialysis. Lower baseline kidney function and reduced urine output were associated with non-recovery of kidney function. Identification of such predictors is important in assessing prognosis among these critically ill patients and has implications for clinical care of individuals critically ill with COVID-19.
Published: 2021

14. Patient perspectives and involvement in precision medicine research

Author: Knight, Richard, Lecker, Stewart, Stillman, Isaac, Waikar, Sushrut, Mcmahon, Gearoid, Weins, Astrid, Short, Samuel, Hacohen, Nir, Hoover, Paul, Aulisio, Mark, Cooperman, Leslie, Herlitz, Leal, O’Toole, John, Poggio, Emilio, Sedor, John, Jolly, Stacey, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Bomback, Andrew, Canetta, Pietro A., d’Agati, Vivette D., Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Radhakrishnan, Jai, Weng, Chenhua, Barisoni, Laura, Alexandrov, Theodore, Ashkar, Tarek, Barwinska, Daria, Dagher, Pierre, Dunn, Kenneth, Eadon, Michael, Ferkowicz, Michael, Kelly, Katherine, Sutton, Timothy, Winfree, Seth, Menez, Steven, Parikh, Chirag, Rosenberg, Avi, Villalobos, Pam, Malik, Rubab, Fine, Derek, Atta, Mohammed, Monroy Trujillo, Jose Manuel, Slack, Alison, Rosas, Sylvia, Williams, Mark, Azeloglu, Evren, He, Cijang (John), Iyengar, Ravi, Hansen, Jens, Parikh, Samir, Rovin, Brad, Anderton, Chris, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Lukowski, Jessica, Oliver, George (Holt), Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Taneisha, Campbell, Catherine, Hayashi, Lynda, Jefferson, Nichole, Koewler, Robert, Roberts, Glenda, Saul, John, Shpigel, Anna, Stutzke, Edith Christine, Wright, Lorenda, Miegs, Leslie, Pinkeney, Roy, Sealfon, Rachel, Troyanskaya, Olga, Tuttle, Katherine, Dobi, Dejan, Goltsev, Yury, Lake, Blue, Zhang, Kun, Joanes, Maria, Laszik, Zoltan, Schroeder, Andrew, Sarwal, Minnie, Sigdel, Tara, Balis, Ulysses, Blanc, Victoria, He, Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Mariani, Laura, Menon, Rajasree, Otto, Edgar, Schaub, Jennifer, Steck, Becky, Lienczewski, Chrysta, Eddy, Sean, Elder, Michele, Hall, Daniel, Kellum, John, Kruth, Mary, Murugan, Raghav, Palevsky, Paul, Randhawa, Parmjeet, Rosengart, Matthew, Sims-Lucas, Sunny, Stefanick, Mary, Stull, Stacy, Tublin, Mitchell, Alpers, Charles, de Boer, Ian, Dighe, Ashveena, Himmelfarb, Jonathan, Mcclelland, Robyn, Mooney, Sean, Shankland, Stuart, Williams, Kayleen, Blank, Kristina, Carson, Jonas, Dowd, Frederick, Drager, Zach, Park, Christopher, Sharma, Kumar, Zhang, Guanshi, Bansal, Shweta, Venkatachalam, Manjeri, Kermani, Asra, Lee, Simon, Lu, Christopher, Miller, Tyler, Moe, Orson, Park, Harold, Sambandam, Kamalanathan, Sanchez, Francisco, Torrealba, Jose, Robert, Toto, Vazquez, Miguel, Wang, Nancy, Gaut, Joe, Jain, Sanjay, Vijayan, Anitha, Luciano, Randy, Moledina, Dennis, Ugochukwu, Ugwuowo, Wilson, Francis Perry, Alfano, Sandy, Tuttle, Katherine R., Hyashi, Lynda, Roberts, Glenda V., and Stutzke, Christy
Published: 2021
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15. Rationale and design of the Kidney Precision Medicine Project

Author: Knight, Richard, Lecker, Stewart, Stillman, Isaac, Waikar, Sushrut, Mcmahon, Gearoid, Weins, Astrid, Short, Samuel, Hacohen, Nir, Hoover, Paul, Aulisio, Mark, Cooperman, Leslie, Herlitz, Leal, O’Toole, John, Poggio, Emilio, Sedor, John, Jolly, Stacey, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Bomback, Andrew, Canetta, Pietro A., d’Agati, Vivette D., Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Radhakrishnan, Jai, Weng, Chenhua, Barisoni, Laura, Alexandrov, Theodore, Ashkar, Tarek, Barwinska, Daria, Dagher, Pierre, Dunn, Kenneth, Eadon, Michael, Ferkowicz, Michael, Kelly, Katherine, Sutton, Timothy, Winfree, Seth, Menez, Steven, Parikh, Chirag, Rosenberg, Avi, Villalobos, Pam, Malik, Rubab, Fine, Derek, Atta, Mohammed, Monroy Trujillo, Jose Manuel, Slack, Alison, Rosas, Sylvia, Williams, Mark, Azeloglu, Evren, He, Cijang (John), Iyengar, Ravi, Hansen, Jens, Parikh, Samir, Rovin, Brad, Anderton, Chris, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Lukowski, Jessica, Oliver, George (Holt), Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Taneisha, Campbell, Catherine, Hayashi, Lynda, Jefferson, Nichole, Koewler, Robert, Roberts, Glenda, Saul, John, Shpigel, Anna, Stutzke, Edith Christine, Wright, Lorenda, Miegs, Leslie, Pinkeney, Roy, Sealfon, Rachel, Troyanskaya, Olga, Tuttle, Katherine, Dobi, Dejan, Goltsev, Yury, Lake, Blue, Zhang, Kun, Joanes, Maria, Laszik, Zoltan, Schroeder, Andrew, Sarwal, Minnie, Sigdel, Tara, Balis, Ulysses, Blanc, Victoria, He, Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Mariani, Laura, Menon, Rajasree, Otto, Edgar, Schaub, Jennifer, Steck, Becky, Lienczewski, Chrysta, Eddy, Sean, Elder, Michele, Hall, Daniel, Kellum, John, Kruth, Mary, Murugan, Raghav, Palevsky, Paul, Randhawa, Parmjeet, Rosengart, Matthew, Sims-Lucas, Sunny, Stefanick, Mary, Stull, Stacy, Tublin, Mitchell, Alpers, Charles, de Boer, Ian, Dighe, Ashveena, Himmelfarb, Jonathan, Mcclelland, Robyn, Mooney, Sean, Shankland, Stuart, Williams, Kayleen, Blank, Kristina, Carson, Jonas, Dowd, Frederick, Drager, Zach, Park, Christopher, Sharma, Kumar, Zhang, Guanshi, Bansal, Shweta, Venkatachalam, Manjeri, Kermani, Asra, Lee, Simon, Lu, Christopher, Miller, Tyler, Moe, Orson, Park, Harold, Sambandam, Kamalanathan, Sanchez, Francisco, Torrealba, Jose, Robert, Toto, Vazquez, Miguel, Wang, Nancy, Gaut, Joe, Jain, Sanjay, Vijayan, Anitha, Luciano, Randy, Moledina, Dennis, Ugochukwu, Ugwuowo, Wilson, Francis Perry, Alfano, Sandy, de Boer, Ian H., Alpers, Charles E., Azeloglu, Evren U., Balis, Ulysses G.J., Barasch, Jonathan M., Blank, Kristina N., Bomback, Andrew S., Dagher, Pierre C., Dighe, Ashveena L., Eadon, Michael T., El-Achkar, Tarek M., Gaut, Joseph P., He, Yongqun, Hodgin, Jeffrey B., Kellum, John A., Laszik, Zoltan G., Mariani, Laura H., McClelland, Robyn L., Moledina, Dennis G., Mooney, Sean D., O’Toole, John F., Palevsky, Paul M., Parikh, Chirag R., Poggio, Emilio D., Rosas, Sylvia E., Rosengart, Matthew R., Sarwal, Minnie M., Schaub, Jennifer A., Sedor, John R., Toto, Robert D., Troyanskaya, Olga G., Tuttle, Katherine R., Vazquez, Miguel A., and Waikar, Sushrut S.
Published: 2021
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16. Hispanic ethnicity and mortality among critically ill patients with COVID-19

Author: Ana C, Ricardo, Jinsong, Chen, Stephanie M, Toth-Manikowski, Natalie, Meza, Min, Joo, Shruti, Gupta, Deepa G, Lazarous, David E, Leaf, James P, Lash, and Ugochukwu, Ugwuowo
Subjects: Adult, Male, Multidisciplinary, SARS-CoV-2, Critical Illness, COVID-19, Middle Aged, United States, Cohort Studies, Intensive Care Units, Ethnicity, Humans, Female, Hospital Mortality, Aged
Abstract: Background Hispanic persons living in the United States (U.S.) are at higher risk of infection and death from coronavirus disease 2019 (COVID-19) compared with non-Hispanic persons. Whether this disparity exists among critically ill patients with COVID-19 is unknown. Objective To evaluate ethnic disparities in mortality among critically ill adults with COVID-19 enrolled in the Study of the Treatment and Outcomes in Critically Ill Patients with COVID-19 (STOP-COVID). Methods Multicenter cohort study of adults with laboratory-confirmed COVID-19 admitted to intensive care units (ICU) at 67 U.S. hospitals from March 4 to May 9, 2020. Multilevel logistic regression was used to evaluate 28-day mortality across racial/ethnic groups. Results A total of 2153 patients were included (994 [46.2%] Hispanic and 1159 [53.8%] non-Hispanic White). The median (IQR) age was 62 (51–71) years (non-Hispanic White, 66 [57–74] years; Hispanic, 56 [46–67] years), and 1462 (67.9%) were men. Compared with non-Hispanic White patients, Hispanic patients were younger; were less likely to have hypertension, chronic obstructive pulmonary disease, coronary artery disease, or heart failure; and had longer duration of symptoms prior to ICU admission. During median (IQR) follow-up of 14 (7–24) days, 785 patients (36.5%) died. In analyses adjusted for age, sex, clinical characteristics, and hospital size, Hispanic patients had higher odds of death compared with non-Hispanic White patients (OR, 1.44; 95% CI, 1.12–1.84). Conclusions Among critically ill adults with COVID-19, Hispanic patients were more likely to die than non-Hispanic White patients, even though they were younger and had lower comorbidity burden. This finding highlights the need to provide earlier access to care to Hispanic individuals with COVID-19, especially given our finding of longer duration of symptoms prior to ICU admission among Hispanic patients. In addition, there is a critical need to address ongoing disparities in post hospital discharge care for patients with COVID-19.
Published: 2021

