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2. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection

Author

Paranjpe, Ishan, Jayaraman, Pushkala, Su, Chen-Yang, Zhou, Sirui, Chen, Steven, Thompson, Ryan, Del Valle, Diane Marie, Kenigsberg, Ephraim, Zhao, Shan, Jaladanki, Suraj, Chaudhary, Kumardeep, Ascolillo, Steven, Vaid, Akhil, Gonzalez-Kozlova, Edgar, Kauffman, Justin, Kumar, Arvind, Paranjpe, Manish, Hagan, Ross O., Kamat, Samir, Gulamali, Faris F., Xie, Hui, Harris, Joceyln, Patel, Manishkumar, Argueta, Kimberly, Batchelor, Craig, Nie, Kai, Dellepiane, Sergio, Scott, Leisha, Levin, Matthew A., He, John Cijiang, Suarez-Farinas, Mayte, Coca, Steven G., Chan, Lili, Azeloglu, Evren U., Schadt, Eric, Beckmann, Noam, Gnjatic, Sacha, Merad, Miram, Kim-Schulze, Seunghee, Richards, Brent, Glicksberg, Benjamin S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2023
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6. The ASSESS-AKI Study found urinary epidermal growth factor is associated with reduced risk of major adverse kidney events

Author

Menez, Steven, Wen, Yumeng, Xu, Leyuan, Moledina, Dennis G., Thiessen-Philbrook, Heather, Hu, David, Obeid, Wassim, Bhatraju, Pavan K., Ikizler, T. Alp, Siew, Edward D., Chinchilli, Vernon M., Garg, Amit X., Go, Alan S., Liu, Kathleen D., Kaufman, James S., Kimmel, Paul L., Himmelfarb, Jonathan, Coca, Steven G., Cantley, Lloyd G., and Parikh, Chirag R.

Published
2023
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8. Plasma Biomarkers and Incident CKD Among Individuals Without Diabetes

Author

Le, Dustin, Chen, Jingsha, Shlipak, Michael G., Ix, Joachim H., Sarnak, Mark J., Gutierrez, Orlando M., Schelling, Jeffrey R., Bonventre, Joseph V., Sabbisetti, Venkata S., Schrauben, Sarah J., Coca, Steven G., Kimmel, Paul L., Vasan, Ramachandran S., Grams, Morgan E., Parikh, Chirag, Coresh, Josef, and Rebholz, Casey M.

Published
2023
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9. Patient-Specific Pharmacokinetics and Dasatinib Nephrotoxicity

Author

Adegbite, Benjamin O., Abramson, Matthew H., Gutgarts, Victoria, Musteata, Florin M., Chauhan, Kinsuk, Muwonge, Alecia N., Meliambro, Kristin A., Salvatore, Steven P., El Ghaity-Beckley, Sebastian, Kremyanskaya, Marina, Marcellino, Bridget, Mascarenhas, John O., Campbell, Kirk N., Chan, Lili, Coca, Steven G., Berman, Ellin M., Jaimes, Edgar A., and Azeloglu, Evren U.

Published
2023
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10. Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19

Author

Deng, Jie, Atta, Mo, Bagnasco, Serena M., Ko, Albert, Iwasaki, Akiko, Farhadian, Shelli, Nelson, Allison, Casanovas-Massana, Arnau, White, Elizabeth B., Schulz, Wade, Coppi, Andreas, Young, Patrick, Nunez, Angela, Shepard, Denise, Matos, Irene, Strong, Yvette, Anastasio, Kelly, Brower, Kristina, Kuang, Maxine, Chiorazzi, Michael, Bermejo, Santos, Vijayakumar, Pavithra, Geng, Bertie, Fournier, John, Minasyan, Maksym, Muenker, M. Catherine, Moore, Adam J., Nadkarni, Girish, Menez, Steven, Coca, Steven G., Moledina, Dennis G., Wen, Yumeng, Chan, Lili, Thiessen-Philbrook, Heather, Obeid, Wassim, Garibaldi, Brian T., Azeloglu, Evren U., Ugwuowo, Ugochukwu, Sperati, C. John, Arend, Lois J., Rosenberg, Avi Z., Kaushal, Madhurima, Jain, Sanjay, Wilson, F. Perry, and Parikh, Chirag R.

Published
2023
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11. Joint Modeling of Clinical and Biomarker Data in Acute Kidney Injury Defines Unique Subphenotypes with Differing Outcomes

Author

Vasquez-Rios, George, Oh, Wonsuk, Lee, Samuel, Bhatraju, Pavan, Mansour, Sherry G., Moledina, Dennis G., Gulamali, Faris F., Siew, Edward D., Garg, Amit X., Sarder, Pinaki, Chinchilli, Vernon M., Kaufman, James S., Hsu, Chi-yuan, Liu, Kathleen D., Kimmel, Paul L., Go, Alan S., Wurfel, Mark M., Himmelfarb, Jonathan, Parikh, Chirag R., Coca, Steven G., and Nadkarni, Girish N.

Published
2023
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12. Plasma Biomarkers as Risk Factors for Incident CKD

Author

Sarnak, Mark J., Katz, Ronit, Ix, Joachim H., Kimmel, Paul L., Bonventre, Joseph V., Schelling, Jeffrey, Cushman, Mary, Vasan, Ramachandran S., Waikar, Sushrut S., Greenberg, Jason H., Parikh, Chirag R., Coca, Steven G., Sabbisetti, Venkata, Jogalekar, Manasi P., Rebholz, Casey, Zheng, Zihe, Gutierrez, Orlando M., and Shlipak, Michael G.

Published
2022
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15. Kidney Recovery and Death in Critically Ill Patients With COVID-19–Associated Acute Kidney Injury Treated With Dialysis: The STOP-COVID Cohort Study

Author

Gupta, Hsu, Faugno, Goyal, Raichoudhury, Tariq, Meyer, Sharma, Leaf, Kshirsagar, Walther, Carl P., Anumudu, Samaya J., Arunthamakun, Justin, Kopecky, Kathleen F., Milligan, Gregory P., McCullough, Peter A., Nguyen, Thuy-Duyen, Shaefi, Shahzad, Krajewski, Megan L., Shankar, Sidharth, Pannu, Ameeka, Valencia, Juan D., Bauer, Kenneth A., Waikar, Sushrut S., Kibbelaar, Zoe A., Athavale, Ambarish M., Hart, Peter, Upadhyay, Shristi, Vohra, Ishaan, Oyintayo, Ajiboye, Green, Adam, Rachoin, Jean-Sebastien, Schorr, Christa A., Shea, Lisa, Edmonston, Daniel L., Mosher, Christopher L., Shehata, Alexandre M., Cohen, Zaza, Allusson, Valerie, Bambrick-Santoyo, Gabriela, Bhatti, Noor ul aain, Mehta, Bijal, WilliamsSamantha K. Brenner, Aquino, Walters, Patricia, Go, Ronaldo C., Rose, Keith M., Hernán, Miguel A., Zhou, Amy M., Kim, Ethan C., Lisk, Rebecca, Chan, Lili, Mathews, Kusum S., Coca, Steven G., Altman, Deena R., Saha, Aparna, Soh, Howard, Wen, Huei Hsun, Bose, Sonali, Leven, Emily A., Wang, Jing G., Mosoyan, Gohar, Pattharanitima, Pattharawin, Gallagher, Emily J., Friedman, Allon N., Guirguis, John, Kapoor, Rajat, Meshberger, Christopher, Kelly, Katherine J., Parikh, Chirag R., Garibaldi, Brian T., Corona-Villalobos, Celia P., Wen, Yumeng, Menez, Steven, Malik, Rubab F., Cervantes, Carmen Elena, Gautam, Samir C., Mallappallil, Mary C., Ouyang, Jie, John, Sabu, Yap, Ernie, Melaku, Yohannes, Mohamed, Ibrahim, Bajracharya, Siddhartha, Puri, Isha, Thaxton, Mariah, Bhattacharya, Jyotsna, Wagner, John, Boudourakis, Leon, Nguyen, H. Bryant, Ahoubim, Afshin, Thomas, Leslie F., Sirganagari, Dheeraj Reddy, Guru, Pramod K., Kashani, Kianoush, Tehranian, Shahrzad, Zhou, Yan, Bergl, Paul A., Rodriguez, Jesus, Shah, Jatan A., Gupta, Mrigank S., Kumar, Princy N., Lazarous, Deepa G., Kassaye, Seble G., Melamed, Michal L., Johns. Ryan Mocerino, Tanya S., Prudhvi, Kalyan, Zhu, Denzel, Levy, Rebecca V., Azzi, Yorg, Fisher, Molly, Yunes, Milagros, Sedaliu, Kaltrina, Golestaneh, Ladan, Brogan, Maureen, Thakkar, Jyotsana, Kumar, Neelja, Ross, Michael J., Chang, Michael, Athreya, Akshay, Farag, Mohamed, Schenck, Edward J., Cho, Soo Jung, Plataki, Maria, Alvarez-Mulett, Sergio L., Gomez-Escobar, Luis G., Pan, Di, Lee, Stefi, Krishnan, Jamuna, Whalen, William, Charytan, David, Macina, Ashley, Srivastava, Anand, Leidner, Alexander S., Martinez, Carlos, Kruser, Jacqueline