17. Hospital-Level Variation in Death for Critically Ill Patients with COVID-19

Author: Matthew M. Churpek, Shruti Gupta, Alexandra B. Spicer, William F. Parker, John Fahrenbach, Samantha K. Brenner, David E. Leaf, Carl P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, Thuy-Duyen Nguyen, Shahzad Shaefi, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Shristi Upadhyay, Ishaan Vohra, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Mehta, Aquino Williams, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Miguel A. Hernán, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily A. Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Pattharawin Pattharanitima, Emily J. Gallagher, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Katherine J. Kelly, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Elena Cervantes, Samir Gautam, Mary C. Mallappallil, Jie Ouyang, Sabu John, Ernie Yap, Yohannes Melaku, Ibrahim Mohamed, Siddartha Bajracharya, Isha Puri, Mariah Thaxton, Jyotsna Bhattacharya, John Wagner, Leon Boudourakis, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Dheeraj Reddy Sirganagari, Pramod K. Guru, Kianoush Kashani, Shahrzad Tehranian, Yan Zhou, Paul A. Bergl, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Michal L. Melamed, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Neelja Kumar, Michael Chang, Jyotsana Thakkar, Ritesh Raichoudhury, Akshay Athreya, Mohamed Farag, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Sobaata Chaudhry, Benjamin Wu, Frank Modersitzki, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner M. B. Mohamed, Rupali S. Avasare, David Zonies, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Ariel L. Mueller, Roberta Redfern, Nicholas S. Cairl, Gabriel Naimy, Abeer Abu-Saif, Danyell Hall, Laura Bickley, Chris Rowan, Farah Madhai-Lovely, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Peter Cangialosi, Mark Liotta, Jared Radbel, Jag Sunderram, Sonika Puri, Jayanth S. Vatson, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Shuchi Anand, Joseph E. Levitt, Pablo Garcia, Suzanne M. Boyle, Rui Song, Ali Arif, Jingjing Zhang, Sang Hoon Woo, Xiaoying Deng, Goni Katz-Greenberg, Katharine Senter, Moh’d A. Sharshir, Vadym V. Rusnak, Muhammad Imran Ali, Terri Peters, Kathy Hughes, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Arash Rashidi, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Santa Monica, Carl E. Schulze, Etienne Macedo, Harin Rhee, Kathleen D. Liu, Vasantha K. Jotwani, Jay L. Koyner, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Alessia Fornoni, Hayley B. Gershengorn, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’Hayer, Chelsea Meloche, Rafey Feroze, Rayan Kaakati, Danny Perry, Abbas Bitar, Elizabeth Anderson, Kishan J. Padalia, Christopher Launius, John P. Donnelly, Andrew J. Admon, Jennifer E. Flythe, Matthew J. Tugman, Emily H. Chang, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Sanjana Kapoor, Siddharth Verma, Huiwen Chen, Csaba P. Kovesdy, Miklos Z. Molnar, Ambreen Azhar, S. Susan Hedayati, Mridula V. Nadamuni, Shani Shastri, Duwayne L. Willett, Samuel A. P. Short, Amanda D. Renaghan, Kyle B. Enfield, Pavan K. Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Nitender Goyal, Anthony J. Faugno, Caroline M. Hsu, Asma Tariq, Leah Meyer, Ravi K. Kshirsagar, Daniel E. Weiner, Marta Christov, Jennifer Griffiths, Sanjeev Gupta, Aromma Kapoor, Perry Wilson, Tanima Arora, and Ugochukwu Ugwuowo
Subjects: Pulmonary and Respiratory Medicine, Male, medicine.medical_specialty, genetic structures, Coronavirus disease 2019 (COVID-19), Critical Illness, Disease, Comorbidity, Critical Care and Intensive Care Medicine, medicine.disease_cause, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, Risk Factors, medicine, Humans, 030212 general & internal medicine, Hospital Mortality, Coronavirus, Aged, Retrospective Studies, Critically ill, business.industry, SARS-CoV-2, Incidence, Editorials, COVID-19, Hospital level, Middle Aged, Prognosis, Intensive care unit, Health equity, United States, Survival Rate, Intensive Care Units, Variation (linguistics), 030228 respiratory system, Emergency medicine, Female, business, Algorithms, Follow-Up Studies
Abstract: Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear.Our objective was to utilize a large, nationally representative dataset of critically ill adults with COVID-19 to determine which factors explain mortality variability.In this multicenter cohort study, we examined adults hospitalized in intensive care units with COVID-19 at 70 United States hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effects logistic regression was used to identify factors associated with interhospital variation. The median odds ratio (OR) was calculated to compare outcomes in higher- vs. lower-mortality hospitals. A gradient boosted machine algorithm was developed for individual-level mortality models.A total of 4,019 patients were included, 1537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0-82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (OR decline from 2.06 [95% CI, 1.73-2.37] to 1.22 [95% CI, 1.00-1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%).There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Published: 2021

18. The Association of COVID-19 With Acute Kidney Injury Independent of Severity of Illness: A Multicenter Cohort Study

Author: Patrick E. Young, Michael Simonov, Jameel Alausa, Melissa Martin, Monique Hinchcliff, Tanima Arora, Lama Ghazi, Jason H. Greenberg, F. Perry Wilson, Sherry G. Mansour, Lloyd G. Cantley, Yu Yamamoto, Labeebah Subair, Jeffrey M. Testani, Aditya Biswas, Ugochukwu Ugwuowo, Dennis G. Moledina, Wade L. Schulz, Aldo J. Peixoto, and Chenxi Huang
Subjects: Male, medicine.medical_specialty, medicine.medical_treatment, 030232 urology & nephrology, Original Investigations, Severity of Illness Index, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Renal Dialysis, Risk Factors, Internal medicine, Severity of illness, medicine, Humans, Vasoconstrictor Agents, Hospital Mortality, 030212 general & internal medicine, Renal Insufficiency, Chronic, Diuretics, Dialysis, Aged, Proportional Hazards Models, Inflammation, SARS-CoV-2, business.industry, Hazard ratio, Acute kidney injury, Absolute risk reduction, COVID-19, Acute Kidney Injury, Length of Stay, Middle Aged, medicine.disease, Respiration, Artificial, United States, Intensive Care Units, C-Reactive Protein, Respiratory failure, Nephrology, Creatinine, Female, business, Kidney disease, Cohort study
Abstract: Rationale and objective While COVID-19 infection has been associated with acute kidney injury (AKI), it is unclear whether this association is independent of traditional risk factors such as hypotension, nephrotoxin exposure, and inflammation. We tested the independent association of COVID-19 with AKI. Study Design Multicenter, observational, cohort study. Setting and participants Patients admitted to one of six hospitals within the Yale-New Haven Health System between 3/10/2020 and 8/31/2020 and tested for SARS-CoV-2 via nasopharyngeal PCR test. Exposure Positive test for SARS-CoV-2. Outcome AKI by Kidney Disease: Improving Global Outcomes criteria. Analytic approach Evaluated the association of COVID-19 with AKI after controlling for time-invariant factors at admission (e.g., demographics, comorbidities) and time-varying factors updated continuously during hospitalization (e.g., vital signs, medications, laboratory results, respiratory failure) using time-updated Cox proportional hazard models. Results Of the 22,122 patients hospitalized between, 2,600 tested positive and 19,522 tested negative for SARS-CoV-2. Compared to patients who tested negative, patients with COVID-19 had more AKI [30.6% vs. 18.2%, absolute risk difference 12.5 (95% CI, 10.6, 14.3)%] and dialysis-requiring AKI (8.5% vs. 3.6%) and lower recovery from AKI (58% vs. 69.8%]. Compared to patients who tested negative, patients with COVID-19 had higher inflammatory markers (C-reactive protein, ferritin), and greater use of vasopressors and diuretics. Compared to patients who tested negative, patients with COVID-19 had higher rate of AKI in univariable analysis (HR, 1.84 [1.73, 1.95]). In fully adjusted model controlling for demographics, comorbidities, vital signs, medications, and laboratory results, COVID-19 remained associated with a high rate of AKI (adjusted HR, 1.40 [1.29-1.53]). Limitations Possibility of residual confounding. Conclusions COVID-19 is associated with high rates of AKI not fully explained by adjustment for known risk factors. This suggests the presence of mechanisms of AKI not accounted for in this analysis, which may include a direct effect of COVID-19 on the kidney or other unmeasured mediators. Future studies should evaluate the possible unique pathways by which COVID-19 may cause AKI., One-third of patients hospitalized with COVID-19 experience acute kidney injury (AKI), which is higher than in other hospitalized patients. Patients with COVID-19 carry many well-known risk factors for AKI including severe lung disease requiring mechanical ventilation, shock and significant inflammation. Whether higher rates of AKI in COVID-19 are above what could be expected in patients with similar risk factors is unknown. We compared AKI rates between those with and without COVID-19 after controlling for risk factors for AKI both before and during hospitalization. We found that COVID-19 was independently associated with high rates of AKI. This indicates that some of the AKI risk in patients with COVID-19 is unexplained by traditional AKI risk factors and is unique to this disease.
Published: 2021
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19. Performance of crisis standards of care guidelines in a cohort of critically ill COVID-19 patients in the United States