M., Wunderink, Richard G., Hodakowski, Alexander J., Velez, Juan Carlos Q., Price-Haywood, Eboni G., Matute-Trochez, Luis A., Hasty, Anna E., Mohamed, Muner M.B., Avasare, Rupali S., Zonies, David, Al-Samkari, Hanny, Leaf, Rebecca Karp, Rosovsky, Rachel, Sise, Meghan E., Newman, Erik T., Omar, Samah Abu, Pokharel, Kapil K., Sharma, Shreyak, Singh, Harkarandeep, Correa, Simon, Shaukat, Tanveer, Kamal, Omer, Wang, Wei, Lee, Meghan, Strohbehn, Ian A., Li, Jiahua, Mueller, Ariel L., Redfern, Roberta E., Cairl, Nicholas S., Naimy, Gabriel, Abu-Saif, Abeer, Hall, Danyell, Bickley, Laura, Rowan, Chris, Madhani-Lovely, Farah, Peev, Vasil, Reiser, Jochen, Byun, John J., Vissing, Andrew, Kapania, Esha M., Post, Zoe, Patel, Nilam P., Hermes, Joy-Marie, Sutherland, Anne K., Patrawalla, Amee, Finkel, Diana G., Danek, Barbara A., Arikapudi, Sowminya, Paer, Jeffrey M., Cangialosi, Peter, Liotta, Mark, Radbel, Jared, Puri, Sonika, Sunderram, Jag, Scharf, Matthew T., Ahmed, Ayesha, Berim, Ilya, Vatson, Jayanth S., Karp, George, Anand, Shuchi, Levitt, Joseph E., Garcia, Pablo, Boyle, Suzanne M., Song, Rui, Zhang, Jingjing, Woo, Sang Hoon, Deng, Xiaoying, Katz-Greenberg, Goni, Sharshir, Moh’d A., Rusnak, Vadym V., Bansal, Anip, Podoll, Amber S., Chonchol, Michel, Sharma, Sunita, Burnham, Ellen L., Rashidi, Arash, Hejal, Rana, Judd, Eric, Latta, Laura, Tolwani, Ashita, Albertson, Timothy E., Adams, Jason Y., Chang, Steven Y., Beutler, Rebecca M., Schulze, Carl E., Macedo, Etienne, RheeKa, Harin, thleen D. Liu, Jotwani, Vasantha K., Koyner, Jay L., Shah, Chintan V., Jaikaransingh, Vishal, Toth-Manikowski, Stephanie M., Joo, Min J., Lash, James P., Neyra, Javier A., Chaaban, Nourhan, Elias, Madona, Ahmad, Yahya, Dy, Rajany, Iardino, Alfredo, Au, Elizabeth H., Sosa, Marie Anne, Taldone, Sabrina, Contreras, Gabriel, De La Zerda, David, Gershengorn, Hayley B., Fornoni, Alessia, Hayek, Salim S., Blakely, Pennelope, Berlin, Hanna, Azam, Tariq U., Shadid, Husam, Pan, Michael, O’Hayer, Patrick, Meloche, Chelsea, Feroze, Rafey, Padalia, Kishan J., Leya, Jeff, Donnelly, John P., Admon, Andrew J., Flythe, Jennifer E., Tugman, Matthew J., Chang, Emily H., Brown, Brent R., Leonberg-Yoo, Amanda K., Spiardi, Ryan C., Miano, Todd A., Roche, Meaghan S., Vasquez, Charles R., Bansal, Amar D., Ernecoff, Natalie C., Kapoor, Sanjana, Verma, Siddharth, Chen, Huiwen, Kovesdy, Csaba P., Molnar, Miklos Z., Azhar, Ambreen, Hedayati, S. Susan, Nadamuni, Mridula V., Shastri, Shani, Willett, Duwayne L., Short, Samuel A.P., Renaghan, Amanda D., Enfield, Kyle B., Bhatraju, Pavan K., Malik, A. Bilal, Semler, Matthew W., Vijayan, Anitha, Joy, Christina Mariyam, Li, Tingting, Goldberg, Seth, Kao, Patricia F., Schumaker, Greg L., Christov, Marta, Griffiths, Jennifer, Gupta, Sanjeev, Kapoor, Aromma, Chugh, Savneek, Wilson, Perry, Arora, Tanima, Ugwuowo, Ugochukwu, Hsu, Caroline M., Gupta, Shruti, Tighiouart, Hocine, Goyal, Nitender, Faugno, Anthony J., Tariq, Asma, Raichoudhury, Ritesh, Sharma, Jill H., Meyer, Leah, Kshirsagar, Ravi K., Jose, Aju, Leaf, David E., and Weiner, Daniel E.