Author: Huei Hsun Wen, Vishal Jaikaransingh, Ernie Yap, Matthew T. Scharf, Jiahua Li, Samir Gautam, Jeffery O. Boateng, John Guirguis, Timothy E. Albertson, Shuchi Anand, Kathleen D. Liu, Amar D. Bansal, Alessia Fornoni, Caroline M. Hsu, Simon Correa, Natalie C. Ernecoff, Chris Rowan, William Feldman, Rupali S. Avasare, Maureen Brogan, Etienne Macedo, Nourhan Chaaban, Sabrina Taldone, Ryan Mocerino, Nilam P. Patel, Christopher Meshberger, Muner Mb. Mohamed, Joy-Marie Hermes, Ritesh Raichoudhury, Jamuna Krishnan, Amy M. Zhou, Abbas Bitar, Danny Perry, Barbara A. Danek, Allon N. Friedman, Salim S. Hayek, Richard G. Wunderink, Duwayne L. Willett, Moh’d A. Sharshir, Luis G. Gomez-Escobar, Wei Wang, Nicholas S. Cairl, Lisa Shea, Miguel A. Hernán, Christa A. Schorr, Rubab F. Malik, Patricia F. Kao, John Wagner, Patrick O’ Hayer, Sonika Puri, Shreyak Sharma, Mariah Thaxton, Seble G. Kassaye, Sidharth Shankar, Paul A. Bergl, Hayley B. Gershengorn, Sanjeev Gupta, Ibrahim Mohamed, Sushrut S. Waikar, Dheeraj Reddy Sirganagari, Jill H. Sharma, Gohar Mosoyan, Kyle B. Enfield, Ian A. Strohbehn, Thuy-Duyen Nguyen, Suzanne M. Boyle, Brent Brown, Rebecca V. Levy, Vasantha K. Jotwani, Alexandre M. Shehata, Maria Plataki, Julia L. Jezmir, Valerie Allusson, Jennifer Griffiths, Chirag R. Parikh, Alfredo Iardino, Emily H. Chang, Sanjana Kapoor, Tanira Ferreira, Harin Rhee, Nicholas Sadovnikoff, Aquino Williams, Vivian S. Cruz, Jay L. Koyner, Kristen M. Hess, Leon Boudourakis, Shahzad Shaefi, Vasil Peev, Omer Kamal, Ugochukwu Ugwuowo, Aromma Kapoor, Anitha Vijayan, Jared Radbel, Husam Shadid, Vadym V. Rusnak, Pattharawin Pattharanitima, Aju Jose, Yohannes Adama Melaku, Jayanth S. Vatson, Tariq U. Azam, Yahya Ahmad, William Whalen, Meghan Lee, Shani Shastri, David De La Zerda, Goni Katz-Greenberg, Hanna Berlin, Todd A. Miano, Seth Goldberg, Jatan A. Shah, Frank Modersitzki, Jag Sunderram, Anna E. Hasty, Esha M. Kapania, Samantha K. Brenner, Pennelope K. Blakely, Elizabeth H. Au, Ronaldo C. Go, Keith M. Rose, Anand Srivastava, Kathleen F. Kopecky, Ilya Berim, Alexander Chaitoff, Danyell Hall, Jingjing Zhang, Michel Chonchol, Gabriel Naimy, Sejal B. Shah, Stephanie M. Toth-Manikowski, Christina Mariyam Joy, Deepa G. Lazarous, Matthew W. Semler, Mark Liotta, Mridula V. Nadamuni, Greg L. Schumaker, Patricia Walters, Joseph E. Levitt, Steven G. Coca, Rana Hejal, Stefi Lee, Pramod Guru, Noor ul aain Bhatti, Jennifer E. Flythe, Daniel L. Edmonston, Asma Tariq, John J. Byun, Jesus Rodriguez, Mrigank S. Gupta, Andrew Vissing, Michal L. Melamed, Howard Soh, Adam E. Green, Yorg Azzi, Ladan Golestaneh, Amee Patrawalla, Amber S. Podoll, Ryan C. Spiardi, Xiaoying Deng, Ishaan Vohra, Carl P. Walther, Michael Chang, John P. Donnelly, David M. Charytan, Anthony J. Faugno, Peter Hart, Ameeka Pannu, Sandeep P. Kishore, Roberta E. Redfern, Ambreen Azhar, Meghan E. Sise, Di Pan, Sang Hoon Woo, H. Bryant Nguyen, Pavan K. Bhatraju, Bradford Diephuis, Justin Arunthamakun, Kaltrina Sedaliu, Ajiboye Oyintayo, Aimee Milliken, Andrew J Admon, Elena Cervantes, Erik T. Newman, Heather Yang, Lili Chan, Nitender Goyal, Peter Cangialosi, Arash Rashidi, David Zonies, Juan D. Valencia, Rebecca Lisk, Zoe Post, Farah Madhani-Lovely, Benjamin M. Wu, Princy N. Kumar, Ethan C. Kim, Maheetha Bharadwaj, Chintan V. Shah, A. Bilal Malik, Siddartha Bajracharya, Gabriela Bambrick-Santoyo, Conor P. Crowley, Ellen L. Burnham, Kianoush Kashani, Ashley Macina, Diana Finkel, Rebecca M. Beutler, Sowminya Arikapudi, Ayesha Ahmed, Edward J. Schenck, Kishan Padalia, Aparna Saha, Alexander J. Hodakowski, Tanya S. Johns, Rayan Kaakati, James P. Lash, Bhavarth Shukla, Mary Mallappallil, Eboni G. Price-Haywood, Steven Menez, Samaya J. Anumudu, Christopher L. Mosher, Rajat Kapoor, Harkarandeep Singh, Amanda K. Leonberg-Yoo, Rui Song, Samah Abu Omar, Laura Latta, Siddharth Verma, Steven Y. Chang, Soo Jung Cho, Emily Leven, Denzel Zhu, Jing G. Wang, Katharine Senter, Bijal Mehta, Ariel Mueller, Peter A. McCullough, Alexander S. Leidner, Milagros Yunes, Akshay Athreya, Carlos Martinez, Muhammad Imran Ali, Matthew J. Tugman, Laura Bickley, Perry Wilson, Chanu Rhee, Ambarish M. Athavale, Shruti Gupta, Samuel A.P. Short, S. Susan Hedayati, Neelja Kumar, Abeer Abu-Saif, Jeffrey M. Paer, Sobaata Chaudhry, Louis T. Merriam, Jochen Reiser, Gabriel Contreras, Eric Judd, Isha Puri, Marta Christov, Afshin Ahoubim, Leslie F. Thomas, Tanima Arora, Eric Goralnick, Elizabeth Anderson, Csaba P. Kovesdy, Alanna L. Jacobs, Marie Anne Sosa, Ashita Tolwani, Ravi K. Kshirsagar, Jason Y. Adams, Tingting Li, Javier A. Neyra, Deena R. Altman, Anip Bansal, Katherine J. Kelly, Sunita Sharma, Jean-Sebastien Rachoin, Zoe A. Kibbelaar, Celia P. Corona-Villalobos, Juan Carlos Q. Velez, Tanveer Shaukat, Leah Meyer, Kalyan Prudhvi, Edy Y. Kim, Madona Elias, Brian T. Garibaldi, Miklos Z. Molnar, Megan L. Krajewski, Sabu John, Girish N. Nadkarni, Molly Fisher, Michael Pan, Zaza Cohen, Min J. Joo, Yumeng Wen, Kapil K. Pokharel, Kusum S. Mathews, Shristi Upadhyay, Charles R. Vasquez, Amanda DeMauro Renaghan, Sergio L. Alvarez-Mulett, Rafey Feroze, Jacqueline M. Kruser, Daniel E. Weiner, Anne Sutherland, Jie Ouyang, Mohamed Farag, Gregory P. Milligan, Meaghan S. Roche, Luis A. Matute-Trochez, Chelsea Meloche, Yan Zhou, Jyotsna Bhattacharya, Sonali Bose, and David E. Leaf
Subjects: Adult, Male, Medicine (General), medicine.medical_specialty, Exacerbation, Critical Care, Organ Dysfunction Scores, Critical Illness, Population, Comorbidity, General Biochemistry, Genetics and Molecular Biology, Cohort Studies, R5-920, Intensive care, medicine, Humans, Hospital Mortality, Intensive care medicine, education, Pandemics, intensive care, Aged, Retrospective Studies, education.field_of_study, crisis standards of care, Receiver operating characteristic, business.industry, SARS-CoV-2, Crew Resource Management, Healthcare, COVID-19, Standard of Care, Middle Aged, medicine.disease, Triage, United States, medical ethics, Cohort, Practice Guidelines as Topic, SOFA score, Female, triage, business, Algorithms
Abstract: Summary: Many US states published crisis standards of care (CSC) guidelines for allocating scarce critical care resources during the COVID-19 pandemic. However, the performance of these guidelines in maximizing their population benefit has not been well tested. In 2,272 adults with COVID-19 requiring mechanical ventilation drawn from the Study of the Treatment and Outcomes in Critically Ill Patients with COVID-19 (STOP-COVID) multicenter cohort, we test the following three approaches to CSC algorithms: Sequential Organ Failure Assessment (SOFA) scores grouped into ranges, SOFA score ranges plus comorbidities, and a hypothetical approach using raw SOFA scores not grouped into ranges. We find that area under receiver operating characteristic (AUROC) curves for all three algorithms demonstrate only modest discrimination for 28-day mortality. Adding comorbidity scoring modestly improves algorithm performance over SOFA scores alone. The algorithm incorporating comorbidities has modestly worse predictive performance for Black compared to white patients. CSC algorithms should be empirically examined to refine approaches to the allocation of scarce resources during pandemics and to avoid potential exacerbation of racial inequities.
Published: 2021

20. Prone Positioning and Survival in Mechanically Ventilated Patients With Coronavirus Disease 2019-Related Respiratory Failure

Author: Kusum S, Mathews, Howard, Soh, Shahzad, Shaefi, Wei, Wang, Sonali, Bose, Steven, Coca, Shruti, Gupta, Salim S, Hayek, Anand, Srivastava, Samantha K, Brenner, Jared, Radbel, Adam, Green, Anne, Sutherland, Amanda, Leonberg-Yoo, Alexandre, Shehata, Edward J, Schenck, Samuel A P, Short, Miguel A, Hernán, Lili, Chan, David E, Leaf, and Ugochukwu, Ugwuowo
Subjects: Male, medicine.medical_specialty, medicine.medical_treatment, Critical Care and Intensive Care Medicine, medicine.disease_cause, Patient Positioning, Time-to-Treatment, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Severity of illness, Prone Position, Medicine, Humans, Hospital Mortality, Hypoxia, Survival analysis, Coronavirus, Aged, Mechanical ventilation, business.industry, Proportional hazards model, SARS-CoV-2, Hazard ratio, COVID-19, 030208 emergency & critical care medicine, Middle Aged, Respiration, Artificial, Survival Analysis, United States, Intensive Care Units, 030228 respiratory system, Respiratory failure, Emergency medicine, Female, business, Respiratory Insufficiency, Cohort study
Abstract: Objectives Therapies for patients with respiratory failure from coronavirus disease 2019 are urgently needed. Early implementation of prone positioning ventilation improves survival in patients with acute respiratory distress syndrome, but studies examining the effect of proning on survival in patients with coronavirus disease 2019 are lacking. Our objective was to estimate the effect of early proning initiation on survival in patients with coronavirus disease 2019-associated respiratory failure. Design Data were derived from the Study of the Treatment and Outcomes in Critically Ill Patients with coronavirus disease 2019, a multicenter cohort study of critically ill adults with coronavirus disease 2019 admitted to 68 U.S. hospitals. Using these data, we emulated a target trial of prone positioning ventilation by categorizing mechanically ventilated hypoxemic (ratio of PaO2 over the corresponding FIO2 ≤ 200 mm Hg) patients as having been initiated on proning or not within 2 days of ICU admission. We fit an inverse probability-weighted Cox model to estimate the mortality hazard ratio for early proning versus no early proning. Patients were followed until death, hospital discharge, or end of follow-up. Setting ICUs at 68 U.S. sites. Patients Critically ill adults with laboratory-confirmed coronavirus disease 2019 receiving invasive mechanical ventilation with ratio of PaO2 over the corresponding FIO2 less than or equal to 200 mm Hg. Interventions None. Measurements and main results Among 2,338 eligible patients, 702 (30.0%) were proned within the first 2 days of ICU admission. After inverse probability weighting, baseline and severity of illness characteristics were well-balanced between groups. A total of 1,017 (43.5%) of the 2,338 patients were discharged alive, 1,101 (47.1%) died, and 220 (9.4%) were still hospitalized at last follow-up. Patients proned within the first 2 days of ICU admission had a lower adjusted risk of death compared with nonproned patients (hazard ratio, 0.84; 95% CI, 0.73-0.97). Conclusions In-hospital mortality was lower in mechanically ventilated hypoxemic patients with coronavirus disease 2019 treated with early proning compared with patients whose treatment did not include early proning.
Published: 2021