Published
2022
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20. A prospective cohort study of acute kidney injury and kidney outcomes, cardiovascular events, and death

Author

Chinchilli, Vernon M., Go, Alan S., Himmelfarb, Jonathan, Ikizler, T. Alp, Kaufman, James S., Kimmel, Paul L., Parikh, Chirag R., Stokes, John B., Coca, Steven, Garg, Amit, Zheng, Sijie, Pravoverov, Leonid, Hsu, Chi-yuan, Hsu, Raymond K., Liu, Kathleen D., Reeves, W. Brian, Siew, Edward D., Lewis, Julia B., Ware, Lorraine, Devarajan, Prasad, Krawczeski, Catherine, Bennett, Michael, Zappitelli, Michael, Wurfel, Mark, Coca, Steven G., Garg, Amit X., Wurfel, Mark M., Ware, Lorraine B., Faulkner, Georgia Brown, and Tan, Thida C.

Published
2021
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22. Modulation of the Association Between Age and Death by Risk Factor Burden in Critically Ill Patients With COVID-19

Author

Sunderraj, Ashwin, Cho, Chloe, Cai, Xuan, Gupta, Shruti, Mehta, Rupal, Isakova, Tamara, Leaf, David E., Srivastava, Anand, Walther, Carl P., Anumudu, Samaya J., Arunthamakun, Justin, Kopecky, Kathleen F., Milligan, Gregory P., McCullough, Peter A., Nguyen, Thuy-Duyen, Shaefi, Shahzad, Krajewski, Megan L., Shankar, Sidharth, Pannu, Ameeka, Valencia, Juan D., Waikar, Sushrut S., Kibbelaar, Zoe A., Athavale, Ambarish M., Hart, Peter, Upadhyay, Shristi, Vohra, Ishaan, Oyintayo, Ajiboye, Green, Adam, Rachoin, Jean-Sebastien, Schorr, Christa A., Shea, Lisa, Edmonston, Daniel L., Mosher, Christopher L., Shehata, Alexandre M., Cohen, Zaza, Allusson, Valerie, Bambrick-Santoyo, Gabriela, Bhatti, Noor ul aain, Mehta, Bijal, Williams, Aquino, Brenner, Samantha K., Walters, Patricia, Go, Ronaldo C., Rose, Keith M., Hernán, Miguel A., Zhou, Amy M., Kim, Ethan C., Lisk, Rebecca, Chan, Lili, Mathews, Kusum S., Coca, Steven G., Altman, Deena R., Saha, Aparna, Soh, Howard, Hsun Wen, Huei, Bose, Sonali, Leven, Emily A., Wang, Jing G., Mosoyan, Gohar, Nadkarni, Girish N., Pattharanitima, Pattharawin, Gallagher, Emily J., Friedman, Allon N., Guirguis, John, Kapoor, Rajat, Meshberger, Christopher, Kelly, Katherine J., Parikh, Chirag R., Garibaldi, Brian T., Corona-Villalobos, Celia P., Wen, Yumeng, Menez, Steven, Malik, Rubab F., Elena Cervantes, Carmen, Gautam, Samir C., Mallappallil, Mary C., Ouyang, Jie, John, Sabu, Yap, Ernie, Melaku, Yohannes, Mohamed, Ibrahim, Bajracharya, Siddhartha, Puri, Isha, Thaxton, Mariah, Bhattacharya, Jyotsna, Wagner, John, Boudourakis, Leon, Bryant Nguyen, H., Ahoubim, Afshin, Thomas, Leslie F., Sirganagari, Dheeraj Reddy, Guru, Pramod K., Kashani, Kianoush, Tehranian, Shahrzad, Zhou, Yan, Bergl, Paul A., Rodriguez, Jesus, Shah, Jatan A., Gupta, Mrigank S., Kumar, Princy N., Lazarous, Deepa G., Kassaye, Seble G., Melamed, Michal L., Johns, Tanya S., Mocerino, Ryan, Prudhvi, Kalyan, Zhu, Denzel, Levy, Rebecca V., Azzi, Yorg, Fisher, Molly, Yunes, Milagros, Sedaliu, Kaltrina, Golestaneh, Ladan, Brogan, Maureen, Kumar, Neelja, Chang, Michael, Thakkar, Jyotsana, Raichoudhury, Ritesh, Athreya, Akshay, Farag, Mohamed, Schenck, Edward J., Cho, Soo Jung, Plataki, Maria, Alvarez-Mulett, Sergio L., Gomez-Escobar, Luis G., Pan, Di, Lee, Stefi, Krishnan, Jamuna, Whalen, William, Charytan, David, Macina, Ashley, Chaudhry, Sobaata, Wu, Benjamin, Modersitzki, Frank, Srivastava, Anand, Leidner, Alexander S., Martinez, Carlos, Kruser, Jacqueline M., Wunderink, Richard G., Hodakowski, Alexander J., Velez, Juan Carlos Q., Price-Haywood, Eboni G., Matute-Trochez, Luis A., Hasty, Anna E., Mohamed, Muner MB., Avasare, Rupali S., Zonies, David, Leaf, David E., Gupta, Shruti, Sise, Meghan E., Newman, Erik T., Abu Omar, Samah, Pokharel, Kapil K., Sharma, Shreyak, Singh, Harkarandeep, Correa, Simon, Shaukat, Tanveer, Kamal, Omer, Wang, Wei, Yang, Heather, Boateng, Jeffery O., Lee, Meghan, Strohbehn, Ian A., Li, Jiahua, Mueller, Ariel L., Redfern, Roberta E., Cairl, Nicholas S., Naimy, Gabriel, Abu-Saif, Abeer, Hall, Danyell, Bickley, Laura, Rowan, Chris, Madhani-Lovely, Farah, Cruz, Vivian S., Hess, Kristen M., Jacobs, Alanna L., Peev, Vasil, Reiser, Jochen, Byun, John J., Vissing, Andrew, Kapania, Esha M., Post, Zoe, Patel, Nilam P., Hermes, Joy-Marie, Sutherland, Anne K., Patrawalla, Amee, Finkel, Diana G., Danek, Barbara A., Arikapudi, Sowminya, Paer, Jeffrey M., Cangialosi, Peter, Liotta, Mark, Radbel, Jared, Puri, Sonika, Sunderram, Jag, Scharf, Matthew T., Ahmed, Ayesha, Berim, Ilya, Vatson, Jayanth S., Anand, Shuchi, Levitt, Joseph E., Garcia, Pablo, Boyle, Suzanne M., Song, Rui, Zhang, Jingjing, Hoon Woo, Sang, Deng, Xiaoying, Katz-Greenberg, Goni, Senter, Katharine, Sharshir, Moh’d A., Rusnak, Vadym V., Ali, Muhammad Imran, Bansal, Anip, Podoll, Amber S., Chonchol, Michel, Sharma, Sunita, Burnham, Ellen L., Douin, David J., Rashidi, Arash, Hejal, Rana, Judd, Eric, Latta, Laura, Tolwani, Ashita, Albertson, Timothy E., Adams, Jason Y., Chang, Steven Y., Beutler, Rebecca M., Schulze, Carl E., Macedo, Etienne, Rhee, Harin, Liu, Kathleen D., Jotwani, Vasantha K., Koyner, Jay L., Shah, Chintan V., Jaikaransingh, Vishal, Toth-Manikowski, Stephanie M., Joo, Min J., Lash, James P., Neyra, Javier A., Chaaban, Nourhan, Elias, Madona, Ahmad, Yahya, Iardino, Alfredo, Au, Elizabeth H., Sharma, Jill H., Anne Sosa, Marie, Taldone, Sabrina, Contreras, Gabriel, De La Zerda, David, Gershengorn, Hayley B., Shukla, Bhavarth, Fornoni, Alessia, Ferreira, Tanira, Hayek, Salim S., Blakely, Pennelope, Berlin, Hanna, Azam, Tariq U., Shadid, Husam, Pan, Michael, O’ Hayer, Patrick, Meloche, Chelsea, Feroze, Rafey, Kaakati, Rayan, Perry, Danny, Bitar, Abbas, Anderson, Elizabeth, Padalia, Kishan J., Donnelly, John P., Admon, Andrew J., Flythe, Jennifer E., Tugman, Matthew J., Chang, Emily H., Brown, Brent R., Leonberg-Yoo, Amanda K., Spiardi, Ryan C., Miano, Todd A., Roche, Meaghan S., Vasquez, Charles R., Bansal, Amar D., Ernecoff, Natalie C., Kapoor, Sanjana, Verma, Siddharth, Chen, Huiwen, Kovesdy, Csaba P., Molnar, Miklos Z., Azhar, Ambreen, Susan Hedayati, S., Nadamuni, Mridula V., Shastri, Shani, Willett, Duwayne L., Short, Samuel A.P., Renaghan, Amanda D., Enfield, Kyle B., Bhatraju, Pavan K., Bilal Malik, A., Semler, Matthew W., Vijayan, Anitha, Mariyam Joy, Christina, Li, Tingting, Goldberg, Seth, Kao, Patricia F., Schumaker, Greg L., Goyal, Nitender, Faugno, Anthony J., Schumaker, Greg L., Hsu, Caroline M., Tariq, Asma, Meyer, Leah, Kshirsagar, Ravi K., Weiner, Daniel E., Jose, Aju, Christov, Marta, Griffiths, Jennifer, Gupta, Sanjeev, Kapoor, Aromma, Wilson, Perry, Arora, Tanima, and Ugwuowo, Ugochukwu