21. Characteristics and Outcomes of Individuals With Pre-existing Kidney Disease and COVID-19 Admitted to Intensive Care Units in the United States

Author: Jennifer E. Flythe, Magdalene M. Assimon, Matthew J. Tugman, Emily H. Chang, Shruti Gupta, Jatan Shah, Marie Anne Sosa, Amanda DeMauro Renaghan, Michal L. Melamed, F. Perry Wilson, Javier A. Neyra, Arash Rashidi, Suzanne M. Boyle, Shuchi Anand, Marta Christov, Leslie F. Thomas, Daniel Edmonston, David E. Leaf, Carl P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, Thuy-Duyen Nguyen, Shahzad Shaefi, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Shristi Upadhyay, Ishaan Vohra, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Mehta, Aquino Williams, Samantha K. Brenner, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily A. Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Pattharawin Pattharanitima, Emily J. Gallagher, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Katherine J. Kelly, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Carmen Elena Cervantes, Samir C. Gautam, Mary C. Mallappallil, Jie Ouyang, Sabu John, Ernie Yap, Yohannes Melaku, Ibrahim Mohamed, Siddhartha Bajracharya, Isha Puri, Mariah Thaxton, Jyotsna Bhattacharya, John Wagner, Leon Boudourakis, H. Bryant Nguyen, Afshin Ahoubim, Kianoush Kashani, Shahrzad Tehranian, Dheeraj Reddy Sirganagari, Pramod K. Guru, Yan Zhou, Paul A. Bergl, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Neelja Kumar, Michael Chang, Jyotsana Thakkar, Ritesh Raichoudhury, Akshay Athreya, Mohamed Farag, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Sobaata Chaudhry, Benjamin Wu, Frank Modersitzki, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner M.B. Mohamed, Rupali S. Avasare, David Zonies, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Ariel L. Mueller, Roberta Redfern, Nicholas S. Cairl, Gabriel Naimy, Abeer Abu-Saif, Danyell Hall, Laura Bickley, Chris Rowan, Farah Madhani-Lovely, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Peter Cangialosi, Mark Liotta, Jared Radbel, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth S. Vatson, Joseph E. Levitt, Pablo Garcia, Rui Song, Jingjing Zhang, Sang Hoon Woo, Xiaoying Deng, Goni Katz-Greenberg, Katharine Senter, Moh’d A. Sharshir, Vadym V. Rusnak, Muhammad Imran Ali, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Ronald Reagan, Steven Y. Chang, Rebecca M. Beutler, Santa Monica, Carl E. Schulze, Etienne Macedo, Harin Rhee, Kathleen D. Liu, Vasantha K. Jotwani, Jay L. Koyner, Alissa Kunczt, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Nourhan Chaaban, Rajany Dy, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Hayley B. Gershengorn, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’ Hayer, Chelsea Meloche, Rafey Feroze, Rayan Kaakati, Danny Perry, Abbas Bitar, Elizabeth Anderson, Kishan J. Padalia, John P. Donnelly, Andrew J. Admon, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Sanjana Kapoor, Siddharth Verma, Huiwen Chen, Csaba P. Kovesdy, Miklos Z. Molnar, Ambreen Azhar, S. Susan Hedayati, Mridula V. Nadamuni, Shani Shastri, Duwayne L. Willett, Samuel A.P. Short, Amanda D. Renaghan, Kyle B. Enfield, Pavan K. Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Nitender Goyal, Anthony J. Faugno, Caroline M. Hsu, Asma Tariq, Leah Meyer, Ravi K. Kshirsagar, Daniel E. Weiner, Aju Jose, Jennifer Griffiths, Sanjeev Gupta, Aromma Kapoor, Perry Wilson, Tanima Arora, and Ugochukwu Ugwuowo
Subjects: medicine.medical_specialty, 030232 urology & nephrology, Renal function, Original Investigations, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Intensive care, medicine, critical illness, 030212 general & internal medicine, Survival analysis, Kidney, business.industry, SARS-CoV-2, Confounding, COVID-19, Retrospective cohort study, medicine.disease, medicine.anatomical_structure, Respiratory failure, Nephrology, end stage kidney disease, dialysis, business, chronic kidney disease, Kidney disease
Abstract: Rationale & Objective Underlying kidney disease is an emerging risk factor for more severe COVID-19 illness. We examined the clinical courses of critically ill COVID-19 patients with and without pre-existing kidney disease and investigated the association between degree of underlying kidney disease and in-hospital outcomes. Study Design Retrospective cohort study Settings & Participants 4,264 critically ill COVID-19 patients (143 dialysis patients, 521 chronic kidney disease [CKD] patients, and 3,600 patients without CKD) admitted to ICUs at 68 hospitals in the United States. Predictor(s) Presence (versus absence) of pre-existing kidney disease Outcome(s) In-hospital mortality (primary); respiratory failure, shock, ventricular arrhythmia/ cardiac arrest, thromboembolic event, major bleed, and acute liver injury (secondary) Analytical Approach We used standardized differences to compare patient characteristics (values >0.10 indicate a meaningful difference between groups) and multivariable adjusted Fine and Gray survival models to examine outcome associations. Results Dialysis patients had a shorter time from symptom onset to ICU admission compared to other groups (median [quartile 1-quartile 3] days: 4 [2-9] for dialysis patients; 7 [3-10] for CKD patients; 7 [4-10] for patients without pre-existing kidney disease). More dialysis patients (25%) reported altered mental status than those with CKD (20%, standardized difference = 0.12) and no kidney disease (12%, standardized difference = 0.36). Half of dialysis and CKD patients died within 28-days of ICU admission versus 35% of patients without pre-existing kidney disease. Compared to patients without pre-existing kidney disease, dialysis patients had a higher risk of 28-day in-hospital death (adjusted HR 1.41; 95% CI 1.09, 1.81), while patients with CKD had an intermediate risk (adjusted HR 1.25; 95% CI 1.08, 1.44). Limitations Potential residual confounding Conclusions Findings highlight the high mortality of individuals with underlying kidney disease and severe COVID-19, underscoring the importance of identifying safe and effective COVID-19 therapies for this vulnerable population., Individuals with underlying kidney disease may be particularly vulnerable to severe COVID-19 illness, marked by multi-system organ failure, thrombosis, and a heightened inflammatory response. Among 4,264 critically ill adults with COVID-19 admitted to 68 intensive care units across the U.S., we found that both chronic kidney disease and dialysis patients had a ∼50% 28-day in-hospital mortality rate. Patients with underlying kidney disease had higher in-hospital mortality than patients without kidney disease, with patients on maintenance dialysis having the highest risk. As evidenced by differences in symptoms and clinical trajectories, patients with pre-existing kidney disease may have unique susceptibility to COVID-19-related complications which warrants additional study and special consideration in the pursuit and development of targeted therapies.
Published: 2021
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22. Thrombosis, Bleeding, and the Observational Effect of Early Therapeutic Anticoagulation on Survival in Critically Ill Patients With COVID-19

Author: Hanny Al-Samkari, Shruti Gupta, Rebecca Karp Leaf, Wei Wang, Rachel P. Rosovsky, Samantha K. Brenner, Salim S. Hayek, Hanna Berlin, Rajat Kapoor, Shahzad Shaefi, Michal L. Melamed, Anne Sutherland, Jared Radbel, Adam Green, Brian T. Garibaldi, Anand Srivastava, Amanda Leonberg-Yoo, Alexandre M. Shehata, Jennifer E. Flythe, Arash Rashidi, Nitender Goyal, Lili Chan, Kusum S. Mathews, S. Susan Hedayati, Rajany Dy, Stephanie M. Toth-Manikowski, Jingjing Zhang, Mary Mallappallil, Roberta E. Redfern, Amar D. Bansal, Samuel A.P. Short, Mark G. Vangel, Andrew J. Admon, Matthew W. Semler, Kenneth A. Bauer, Miguel A. Hernán, David E. Leaf, Carl P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, Thuy-Duyen Nguyen, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Shristi Upadhyay, Ishaan Vohra, Ajiboye Oyintayo, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Mehta, Aquino Williams, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Amy M. Zhou, Ethan C. Kim, Rebecca Lisk, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily A. Leven, Jing G. Wang, Gohar Mosoyan, Pattharawin Pattharanitima, Emily J. Gallagher, Allon N. Friedman, John Guirguis, Christopher Meshberger, Katherine J. Kelly, Chirag R. Parikh, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Carmen Elena Cervantes, Samir C. Gautam, Mary C. Mallappallil, Jie Ouyang, Sabu John, Ernie Yap, Yohannes Melaku, Ibrahim Mohamed, Siddhartha Bajracharya, Isha Puri, Mariah Thaxton, Jyotsna Bhattacharya, John Wagner, Leon Boudourakis, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Dheeraj Reddy Sirganagari, Pramod K. Guru, Yan Zhou, Paul A. Bergl, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Jyotsana Thakkar, Neelja Kumar, Michael J. Ross, Michael Chang, Ritesh Raichoudhury, Akshay Athreya, Mohamed Farag, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner M.B. Mohamed, Rupali S. Avasare, David Zonies, Rachel Rosovsky, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa, Tanveer Shaukat, Omer Kamal, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Ariel L. Mueller, Nicholas S. Cairl, Gabriel Naimy, Abeer Abu-Saif, Danyell Hall, Laura Bickley, Chris Rowan, Farah Madhani-Lovely, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Peter Cangialosi, Mark Liotta, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth S. Vatson, George Karp, Shuchi Anand, Joseph E. Levitt, Pablo Garcia, Suzanne M. Boyle, Rui Song, Sang Hoon Woo, Xiaoying Deng, Goni Katz-Greenberg, Moh'd A. Sharshir, Vadym V. Rusnak, Muhammad Imran Ali, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Carl E. Schulze, Etienne Macedo, Harin Rhee, Kathleen D. Liu, Vasantha K. Jotwani, Jay L. Koyner, Chintan V. Shah, Vishal Jaikaransingh, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Madona Elias, Yahya Ahmad, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Hayley B. Gershengorn, Alessia Fornoni, Pennelope Blakely, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’Hayer, Chelsea Meloche, Rafey Feroze, Kishan J. Padalia, Jeff Leya, John P. Donnelly, Matthew J. Tugman, Emily H. Chang, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Natalie C. Ernecoff, Sanjana Kapoor, Siddharth Verma, Huiwen Chen, Csaba P. Kovesdy, Miklos Z. Molnar, Ambreen Azhar, Mridula V. Nadamuni, Shani Shastri, Duwayne L. Willett, Amanda D. Renaghan, Kyle B. Enfield, Pavan K. Bhatraju, A. Bilal Malik, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Anthony J. Faugno, Caroline M. Hsu, Asma Tariq, Leah Meyer, Ravi K. Kshirsagar, Daniel E. Weiner, Marta Christov, Jennifer Griffiths, Sanjeev Gupta, Aromma Kapoor, Savneek Chugh, Perry Wilson, Tanima Arora, and Ugochukwu Ugwuowo
Subjects: Male, medicine.medical_specialty, Critical Illness, Hemorrhage, 01 natural sciences, law.invention, 03 medical and health sciences, 0302 clinical medicine, Interquartile range, law, Internal Medicine, medicine, Humans, 030212 general & internal medicine, 0101 mathematics, Survival rate, Original Research, Aged, Proportional hazards model, business.industry, SARS-CoV-2, Incidence (epidemiology), 010102 general mathematics, Hazard ratio, Anticoagulants, COVID-19, General Medicine, Venous Thromboembolism, Blood Coagulation Disorders, Middle Aged, Intensive care unit, United States, Survival Rate, Intensive Care Units, Emergency medicine, Observational study, Female, business, Cohort study
Abstract: Hypercoagulability may be a key mechanism of death in patients with COVID-19. This cohort study evaluated the incidence of venous thromboembolism and major bleeding in critically ill patients with COVID-19 and examined the observational effect of early therapeutic anticoagulation on survival., Visual Abstract. Early Anticoagulation in COVID-19 Hypercoagulability may be a key mechanism of death in patients with COVID-19. This cohort study evaluated the incidence of venous thromboembolism and major bleeding in critically ill patients with COVID-19 and examined the observational effect of early therapeutic anticoagulation on survival. Visual Abstract. Early Anticoagulation in COVID-19 Hypercoagulability may be a key mechanism of death in patients with COVID-19. This cohort study evaluated the incidence of venous thromboembolism and major bleeding in critically ill patients with COVID-19 and examined the observational effect of early therapeutic anticoagulation on survival., Background: Hypercoagulability may be a key mechanism of death in patients with coronavirus disease 2019 (COVID-19). Objective: To evaluate the incidence of venous thromboembolism (VTE) and major bleeding in critically ill patients with COVID-19 and examine the observational effect of early therapeutic anticoagulation on survival. Design: In a multicenter cohort study of 3239 critically ill adults with COVID-19, the incidence of VTE and major bleeding within 14 days after intensive care unit (ICU) admission was evaluated. A target trial emulation in which patients were categorized according to receipt or no receipt of therapeutic anticoagulation in the first 2 days of ICU admission was done to examine the observational effect of early therapeutic anticoagulation on survival. A Cox model with inverse probability weighting to adjust for confounding was used. Setting: 67 hospitals in the United States. Participants: Adults with COVID-19 admitted to a participating ICU. Measurements: Time to death, censored at hospital discharge, or date of last follow-up. Results: Among the 3239 patients included, the median age was 61 years (interquartile range, 53 to 71 years), and 2088 (64.5%) were men. A total of 204 patients (6.3%) developed VTE, and 90 patients (2.8%) developed a major bleeding event. Independent predictors of VTE were male sex and higher D-dimer level on ICU admission. Among the 2809 patients included in the target trial emulation, 384 (11.9%) received early therapeutic anticoagulation. In the primary analysis, during a median follow-up of 27 days, patients who received early therapeutic anticoagulation had a similar risk for death as those who did not (hazard ratio, 1.12 [95% CI, 0.92 to 1.35]). Limitation: Observational design. Conclusion: Among critically ill adults with COVID-19, early therapeutic anticoagulation did not affect survival in the target trial emulation. Primary Funding Source: None.
Published: 2021

23. Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial

Author: Harold I. Feldman, Caitlin Partridge, Aditya Biswas, Paul M. Palevsky, Yu Yamamoto, Dennis G. Moledina, Monique Hinchcliff, Fan Li, Sherry G. Mansour, F. Perry Wilson, Erica Moreira, Ugochukwu Ugwuowo, Chirag R. Parikh, Jason H. Greenberg, Stephen R. Latham, Melissa Martin, Michael Simonov, Tanima Arora, Jeffrey M. Testani, Amit X. Garg, and Haiqun Lin
Subjects: Male, medicine.medical_specialty, Randomization, Medical Records Systems, Computerized, medicine.medical_treatment, 030232 urology & nephrology, law.invention, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, Double-Blind Method, law, Renal Dialysis, medicine, Electronic Health Records, Humans, 030212 general & internal medicine, Dialysis, Aged, Aged, 80 and over, business.industry, Medical record, Research, Acute kidney injury, General Medicine, Acute Kidney Injury, Middle Aged, medicine.disease, Confidence interval, Treatment Outcome, Relative risk, Emergency medicine, Disease Progression, Female, business, Kidney disease
Abstract: Objective To determine whether electronic health record alerts for acute kidney injury would improve patient outcomes of mortality, dialysis, and progression of acute kidney injury. Design Double blinded, multicenter, parallel, randomized controlled trial. Setting Six hospitals (four teaching and two non-teaching) in the Yale New Haven Health System in Connecticut and Rhode Island, US, ranging from small community hospitals to large tertiary care centers. Participants 6030 adult inpatients with acute kidney injury, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria. Interventions An electronic health record based “pop-up” alert for acute kidney injury with an associated acute kidney injury order set upon provider opening of the patient’s medical record. Main outcome measures A composite of progression of acute kidney injury, receipt of dialysis, or death within 14 days of randomization. Prespecified secondary outcomes included outcomes at each hospital and frequency of various care practices for acute kidney injury. Results 6030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.3%) of 3059 patients with an alert and in 622 (20.9%) of 2971 patients receiving usual care (relative risk 1.02, 95% confidence interval 0.93 to 1.13, P=0.67). Analysis by each hospital showed worse outcomes in the two non-teaching hospitals (n=765, 13%), where alerts were associated with a higher risk of the primary outcome (relative risk 1.49, 95% confidence interval 1.12 to 1.98, P=0.006). More deaths occurred at these centers (15.6% in the alert group v 8.6% in the usual care group, P=0.003). Certain acute kidney injury care practices were increased in the alert group but did not appear to mediate these outcomes. Conclusions Alerts did not reduce the risk of our primary outcome among patients in hospital with acute kidney injury. The heterogeneity of effect across clinical centers should lead to a re-evaluation of existing alerting systems for acute kidney injury. Trial registration ClinicalTrials.gov NCT02753751 .
Published: 2021

24. Large-scale, three-dimensional tissue cytometry of the human kidney: a complete and accessible pipeline

Author: Ferkowicz, Michael J., primary, Winfree, Seth, additional, Sabo, Angela R., additional, Kamocka, Malgorzata M., additional, Khochare, Suraj, additional, Barwinska, Daria, additional, Eadon, Michael T., additional, Cheng, Ying-Hua, additional, Phillips, Carrie L., additional, Sutton, Timothy A., additional, Kelly, Katherine J., additional, Dagher, Pierre C., additional, El-Achkar, Tarek M., additional, Dunn, Kenneth W., additional, Knight, Richard, additional, Lecker, Stewart, additional, Stillman, Isaac, additional, Mcmahon, Gearoid, additional, Waikar, Sus, additional, Weins, Astrid, additional, Hacohen, Nir, additional, Hoover, Paul, additional, Aulisio, Mark, additional, Cooperman, Leslie, additional, Herlitz, Leal, additional, O'toole, John, additional, Poggio, Emilio, additional, Sedor, John, additional, Appelbaum, Paul, additional, Barasch, Jonathan, additional, Bomback, Andrew, additional, D'agati, Vivette, additional, Kiryluk, Krzysztof, additional, Mehl, Karla, additional, Shang, Ning (Sunny), additional, Weng, Chenhua, additional, Barisoni, Laura, additional, Alexandrov, Theodore, additional, Ashkar, Tarek, additional, Dagher, Pierre, additional, Dunn, Kenneth, additional, Eadon, Michael, additional, Ferkowicz, Michael, additional, Kelly, Katherine, additional, Sutton, Timothy, additional, Menez, Steven, additional, Parikh, Chirag, additional, Rosenberg, Avi, additional, Villalobos, Pam, additional, Slack, Alison, additional, Rosas, Sylvia, additional, Williams, Mark, additional, Azeloglu, Evren, additional, He, Cijang (John), additional, Iyengar, Ravi, additional, Parikh, Samir, additional, Anderton, Chris, additional, Pasa-Tolic, Ljiljana, additional, Velickovic, Dusan, additional, Oliver, George (Holt), additional, Ardayfio, Joseph, additional, Bebiak, Jack, additional, Brown, Keith, additional, Campbell, Taneisha, additional, Campbell, Catherine, additional, Hayashi, Lynda, additional, Jefferson, Nichole, additional, Koewler, Robert, additional, Roberts, Glenda, additional, Saul, John, additional, Shpigel, Anna, additional, Stutzke, Edith Christine, additional, Wright, Lorenda, additional, Miegs, Leslie, additional, Pinkeney, Roy, additional, Sealfon, Rachel, additional, Troyanskaya, Olga, additional, Tuttle, Katherine, additional, Goltsev, Yury, additional, Lake, Blue, additional, Zhang, Kun, additional, Dobi, Dejan, additional, Joanes, Maria, additional, Laszik, Zoltan, additional, Nolan, Garry, additional, Schroeder, Andrew, additional, Balis, Ulysses, additional, He, Oliver, additional, Hodgin, Jeffrey, additional, Kretzler, Matthias, additional, Mariani, Laura, additional, Menon, Rajasree, additional, Otto, Edgar, additional, Schaub, Jennifer, additional, Steck, Becky, additional, Elder, Michele, additional, Hall, Daniel, additional, Kellum, John, additional, Kruth, Mary, additional, Murugan, Raghav, additional, Palevsky, Paul, additional, Randhawa, Parmjeet, additional, Rosengart, Matthew, additional, Sims-Lucas, Sunny, additional, Stefanick, Mary, additional, Stull, Stacy, additional, Tublin, Mitchell, additional, Alpers, Charles, additional, Boer, Ian De, additional, Fullerton, Malia, additional, Himmelfarb, Jonathan, additional, Mcclelland, Robyn, additional, Mooney, Sean, additional, Shankland, Stuart, additional, Williams, Kayleen, additional, Blank, Kristina, additional, Dighe, Ashveena, additional, Carson, Jonas, additional, Dowd, Frederick, additional, Drager, Zach, additional, Sharma, Kumar, additional, Zhang, Guanshi, additional, Kermani, Asra, additional, Lee, Simon, additional, Lu, Christopher, additional, Miller, Tyler, additional, Moe, Orson, additional, Park, Harold, additional, Sambandam, Kamalanathan, additional, Sanchez, Francisco, additional, Torrealba, Jose, additional, Robert, Toto, additional, Vazquez, Miguel, additional, Wang, Nancy, additional, Gaut, Joe, additional, Jain, Sanjay, additional, Vijayan, Anitha, additional, Luciano, Randy, additional, Moledina, Dennis, additional, Ugochukwu, Ugwuowo, additional, and Wilson, Francis Perry, additional
Published: 2021
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25. AKI Treated with Renal Replacement Therapy in Critically Ill Patients with COVID-19

Author: Shruti, Gupta, Steven G, Coca, Lili, Chan, Michal L, Melamed, Samantha K, Brenner, Salim S, Hayek, Anne, Sutherland, Sonika, Puri, Anand, Srivastava, Amanda, Leonberg-Yoo, Alexandre M, Shehata, Jennifer E, Flythe, Arash, Rashidi, Edward J, Schenck, Nitender, Goyal, S Susan, Hedayati, Rajany, Dy, Anip, Bansal, Ambarish, Athavale, H Bryant, Nguyen, Anitha, Vijayan, David M, Charytan, Carl E, Schulze, Min J, Joo, Allon N, Friedman, Jingjing, Zhang, Marie Anne, Sosa, Eric, Judd, Juan Carlos Q, Velez, Mary, Mallappallil, Roberta E, Redfern, Amar D, Bansal, Javier A, Neyra, Kathleen D, Liu, Amanda D, Renaghan, Marta, Christov, Miklos Z, Molnar, Shreyak, Sharma, Omer, Kamal, Jeffery Owusu, Boateng, Samuel A P, Short, Andrew J, Admon, Meghan E, Sise, Wei, Wang, Chirag R, Parikh, David E, Leaf, and Ugochukwu, Ugwuowo
Subjects: Adult, Male, medicine.medical_specialty, Adolescent, Critical Care, medicine.medical_treatment, 030232 urology & nephrology, 030204 cardiovascular system & hematology, urologic and male genital diseases, Cohort Studies, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Oliguria, Risk Factors, Intensive care, Up Front Matters, medicine, Humans, Renal replacement therapy, Hospital Mortality, Dialysis, Aged, Aged, 80 and over, business.industry, Incidence, Acute kidney injury, COVID-19, Sequela, General Medicine, Acute Kidney Injury, Middle Aged, medicine.disease, female genital diseases and pregnancy complications, United States, Hospitalization, Renal Replacement Therapy, Survival Rate, Logistic Models, Nephrology, Emergency medicine, Female, medicine.symptom, business, Body mass index, Cohort study
Abstract: Background AKI is a common sequela of coronavirus disease 2019 (COVID-19). However, few studies have focused on AKI treated with RRT (AKI-RRT). Methods We conducted a multicenter cohort study of 3099 critically ill adults with COVID-19 admitted to intensive care units (ICUs) at 67 hospitals across the United States. We used multivariable logistic regression to identify patient-and hospital-level risk factors for AKI-RRT and to examine risk factors for 28-day mortality among such patients. Results A total of 637 of 3099 patients (20.6%) developed AKI-RRT within 14 days of ICU admission, 350 of whom (54.9%) died within 28 days of ICU admission. Patient-level risk factors for AKI-RRT included CKD, men, non-White race, hypertension, diabetes mellitus, higher body mass index, higher d-dimer, and greater severity of hypoxemia on ICU admission. Predictors of 28-day mortality in patients with AKI-RRT were older age, severe oliguria, and admission to a hospital with fewer ICU beds or one with greater regional density of COVID-19. At the end of a median follow-up of 17 days (range, 1-123 days), 403 of the 637 patients (63.3%) with AKI-RRT had died, 216 (33.9%) were discharged, and 18 (2.8%) remained hospitalized. Of the 216 patients discharged, 73 (33.8%) remained RRT dependent at discharge, and 39 (18.1%) remained RRT dependent 60 days after ICU admission. Conclusions AKI-RRT is common among critically ill patients with COVID-19 and is associated with a hospital mortality rate of >60%. Among those who survive to discharge, one in three still depends on RRT at discharge, and one in six remains RRT dependent 60 days after ICU admission.
Published: 2020

26. Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept

Author: Dennis G. Moledina, Jason H. Greenberg, F. Perry Wilson, Aditya Biswas, Jeffrey M. Testani, Ugochukwu Ugwuowo, Tanima Arora, Keith Rentfro, Wassim Obeid, Ricardo Vela, Sherry G. Mansour, Veena Rao, Caitlin Partridge, Yu Yamamoto, Chirag R. Parikh, Melissa Martin, Ishan Saran, and Michael Simonov
Subjects: Male, medicine.medical_specialty, Population, 030232 urology & nephrology, urologic and male genital diseases, Risk Assessment, Severity of Illness Index, Article, Blood Urea Nitrogen, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, medicine, Humans, 030212 general & internal medicine, Prospective Studies, education, Oxygen saturation (medicine), Aged, Aged, 80 and over, Creatinine, education.field_of_study, Inpatients, Inpatient mortality, Descriptive statistics, business.industry, urogenital system, Acute kidney injury, Acute Kidney Injury, Middle Aged, medicine.disease, Prognosis, female genital diseases and pregnancy complications, chemistry, ROC Curve, Nephrology, Emergency medicine, Disease Progression, Biomarker (medicine), Female, business, Biomarkers, Cohort study, Follow-Up Studies
Abstract: RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm. STUDY DESIGN: Prospective observational cohort study. SETTING & PARTICIPANTS: 2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours >15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers. EXPOSURE: Clinical characteristics at the time of pre-AKI alert. OUTCOME: AKI within 24 hours of pre-AKI alert (AKI(24)). ANALYTICAL APPROACH: Descriptive statistics and univariable associations. RESULTS: At enrollment, mean predicted probability of AKI(24) was 19.1%; 18.9% of patients went on to develop AKI(24). Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI(24) and 14% among those who did not (P < 0.001). Systolic blood pressure < 100 mm Hg (28% of patients with AKI(24) vs 18% without), heart rate > 100 beats/min (32% of patients with AKI(24) vs 24% without), and oxygen saturation < 92% (15% of patients with AKI(24) vs 6% without) were all more common among those who developed AKI(24). Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI(24) vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%−36%] among patients with AKI(24) vs 34% [IQR, 25%−44%] without) differed between those who did and did not develop AKI(24). LIMITATIONS: Single-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation. CONCLUSIONS: A real-time AKI risk model was successfully integrated into the EHR.
Published: 2020

27. Using evolutionary genomics, transcriptomics, and systems biology to reveal gene networks underlying fungal development

Author: Aditya Gudibanda, Jeffrey P. Townsend, Frances Trail, Ugochukwu Ugwuowo, and Zheng Wang
Subjects: 0301 basic medicine, medicine.medical_specialty, Systems biology, 030106 microbiology, Gene regulatory network, Genomics, Morphology (biology), Computational biology, Biology, Microbiology, 03 medical and health sciences, Multicellular organism, 030104 developmental biology, Phylogenetics, Molecular genetics, Evolutionary developmental biology, medicine
Abstract: Fungal model species have contributed to many aspects of modern biology, from biochemistry and cell biology to molecular genetics. Nevertheless, only a few genes associated with morphological development in fungi have been functionally characterized in terms of their genetic or molecular interactions. Evolutionary developmental biology in fungi faces challenges from a lack of fossil records and unresolved species phylogeny, to homoplasy associated with simple morphology. Traditionally, reductive approaches use genetic screens to reveal phenotypes from a large number of mutants; the efficiency of these approaches relies on profound prior knowledge of the genetics and biology of the designated development trait—knowledge which is often not available for even well-studied fungal model species. Reductive approaches become less efficient for the study of developmental traits that are regulated quantitatively by more than one gene via networks. Recent advances in genome-wide analysis performed in representative multicellular fungal models and non-models have greatly improved upon the traditional reductive approaches in fungal evo-devo research by providing clues for focused knockout strategies. In particular, genome-wide gene expression data across developmental processes of interest in multiple species can expedite the advancement of integrative synthetic and systems biology strategies to reveal regulatory networks underlying fungal development.
Published: 2018
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28. Identification of Patients Expected to Benefit from Electronic Alerts for Acute Kidney Injury

Author: Harold I. Feldman, Amit X. Garg, Aditya Biswas, Stephen R. Latham, Ugochukwu Ugwuowo, Paul M. Palevsky, Haiqun Lin, F. Perry Wilson, and Chirag R. Parikh
Subjects: Adult, Male, medicine.medical_specialty, Randomization, Epidemiology, 030232 urology & nephrology, Psychological intervention, Critical Care and Intensive Care Medicine, Clinical decision support system, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Humans, Medicine, Diagnosis, Computer-Assisted, 030212 general & internal medicine, Aged, Transplantation, Creatinine, business.industry, Acute kidney injury, Original Articles, Acute Kidney Injury, Middle Aged, medicine.disease, Clinical trial, Identification (information), Early Diagnosis, chemistry, Nephrology, Clinical Alarms, Emergency medicine, Female, Personalized medicine, business
Abstract: Background and objectives Electronic alerts for heterogenous conditions such as AKI may not provide benefit for all eligible patients and can lead to alert fatigue, suggesting that personalized alert targeting may be useful. Uplift-based alert targeting may be superior to purely prognostic-targeting of interventions because uplift models assess marginal treatment effect rather than likelihood of outcome. Design, setting, participants, & measurements This is a secondary analysis of a clinical trial of 2278 adult patients with AKI randomized to an automated, electronic alert system versus usual care. We used three uplift algorithms and one purely prognostic algorithm, trained in 70% of the data, and evaluated the effect of targeting alerts to patients with higher scores in the held-out 30% of the data. The performance of the targeting strategy was assessed as the interaction between the model prediction of likelihood to benefit from alerts and randomization status. The outcome of interest was maximum relative change in creatinine from the time of randomization to 3 days after randomization. Results The three uplift score algorithms all gave rise to a significant interaction term, suggesting that a strategy of targeting individuals with higher uplift scores would lead to a beneficial effect of AKI alerting, in contrast to the null effect seen in the overall study. The prognostic model did not successfully stratify patients with regards to benefit of the intervention. Among individuals in the high uplift group, alerting was associated with a median reduction in change in creatinine of −5.3% (P=0.03). In the low uplift group, alerting was associated with a median increase in change in creatinine of +5.3% (P=0.005). Older individuals, women, and those with a lower randomization creatinine were more likely to receive high uplift scores, suggesting that alerts may benefit those with more slowly developing AKI. Conclusions Uplift modeling, which accounts for treatment effect, can successfully target electronic alerts for AKI to those most likely to benefit, whereas purely prognostic targeting cannot.
Published: 2018
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29. Primary hepatic neuroendocrine carcinoma: report of two cases and literature review