Published
2022
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32. Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City.

Author

Takkavatakarn, Kullaya, Dai, Yang, Hsun Wen, Huei, Kauffman, Justin, Charney, Alexander, Coca, Steven G., Nadkarni, Girish N., and Chan, Lili

Subjects
EDUCATIONAL attainment, MACHINE learning, AMERICAN Community Survey, CARDIOVASCULAR diseases, ETHNICITY, ZIP codes
Abstract

Background: Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown. Methods: This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment. Results: A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003). Conclusion: The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019

Author

Churpek, Matthew M., Gupta, Shruti, Spicer, Alexandra B., Hayek, Salim S., Srivastava, Anand, Chan, Lili, Melamed, Michal L., Brenner, Samantha K., Radbel, Jared, Madhani-Lovely, Farah, Bhatraju, Pavan K., Bansal, Anip, Green, Adam, Goyal, Nitender, Shaefi, Shahzad, Parikh, Chirag R., Semler, Matthew W., Leaf, David E., Walther, Carol P., Anumudu, Samaya J., Arunthamakun, Justin, Kopecky, Kathleen F., Milligan, Gregory P., McCullough, Peter A., Nguyen, ThuyDuyen, Shaefi, Shahzad, Krajewski, Megan L., Shankar, Sidharth, Pannu, Ameeka, Valencia, Juan D., Waikar, Sushrut S., Kibbelaar, Zoe A., Athavale, Ambarish M., Hart, Peter, Ajiboye, Oyintayo, Itteera, Matthew, Green, Adam, Rachoin, Jean-Sebastien, Schorr, Christa A., Shea, Lisa, Edmonston, Daniel L., Mosher, Christopher L., Shehata, Alexandre M., Cohen, Zaza, Allusson, Valerie, Bambrick-Santoyo, Gabriela, Bhatti, Noor ul aain, Metha, Bijal, Williams, Aquino, Brenner, Samantha K., Walters, Patricia, Go, Ronaldo C., Rose, Keith M., Hernán, Miguel A., Zhou, Amy M., Kim, Ethan C., Lisk, Rebecca, Chan, Lili, Mathews, Kusum S., Coca, Steven G., Altman, Deena R., Saha, Aparna, Soh, Howard, Wen, Huei Hsun, Bose, Sonali, Leven, Emily, Wang, Jing G., Mosoyan, Gohar, Nadkarni, Girish N., Friedman, Allon N., Guirguis, John, Kapoor, Rajat, Meshberger, Christopher, Parikh, Chirag R., Garibaldi, Brian T., Corona-Villalobos, Celia P., Wen, Yumeng, Menez, Steven, Malik, Rubab F., Cervantes, Carmen Elena, Gautam, Samir C., Chang, Crystal, Nguyen, H. Bryant, Ahoubim, Afshin, Thomas, Leslie F., Guru, Pramod K., Bergl, Paul A., Zhou, Yan, Rodriguez, Jesus, Shah, Jatan A., Gupta, Mrigank S., Kumar, Princy N., Lazarous, Deepa G., Kassaye, Seble G., Melamed, Michal L., Johns, Tanya S., Mocerino, Ryan, Prudhvi, Kalyan, Zhu, Denzel, Levy, Rebecca V., Azzi, Yorg, Fisher, Molly, Yunes, Milagros, Sedaliu, Kaltrina, Golestaneh, Ladan, Brogan, Maureen, Thakkar, Jyotsana, Kumar, Neelja, Ross, Michael J., Chang, Michael, Raichoudhury, Ritesh, Schenck, Edward J., Cho, Soo Jung, Plataki, Maria, Alvarez-Mulett, Sergio L., Gomez-Escobar, Luis G., Pan, Di, Lee, Stefi, Krishnan, Jamuna, Whalen, William, Charytan, David, Macina, Ashley, Ross, Daniel W., Srivastava, Anand, Leidner, Alexander S., Martinez, Carlos, Kruser, Jacqueline M., Wunderink, Richard G., Hodakowski, Alexander J., Velez, Juan Carlos Q., Price-Haywood, Eboni G., Matute-Trochez, Luis A., Hasty, Anna E., Mohamed, Muner MB., Avasare, Rupali S., Zonies, David, Leaf, David E., Gupta, Shruti, Baron, Rebecca M., Sise, Meghan E., Newman, Erik T., Omar, Samah Abu, Pokharel, Kapil K., Sharma, Shreyak, Singh, Harkarandeep, Gaviria, Simon Correa, Shaukat, Tanveer, Kamal, Omer, Wang, Wei, Yang, Heather, Boateng, Jeffery O., Lee, Meghan, Strohbehn, Ian A., Li, Jiahua, Muhsin, Saif A., Mandel, Ernest I., Mueller, Ariel L., Cairl, Nicholas S., Madhani-Lovely, Farah, Rowan, Chris, Madhai-Lovely, Farah, Peev, Vasil, Reiser, Jochen, Byun, John J., Vissing, Andrew, Kapania, Esha M., Post, Zoe, Patel, Nilam P., Hermes, Joy-Marie, Sutherland, Anne K., Patrawalla, Amee, Finkel, Diana G., Danek, Barbara A., Arikapudi, Sowminya, Paer, Jeffrey M., Radbel, Jared, Puri, Sonika, Sunderram, Jag, Scharf, Matthew T., Ahmed, Ayesha, Berim, Ilya, Vatson, Jayanth, Anand, Shuchi, Levitt, Joseph E., Garcia, Pablo, Boyle, Suzanne M., Song, Rui, Zhang, Jingjing, Sharshir, Moh’d A., Rusnak, Vadym V., Bansal, Anip, Podoll, Amber S., Chonchol, Michel, Sharma, Sunita, Burnham, Ellen L., Rashidi, Arash, Hejal, Rana, Judd, Eric, Latta, Laura, Tolwani, Ashita, Albertson, Timothy E., Adams, Jason Y., Chang, Steven Y., Beutler, Rebecca M., Schulze, Carl E., Macedo, Etienne, Rhee, Harin, Liu, Kathleen D., Jotwani, Vasantha K., Koyner, Jay L., Shah, Chintan V., Jaikaransingh, Vishal, Toth-Manikowski, Stephanie M., Joo, Min J., Lash, James P., Neyra, Javier A., Chaaban, Nourhan, Iardino, Alfredo, Au, Elizabeth H., Sharma, Jill H., Anne Sosa, Marie, Taldone, Sabrina, Contreras, Gabriel, De La Zerda, David, Gershengorn, Hayley B., Hayek, Salim S., Blakely, Pennelope, Berlin, Hanna, Azam, Tariq U., Shadid, Husam, Pan, Michael, Hayer, Patrick O’, Meloche, Chelsea, Feroze, Rafey, Padalia, Kishan J., Leya, Jeff, Donnelly, John P., Admon, Andrew J., Flythe, Jennifer E., Tugman, Matthew J., Brown, Brent R., Leonberg-Yoo, Amanda K., Spiardi, Ryan C., Miano, Todd A., Roche, Meaghan S., Vasquez, Charles R., Bansal, Amar D., Ernecoff, Natalie C., Kovesdy, Csaba P., Molnar, Miklos Z., Hedayati, S. Susan, Nadamuni, Mridula V., Khan, Sadaf S., Willett, Duwayne L., Short, Samuel A.P., Renaghan, Amanda D., Bhatraju, Pavan, Malik, A. Bilal, Semler, Matthew W., Vijayan, Anitha, Mariyam Joy, Christina, Li, Tingting, Goldberg, Seth, Kao, Patricia F., Schumaker, Greg L., Goyal, Nitender, Faugno, Anthony J., Schumaker, Greg L., Hsu, Caroline M., Tariq, Asma, Meyer, Leah, Christov, Marta, Wilson, Francis P., Arora, Tanima, and Ugwuowo, Ugochukwu