Author: Liming Wang, Jin Wang, Jeffrey P. Townsend, Ugochukwu Ugwuowo, and Zi-Ming Zhao
Subjects: Primary hepatic neuroendocrine carcinoma, medicine.medical_specialty, Histology, Liver tumor, Octreotide, Case Report, Immunohistochemical staining, Gastroenterology, Somatostatin analogue, Pathology and Forensic Medicine, Metastasis, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, lcsh:Pathology, Transcatheter arterial chemoembolization, medicine.diagnostic_test, business.industry, Jaundice, Liver biopsy, medicine.disease, Liver Lobe, 030220 oncology & carcinogenesis, 030211 gastroenterology & hepatology, medicine.symptom, business, medicine.drug, lcsh:RB1-214
Abstract: Background Primary hepatic neuroendocrine carcinoma (PHNEC) is extremely rare. The diagnosis of PHNEC remains challenging—partly due to its rarity, and partly due to its lack of unique clinical features. Available treatment options for PHNEC include surgical resection of the liver tumor(s), radiotherapy, liver transplant, transcatheter arterial chemoembolization (TACE), and administration of somatostatin analogues. Case presentation We report two male PHNEC cases and discuss the diagnosis and treatment options. Both cases presented with abdominal pain; case two also presented with symptoms of jaundice. The initial diagnosis for both cases was poorly differentiated grade 3 small-cell neuroendocrine carcinoma, based on imaging characteristics and the pathology of liver biopsies. Final diagnoses of PHNEC were arrived at by ruling out non-hepatic origins. Case one presented with a large tumor in the right liver lobe, and the patient was treated with TACE. Case two presented with tumors in both liver lobes, invasions into the left branch of hepatic portal vein, and metastasis in the hepatic hilar lymph node. This patient was ineligible for TACE and was allergic to the somatostatin analogue octreotide. This limited treatment options to supportive therapies such as albumin supplementation for liver protection. Patient one and two died at 61 and 109 days, respectively, following initial hospital admission. Conclusions We diagnosed both cases with poorly differentiated grade 3 small-cell PHNEC through imaging characteristics, immunohistochemical staining of liver biopsies, and examinations to eliminate non-hepatic origins. Neither TACE nor liver protection appeared to significantly extend survival time of the two patients, suggesting these treatments may be inadequate to improve survival of patients with poorly differentiated grade 3 small-cell PHNEC. The prognosis of poorly differentiated grade 3 small-cell PHNEC is poor due to limited and ineffective treatment options.
Published: 2018
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30. Patient perspectives and involvement in precision medicine research

Author: Tuttle, Katherine R., primary, Bebiak, Jack, additional, Brown, Keith, additional, Campbell, Catherine, additional, Dighe, Ashveena, additional, Hyashi, Lynda, additional, Jefferson, Nichole, additional, Roberts, Glenda V., additional, Stutzke, Christy, additional, Knight, Richard, additional, Lecker, Stewart, additional, Stillman, Isaac, additional, Waikar, Sushrut, additional, Mcmahon, Gearoid, additional, Weins, Astrid, additional, Short, Samuel, additional, Hacohen, Nir, additional, Hoover, Paul, additional, Aulisio, Mark, additional, Cooperman, Leslie, additional, Herlitz, Leal, additional, O’Toole, John, additional, Poggio, Emilio, additional, Sedor, John, additional, Jolly, Stacey, additional, Appelbaum, Paul, additional, Balderes, Olivia, additional, Barasch, Jonathan, additional, Bomback, Andrew, additional, Canetta, Pietro A., additional, d’Agati, Vivette D., additional, Kiryluk, Krzysztof, additional, Kudose, Satoru, additional, Mehl, Karla, additional, Radhakrishnan, Jai, additional, Weng, Chenhua, additional, Barisoni, Laura, additional, Alexandrov, Theodore, additional, Ashkar, Tarek, additional, Barwinska, Daria, additional, Dagher, Pierre, additional, Dunn, Kenneth, additional, Eadon, Michael, additional, Ferkowicz, Michael, additional, Kelly, Katherine, additional, Sutton, Timothy, additional, Winfree, Seth, additional, Menez, Steven, additional, Parikh, Chirag, additional, Rosenberg, Avi, additional, Villalobos, Pam, additional, Malik, Rubab, additional, Fine, Derek, additional, Atta, Mohammed, additional, Monroy Trujillo, Jose Manuel, additional, Slack, Alison, additional, Rosas, Sylvia, additional, Williams, Mark, additional, Azeloglu, Evren, additional, He, Cijang (John), additional, Iyengar, Ravi, additional, Hansen, Jens, additional, Parikh, Samir, additional, Rovin, Brad, additional, Anderton, Chris, additional, Pasa-Tolic, Ljiljana, additional, Velickovic, Dusan, additional, Lukowski, Jessica, additional, Oliver, George (Holt), additional, Ardayfio, Joseph, additional, Campbell, Taneisha, additional, Hayashi, Lynda, additional, Koewler, Robert, additional, Roberts, Glenda, additional, Saul, John, additional, Shpigel, Anna, additional, Stutzke, Edith Christine, additional, Wright, Lorenda, additional, Miegs, Leslie, additional, Pinkeney, Roy, additional, Sealfon, Rachel, additional, Troyanskaya, Olga, additional, Tuttle, Katherine, additional, Dobi, Dejan, additional, Goltsev, Yury, additional, Lake, Blue, additional, Zhang, Kun, additional, Joanes, Maria, additional, Laszik, Zoltan, additional, Schroeder, Andrew, additional, Sarwal, Minnie, additional, Sigdel, Tara, additional, Balis, Ulysses, additional, Blanc, Victoria, additional, He, Oliver, additional, Hodgin, Jeffrey, additional, Kretzler, Matthias, additional, Mariani, Laura, additional, Menon, Rajasree, additional, Otto, Edgar, additional, Schaub, Jennifer, additional, Steck, Becky, additional, Lienczewski, Chrysta, additional, Eddy, Sean, additional, Elder, Michele, additional, Hall, Daniel, additional, Kellum, John, additional, Kruth, Mary, additional, Murugan, Raghav, additional, Palevsky, Paul, additional, Randhawa, Parmjeet, additional, Rosengart, Matthew, additional, Sims-Lucas, Sunny, additional, Stefanick, Mary, additional, Stull, Stacy, additional, Tublin, Mitchell, additional, Alpers, Charles, additional, de Boer, Ian, additional, Himmelfarb, Jonathan, additional, Mcclelland, Robyn, additional, Mooney, Sean, additional, Shankland, Stuart, additional, Williams, Kayleen, additional, Blank, Kristina, additional, Carson, Jonas, additional, Dowd, Frederick, additional, Drager, Zach, additional, Park, Christopher, additional, Sharma, Kumar, additional, Zhang, Guanshi, additional, Bansal, Shweta, additional, Venkatachalam, Manjeri, additional, Kermani, Asra, additional, Lee, Simon, additional, Lu, Christopher, additional, Miller, Tyler, additional, Moe, Orson, additional, Park, Harold, additional, Sambandam, Kamalanathan, additional, Sanchez, Francisco, additional, Torrealba, Jose, additional, Robert, Toto, additional, Vazquez, Miguel, additional, Wang, Nancy, additional, Gaut, Joe, additional, Jain, Sanjay, additional, Vijayan, Anitha, additional, Luciano, Randy, additional, Moledina, Dennis, additional, Ugochukwu, Ugwuowo, additional, Wilson, Francis Perry, additional, and Alfano, Sandy, additional
Published: 2021
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31. Rationale and design of the Kidney Precision Medicine Project

Author: de Boer, Ian H., primary, Alpers, Charles E., additional, Azeloglu, Evren U., additional, Balis, Ulysses G.J., additional, Barasch, Jonathan M., additional, Barisoni, Laura, additional, Blank, Kristina N., additional, Bomback, Andrew S., additional, Brown, Keith, additional, Dagher, Pierre C., additional, Dighe, Ashveena L., additional, Eadon, Michael T., additional, El-Achkar, Tarek M., additional, Gaut, Joseph P., additional, Hacohen, Nir, additional, He, Yongqun, additional, Hodgin, Jeffrey B., additional, Jain, Sanjay, additional, Kellum, John A., additional, Kiryluk, Krzysztof, additional, Knight, Richard, additional, Laszik, Zoltan G., additional, Lienczewski, Chrysta, additional, Mariani, Laura H., additional, McClelland, Robyn L., additional, Menez, Steven, additional, Moledina, Dennis G., additional, Mooney, Sean D., additional, O’Toole, John F., additional, Palevsky, Paul M., additional, Parikh, Chirag R., additional, Poggio, Emilio D., additional, Rosas, Sylvia E., additional, Rosengart, Matthew R., additional, Sarwal, Minnie M., additional, Schaub, Jennifer A., additional, Sedor, John R., additional, Sharma, Kumar, additional, Steck, Becky, additional, Toto, Robert D., additional, Troyanskaya, Olga G., additional, Tuttle, Katherine R., additional, Vazquez, Miguel A., additional, Waikar, Sushrut S., additional, Williams, Kayleen, additional, Wilson, Francis Perry, additional, Zhang, Kun, additional, Iyengar, Ravi, additional, Kretzler, Matthias, additional, Himmelfarb, Jonathan, additional, Lecker, Stewart, additional, Stillman, Isaac, additional, Waikar, Sushrut, additional, Mcmahon, Gearoid, additional, Weins, Astrid, additional, Short, Samuel, additional, Hoover, Paul, additional, Aulisio, Mark, additional, Cooperman, Leslie, additional, Herlitz, Leal, additional, O’Toole, John, additional, Poggio, Emilio, additional, Sedor, John, additional, Jolly, Stacey, additional, Appelbaum, Paul, additional, Balderes, Olivia, additional, Barasch, Jonathan, additional, Bomback, Andrew, additional, Canetta, Pietro A., additional, d’Agati, Vivette D., additional, Kudose, Satoru, additional, Mehl, Karla, additional, Radhakrishnan, Jai, additional, Weng, Chenhua, additional, Alexandrov, Theodore, additional, Ashkar, Tarek, additional, Barwinska, Daria, additional, Dagher, Pierre, additional, Dunn, Kenneth, additional, Eadon, Michael, additional, Ferkowicz, Michael, additional, Kelly, Katherine, additional, Sutton, Timothy, additional, Winfree, Seth, additional, Parikh, Chirag, additional, Rosenberg, Avi, additional, Villalobos, Pam, additional, Malik, Rubab, additional, Fine, Derek, additional, Atta, Mohammed, additional, Monroy Trujillo, Jose Manuel, additional, Slack, Alison, additional, Rosas, Sylvia, additional, Williams, Mark, additional, Azeloglu, Evren, additional, He, Cijang (John), additional, Hansen, Jens, additional, Parikh, Samir, additional, Rovin, Brad, additional, Anderton, Chris, additional, Pasa-Tolic, Ljiljana, additional, Velickovic, Dusan, additional, Lukowski, Jessica, additional, Oliver, George (Holt), additional, Ardayfio, Joseph, additional, Bebiak, Jack, additional, Campbell, Taneisha, additional, Campbell, Catherine, additional, Hayashi, Lynda, additional, Jefferson, Nichole, additional, Koewler, Robert, additional, Roberts, Glenda, additional, Saul, John, additional, Shpigel, Anna, additional, Stutzke, Edith Christine, additional, Wright, Lorenda, additional, Miegs, Leslie, additional, Pinkeney, Roy, additional, Sealfon, Rachel, additional, Troyanskaya, Olga, additional, Tuttle, Katherine, additional, Dobi, Dejan, additional, Goltsev, Yury, additional, Lake, Blue, additional, Joanes, Maria, additional, Laszik, Zoltan, additional, Schroeder, Andrew, additional, Sarwal, Minnie, additional, Sigdel, Tara, additional, Balis, Ulysses, additional, Blanc, Victoria, additional, He, Oliver, additional, Hodgin, Jeffrey, additional, Mariani, Laura, additional, Menon, Rajasree, additional, Otto, Edgar, additional, Schaub, Jennifer, additional, Eddy, Sean, additional, Elder, Michele, additional, Hall, Daniel, additional, Kellum, John, additional, Kruth, Mary, additional, Murugan, Raghav, additional, Palevsky, Paul, additional, Randhawa, Parmjeet, additional, Rosengart, Matthew, additional, Sims-Lucas, Sunny, additional, Stefanick, Mary, additional, Stull, Stacy, additional, Tublin, Mitchell, additional, Alpers, Charles, additional, de Boer, Ian, additional, Dighe, Ashveena, additional, Mcclelland, Robyn, additional, Mooney, Sean, additional, Shankland, Stuart, additional, Blank, Kristina, additional, Carson, Jonas, additional, Dowd, Frederick, additional, Drager, Zach, additional, Park, Christopher, additional, Zhang, Guanshi, additional, Bansal, Shweta, additional, Venkatachalam, Manjeri, additional, Kermani, Asra, additional, Lee, Simon, additional, Lu, Christopher, additional, Miller, Tyler, additional, Moe, Orson, additional, Park, Harold, additional, Sambandam, Kamalanathan, additional, Sanchez, Francisco, additional, Torrealba, Jose, additional, Robert, Toto, additional, Vazquez, Miguel, additional, Wang, Nancy, additional, Gaut, Joe, additional, Vijayan, Anitha, additional, Luciano, Randy, additional, Moledina, Dennis, additional, Ugochukwu, Ugwuowo, additional, and Alfano, Sandy, additional
Published: 2021
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32. Electronic Alerts for Acute Kidney Injury Amelioration (ELAIA-1): a completely electronic, multicentre, randomised controlled trial: design and rationale

Author: Francis P. Wilson, Harold I. Feldman, Aditya Biswas, Ugochukwu Ugwuowo, Yu Yamamoto, Noah Gourlie, Stephen R. Latham, Boian Etropolski, Melissa Martin, Chirag R. Parikh, Paul M. Palevsky, Marina Mutter, Amit X. Garg, Haiqun Lin, and Erica Moreira
Subjects: clinical decision support, Adult, Male, 030232 urology & nephrology, Pilot Projects, Clinical decision support system, acute renal failure, Severity of Illness Index, law.invention, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, Clinical Protocols, Informed consent, law, Intervention (counseling), medicine, Protocol, Humans, 030212 general & internal medicine, Electronic Data Processing, Data collection, Renal Medicine, business.industry, General Medicine, electronic health record, Acute Kidney Injury, Middle Aged, medicine.disease, Waiver, 3. Good health, Data sharing, Intensive Care Units, Early Diagnosis, Outcome and Process Assessment, Health Care, Clinical Alarms, Creatinine, Inclusion and exclusion criteria, Female, Medical emergency, business, randomized, alert, Biomarkers
Abstract: IntroductionAcute kidney injury (AKI) is common among hospitalised patients and under-recognised by providers and yet carries a significant risk of morbidity and mortality. Electronic alerts for AKI have become more common despite a lack of strong evidence of their benefits. We designed a multicentre, randomised, controlled trial to evaluate the effectiveness of AKI alerts. Our aim is to highlight several challenges faced in the design of this trial, which uses electronic screening, enrolment, randomisation, intervention and data collection.Methods and analysisThe design and implementation of an electronic alert system for AKI was a reiterative process involving several challenges and limitations set by the confines of the electronic medical record system. The trial will electronically identify and randomise 6030 adults with AKI at six hospitals over a 1.5–2 year period to usual care versus an electronic alert containing an AKI-specific order set. Our primary outcome will be a composite of AKI progression, inpatient dialysis and inpatient death within 14 days of randomisation. During a 1-month pilot in the medical intensive care unit of Yale New Haven Hospital, we have demonstrated feasibility of automating enrolment and data collection. Feedback from providers exposed to the alerts was used to continually improve alert clarity, user friendliness and alert specificity through refined inclusion and exclusion criteria.Ethics and disseminationThis study has been approved by the appropriate ethics committees for each of our study sites. Our study qualified for a waiver of informed consent as it presents no more than minimal risk and cannot be feasibly conducted in the absence of a waiver. We are committed to open dissemination of our data through clinicaltrials.gov and submission of results to the NIH data sharing repository. Results of our trial will be submitted for publication in a peer-reviewed journal.Trial registration numberNCT02753751; Pre-results.
Published: 2019

33. Association between long-term NSAID use and opioid abuse among patients with breast cancer

Author: Olalekan Akanbi, Chinelo C. Orji, Onyenikewe Igbeta, Nnaemeka Onyeakusi, Olubode A. Olufajo, Fahad Mukhtar, Ugochukwu Ugwuowo, Adeyinka Charles Adejumo, and Semiu O. Gbadamosi
Subjects: Risk, Cancer Research, medicine.medical_specialty, Pain, Breast Neoplasms, Odds, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Pain control, Internal medicine, Odds Ratio, Medicine, Humans, 030212 general & internal medicine, skin and connective tissue diseases, Aged, Retrospective Studies, Inpatient mortality, business.industry, Anti-Inflammatory Agents, Non-Steroidal, Opioid abuse, Odds ratio, Pain management, Middle Aged, Cancer-Related Pain, medicine.disease, Opioid-Related Disorders, Analgesics, Opioid, Oncology, 030220 oncology & carcinogenesis, Female, business
Abstract: BACKGROUND : Improving survival rates among patients with breast cancer has been associated with an increase in the prevalence of co-morbidities like cancer-related pain. Opioids are an important component in the management of pain among these patients. However, the progression from judicious use to abuse defeats the aim of pain control. Non-steroidal anti-inflammatory drugs (NSAIDs) are recommended as the first step in cancer-related pain management. Due to their anti-inflammatory, anti-neoplastic and neuroprotective properties, NSAIDs have been shown to reduce the risk of progression of certain cancers including breast cancers. In this study, we assessed whether an association exists between long-term NSAID use and opioid abuse among breast cancer survivors. We also explored the relationship between long-term NSAID use and inpatient mortality and length of stay (LOS). METHODS Using ICD-9-CM codes, we identified and selected women aged 18 years and older with breast cancer from the National Inpatient Sample. Our primary predictor was a history of long-term NSAID use. Multivariable regression models were employed in assessing the association between long-term NSAID use and opioid abuse, inpatient mortality and LOS . RESULTS Among 170,644 women with breast cancer, 7,838 (4.6%) reported a history of long-term NSAID use. Patients with a history of long-term NSAID use had lower odds of opioid abuse (adjusted odds ratio (aOR) 0.53; 95% CI [0.32-0.88]), lower in-hospital mortality (aOR 0.52; 95% CI [0.45-0.60]) and shorter LOS (7.12 vs. 8.11 days). DISCUSSION Further studies are needed to understand the underlying mechanism of the association between long-term NSAID use and opioid abuse.
Published: 2019

34. Additional file 2: of Primary hepatic neuroendocrine carcinoma: report of two cases and literature review

Author: Zhao, Zi-Ming, Wang, Jin, Ugochukwu Ugwuowo, Liming Wang, and Townsend, Jeffrey
Abstract: Figure S2. Microscopic finding of a carcinoma of neuroendocrine cells in the liver biopsy from case two, the original image of Fig. 8 before adjustment in Photoshop to remove the pink background and increase the contrast. (PDF 3192Â kb)
Published: 2018
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35. Additional file 1: of Primary hepatic neuroendocrine carcinoma: report of two cases and literature review

Author: Zhao, Zi-Ming, Wang, Jin, Ugochukwu Ugwuowo, Liming Wang, and Townsend, Jeffrey
Abstract: Figure S1. Microscopic finding of a carcinoma of neuroendocrine cells in the liver biopsy from case one, the original image of Fig. 5, before adjustment in Photoshop to remove the pink background and increase the contrast. (PDF 3304Â kb)
Published: 2018
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36. 3002 Effect of Long-Term NSAID Use on Opioid Abuse and Health Outcomes among Breast Cancer Patients

Author: Chinelo C. Orji, Adeyinka Charles Adejumo, Nnaemeka Onyeakusi, Ugochukwu Ugwuowo, Semiu O. Gbadamosi, Onyenikewe Igbeta, and Fahad Mukhtar
Subjects: medicine.medical_specialty, Text mining, Breast cancer, Clinical Epidemiology/Clinical Trial, business.industry, Opioid abuse, Medicine, General Medicine, business, Intensive care medicine, Health outcomes, medicine.disease, Term (time)
Abstract: OBJECTIVES/SPECIFIC AIMS: Cancer related pain presents a significant risk for opioid abuse among cancer survivors and contributes to the current opioid crisis. Nearly 90% of breast cancer patients have been reported to have cancer-related pain requiring treatment. Opioids, in combination with NSAIDs, have been widely used for pain management in this population despite the risk of abuse. Long-term NSAID use due to their antineoplastic and neuroprotective effects may offer additional protective effects against opioid abuse. Here, we assess the relationship between NSAID use and opioid abuse among breast cancer patients. METHODS/STUDY POPULATION: Using ICD-9-CM codes, we identified and selected women aged >18 years with breast cancer from the National Inpatient Sample (NIS). Our primary predictor was a history of long-term NSAID use. Opioid abuse was the primary outcome of interest. Secondary outcomes were inpatient mortality and length of stay. Multivariable regression models were employed in assessing the association between predictors and outcomes while adjusting for relevant covariates. RESULTS/ANTICIPATED RESULTS: Among 170,644 women with breast cancer, 7,838 (4.6%) reported a history of long-term NSAID use. Patients with a history of long-term NSAID use had lower odds of opioid abuse (aOR 0.53; 95% CI [0.32-0.88]) and in-hospital mortality (aOR 0.52; 95% CI [0.45-0.60]) and were likely to have shorter hospital stay (7.12 vs. 8.11 days) compared to women with no history of long-term NSAID use. DISCUSSION/SIGNIFICANCE OF IMPACT: Long-term NSAID use may offer a protective effect against opioid abuse and improve in-hospital outcomes translating to better quality of life and healthcare utilization indices among breast cancer patients.
Published: 2019

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