Published
2021
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36. Recipient APOL1 risk alleles associate with death-censored renal allograft survival and rejection episodes

Author

Zhang, Zhongyang, Sun, Zeguo, Fu, Jia, Lin, Qisheng, Banu, Khadija, Chauhan, Kinsuk, Planoutene, Marina, Wei, Chengguo, Salem, Fadi, Yi, Zhengzi, Liu, Ruijie, Cravedi, Paolo, Cheng, Haoxiang, Hao, Ke, O'Connell, Philip J., Ishibe, Shuta, Zhang, Weijia, Coca, Steven G., Gibson, Ian W., Colvin, Robert B., He, John Cijiang, Heeger, Peter S., Murphy, Barbara, and Menon, Madhav C.

Subjects
Kidneys -- Transplantation, Graft rejection -- Genetic aspects -- Risk factors, Apolipoproteins -- Genetic aspects -- Health aspects, Allelomorphism -- Health aspects, Organ transplant recipients -- Genetic aspects, Health care industry
Abstract

Apolipoprotein L1 (APOL1) risk alleles in donor kidneys associate with graft loss, but whether recipient risk allele expression affects transplant outcomes is unclear. To test whether recipient APOL1 risk alleles independently correlate with transplant outcomes, we analyzed genome-wide SNP genotyping data on donors and recipients from 2 kidney transplant cohorts: Genomics of Chronic Allograft Rejection (GOCAR) and Clinical Trials in Organ Transplantation 01/17 (CTOT-01/17). We estimated genetic ancestry (quantified as the proportion of African ancestry, or pAFR) by ADMIXTURE and correlated APOL1 genotypes and pAFR with outcomes. In the GOCAR discovery set, we noted that the number of recipient APOL1 G1/G2 alleles (R-nAPOL1) associated with an increased risk of death-censored allograft loss (DCAL), independent of ancestry (HR = 2.14; P = 0.006), as well as within the subgroup of African American and Hispanic (AA/H) recipients (HR = 2.36; P = 0.003). R-nAPOL1 also associated with an increased risk of any T cell-mediated rejection (TCMR) event. These associations were validated in CTOT-01/17. Ex vivo studies of PMBCs revealed, unexpectedly, high expression levels of APOL1 in activated [CD4.sup.+]/[CD8.sup.+] T cells and NK cells. We detected enriched immune response gene pathways in risk allele carriers compared with noncarriers on the kidney transplant waitlist and among healthy controls. Our findings demonstrate an immunomodulatory role for recipient APOL1 risk alleles associated with TCMR and DCAL. We believe this finding has broader implications for immune-mediated injury to native kidneys., Introduction Patients with African ancestry have a significantly higher risk of non-Mendelian focal segmental glomerulosclerosis (FSGS) as well as end-stage renal disease (ESRD) (reviewed in ref. 1). Seminal work identified [...]

Published
2021
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39. A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease.

Author

Tokita, Joji, Lam, David, Vega, Aida, Wang, Stephanie, Amoruso, Leonard, Muller, Tamara, Naik, Nidhi, Rathi, Shivani, Martin, Sharlene, Zabetian, Azadeh, Liu, Catherine, Sinfield, Catherine, McNicholas, Tony, Fleming, Fergus, Coca, Steven G., Nadkarni, Girish N, Tun, Roger, Kattan, Mike, Donovan, Michael J., and Rahim, Arshad K.

Subjects
BIOMARKERS, IN vitro studies, KIDNEYS, CONFIDENCE intervals, MACHINE learning, TYPE 2 diabetes, PRE-tests & post-tests, T-test (Statistics), RESEARCH funding, ODDS ratio, EARLY diagnosis, DIABETIC nephropathies
Abstract

Introduction/Objective: The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient's risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. Methods: The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. Results: A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group (P <.001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from −7.08 ml/min/1.73 m2/year to −4.27 ml/min/1.73 m2/year in high-risk patients (P =.0003), −2.65 to −1.04 in intermediate risk, and −3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk (P <.001). Conclusions: Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression.

Author

Nadkarni, Girish N., Stapleton, Sharon, Takale, Dipti, Edwards, Katherine, Moran, Kara, Mosoyan, Gohar, Hansen, Michael K., Donovan, Michael J., Heerspink, Hiddo J. L., Fleming, Fergus, and Coca, Steven G.

Subjects
DIABETIC nephropathies, PROGNOSTIC tests, CHRONIC kidney failure, DISEASE progression, GLOMERULAR filtration rate, RANDOM forest algorithms
Abstract

Aims: To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway. Methods: We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (BioMe and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a ≥40% sustained decline in estimated glomerular filtration rate or end‐stage kidney disease within 5 years of follow‐up. Results: In 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 BioMe participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut‐offs, 57%, 35% and 8% of BioMe participants, and 56%, 38% and 6% of CANVAS participants were classified as having low‐, moderate‐ and high‐risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in BioMe and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0‐19.6) and 3.7 (95% CI 2.0‐6.8) in BioMe, and 5.4 (95% CI 2.5‐11.9) and 2.3 (95% CI 1.4‐3.9) in CANVAS, for high‐ versus low‐risk and moderate‐ versus low‐risk levels, respectively. Conclusions: Using two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Biomarkers of inflammation and repair in kidney disease progression

Author

Puthumana, Jeremy, Thiessen-Philbrook, Heather, Xu, Leyuan, Coca, Steven G., Garg, Amit X., Himmelfarb, Jonathan, Bhatraju, Pavan K., Ikizler, T. Alp, Siew, Edward D., Ware, Lorraine B., Liu, Kathleen D., Go, Alan S., Kaufman, James S., Kimmel, Paul L., Chinchilli, Vernon M., Cantley, Lloyd G., and Parikh, Chirag R.

Subjects
Acute phase reaction -- Physiological aspects, Acute renal failure -- Development and progression -- Prognosis -- Models, Glomerular filtration rate -- Health aspects, Biological markers -- Physiological aspects -- Health aspects, Health care industry
Abstract

INTRODUCTION. Acute kidney injury and chronic kidney disease (CKD) are common in hospitalized patients. To inform clinical decision making, more accurate information regarding risk of long-term progression to kidney failure is required. METHODS. We enrolled 1538 hospitalized patients in a multicenter, prospective cohort study. Monocyte chemoattractant protein 1 (MCP-1/CCL2), uromodulin (UMOD), and YKL-40 (CHI3L1) were measured in urine samples collected during outpatient follow-up at 3 months. We followed patients for a median of 4.3 years and assessed the relationship between biomarker levels and changes in estimated glomerular filtration rate (eGFR) over time and the development of a composite kidney outcome (CKD incidence, CKD progression, or end-stage renal disease). We paired these clinical studies with investigations in mouse models of renal atrophy and renal repair to further understand the molecular basis of these markers in kidney disease progression. RESULTS. Higher MCP-1 and YKL-40 levels were associated with greater eGFR decline and increased incidence of the composite renal outcome, whereas higher UMOD levels were associated with smaller eGFR declines and decreased incidence of the composite kidney outcome. A multimarker score increased prognostic accuracy and reclassification compared with traditional clinical variables alone. The mouse model of renal atrophy showed greater Ccl2 and Chi3l1 mRNA expression in infiltrating macrophages and neutrophils, respectively, and evidence of progressive renal fibrosis compared with the repair model. The repair model showed greater Umod expression in the loop of Henle and correspondingly less fibrosis. CONCLUSIONS. Biomarker levels at 3 months after hospitalization identify patients at risk for kidney disease progression. FUNDING. NIH., Introduction Acute kidney injury (AKI) is a complex disorder associated with increased risk of morbidity and mortality. AKI has long been believed to be a fully reversible condition. However, recent [...]

Published
2021
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46. Urine Biomarkers and Perioperative Acute Kidney Injury: The Impact of Preoperative Estimated GFR

Author

Raman, Jai, Jeevanandam, Valluvan, Akhter, Shahab, Devarajan, Prasad, Bennett, Michael, Ma, Qing, Griffiths, Rachel, Edelstein, Charles, Passik, Cary, Nagy, Judy, Swaminathan, Madhav, Chu, Michael, Goldbach, Martin, Guo, Lin Ruo, McKenzie, Neil, Myers, Mary Lee, Novick, Richard, Quantz, Mac, Schumann, Virginia, Webster, Laura, Zappitelli, Michael, Palijan, Ana, Dewar, Michael, Darr, Umer, Hashim, Sabet, Elefteriades, John, Geirsson, Arnar, Garwood, Susan, Kemp, Rowena, Butrymowicz, Isabel, Koyner, Jay L., Coca, Steven G., Thiessen-Philbrook, Heather, Patel, Uptal D., Shlipak, Michael G., Garg, Amit X., and Parikh, Chirag R.

Published
2015
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47. A protective role for microRNA-688 in acute kidney injury

Author

Chun, Nicholas, Coca, Steven G., and He, John Cijiang

Subjects
Acute kidney failure -- Care and treatment, MicroRNA -- Research, Reperfusion injury -- Care and treatment, Cell death, Ischemia, Medical schools, Biochemistry, Health care industry
Abstract

Ischemia-reperfusion (I/R) sets off a devastating cascade of events, leading to cell death and possible organ failure. Treatments to limit I/R-associated damage are lacking, and the pathways that drive injury are poorly understood. In this issue of the JCI, Wei and colleagues identify microRNA-668 (miR-668) as a protective factor in acute kidney injury (AKI). miR-668 was shown to repress mitochondrial fission-associated protein MTP18, thereby inhibiting pathogenic mitochondrial fragmentation. In murine models of I/R-induced AKI, treatment with a miR-668 mimetic reduced mitochondrial fragmentation and improved renal function. Moreover, HIF-1[alpha] was shown to be required for miR-688 expression in response to I/R. Importantly, Wei et al. show miR-668 upregulation in a cohort of human patients with AKI. Together, these results identify a HIF-1[alpha]/miR-668/MTP18 axis that may have potential as a therapeutic target for AKI., Ischemia-reperfusion and acute kidney injuryIschemia-reperfusion (I/R) injury occurs broadly across organ systems, causing substantial human disease; however, effective treatments are lacking. Clinically, I/R manifests as heart attack, stroke, ischemic hepatitis/bowel, [...]

Published
2018
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