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1. A Stochastic Model-Based Control Methodology for Glycemic Management in the Intensive Care Unit

Author

Sirlanci, Melike, Hripcsak, George, Wang, Cecilia C. Low, Stroh, J. N., Wang, Yanran, Bennett, Tellen D., Stuart, Andrew M., and Albers, David J.

Subjects
Mathematics - Optimization and Control, I.6.3
Abstract

Intensive care unit (ICU) patients exhibit erratic blood glucose (BG) fluctuations, including hypoglycemic and hyperglycemic episodes, and require exogenous insulin delivery to keep their BG in healthy ranges. Glycemic control via glycemic management (GM) is associated with reduced mortality and morbidity in the ICU, but GM increases the cognitive load on clinicians. The availability of robust, accurate, and actionable clinical decision support (CDS) tools reduces this burden and assists in the decision-making process to improve health outcomes. Clinicians currently follow GM protocol flow charts for patient intravenous insulin delivery rate computations. We present a mechanistic model-based control algorithm that predicts the optimal intravenous insulin rate to keep BG within a target range; the goal is to develop this approach for eventual use within CDS systems. In this control framework, we employed a stochastic model representing BG dynamics in the ICU setting and used the linear quadratic Gaussian control methodology to develop a controller. We designed two experiments, one using virtual (simulated) patients and one using a real-world retrospective dataset. Using these, we evaluate the safety and efficacy of this model-based glycemic control methodology. The presented controller avoids hypoglycemia and hyperglycemia in virtual patients, maintaining BG levels in the target range more consistently than two existing GM protocols. Moreover, this methodology could theoretically prevent a large proportion of hypoglycemic and hyperglycemic events recorded in a real-world retrospective dataset., Comment: 26 pages, 4 figures, 5 tables

Published
2024

2. An open source knowledge graph ecosystem for the life sciences

Author

Callahan, Tiffany J, Tripodi, Ignacio J, Stefanski, Adrianne L, Cappelletti, Luca, Taneja, Sanya B, Wyrwa, Jordan M, Casiraghi, Elena, Matentzoglu, Nicolas A, Reese, Justin, Silverstein, Jonathan C, Hoyt, Charles Tapley, Boyce, Richard D, Malec, Scott A, Unni, Deepak R, Joachimiak, Marcin P, Robinson, Peter N, Mungall, Christopher J, Cavalleri, Emanuele, Fontana, Tommaso, Valentini, Giorgio, Mesiti, Marco, Gillenwater, Lucas A, Santangelo, Brook, Vasilevsky, Nicole A, Hoehndorf, Robert, Bennett, Tellen D, Ryan, Patrick B, Hripcsak, George, Kahn, Michael G, Bada, Michael, Baumgartner, William A, and Hunter, Lawrence E

Subjects
Information and Computing Sciences, Artificial Intelligence, Bioengineering, Data Science, Networking and Information Technology R&D (NITRD), Algorithms, Biological Science Disciplines, Pattern Recognition, Automated, Translational Research, Biomedical, Knowledge Bases
Abstract

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Published
2024

5. An Open-Source Knowledge Graph Ecosystem for the Life Sciences

Author

Callahan, Tiffany J., Tripodi, Ignacio J., Stefanski, Adrianne L., Cappelletti, Luca, Taneja, Sanya B., Wyrwa, Jordan M., Casiraghi, Elena, Matentzoglu, Nicolas A., Reese, Justin, Silverstein, Jonathan C., Hoyt, Charles Tapley, Boyce, Richard D., Malec, Scott A., Unni, Deepak R., Joachimiak, Marcin P., Robinson, Peter N., Mungall, Christopher J., Cavalleri, Emanuele, Fontana, Tommaso, Valentini, Giorgio, Mesiti, Marco, Gillenwater, Lucas A., Santangelo, Brook, Vasilevsky, Nicole A., Hoehndorf, Robert, Bennett, Tellen D., Ryan, Patrick B., Hripcsak, George, Kahn, Michael G., Bada, Michael, Baumgartner Jr, William A., and Hunter, Lawrence E.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Finance, and Science
Abstract

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Published
2023

6. Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality

Author

Callahan, Tiffany J., Stefanski, Adrianne L., Wyrwa, Jordan M., Zeng, Chenjie, Ostropolets, Anna, Banda, Juan M., Baumgartner Jr., William A., Boyce, Richard D., Casiraghi, Elena, Coleman, Ben D., Collins, Janine H., Deakyne-Davies, Sara J., Feinstein, James A., Haendel, Melissa A., Lin, Asiyah Y., Martin, Blake, Matentzoglu, Nicolas A., Meeker, Daniella, Reese, Justin, Sinclair, Jessica, Taneja, Sanya B., Trinkley, Katy E., Vasilevsky, Nicole A., Williams, Andrew, Zhang, Xingman A., Denny, Joshua C., Robinson, Peter N., Ryan, Patrick, Hripcsak, George, Bennett, Tellen D., Hunter, Lawrence E., and Kahn, Michael G.

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence, J.3
Abstract

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping., Comment: Supplementary Material is included at the end of the manuscript

Published
2022

8. The prevalence of postacute sequelae of coronavirus disease 2019 in solid organ transplant recipients: Evaluation of risk in the National COVID Cohort Collaborative

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred Jerrod, Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Vinson, Amanda J., Schissel, Makayla, Anzalone, Alfred J., Dai, Ran, French, Evan T., Olex, Amy L., Lee, Stephen B., Ison, Michael, and Mannon, Roslyn B.

Published
2024
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9. From Alpha to Omicron and Beyond: Associations Between SARS-CoV-2 Variants and Surgical Outcomes

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred, Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Verhagen, Nathaniel B., Geissler, Thomas, SenthilKumar, Gopika, Gehl, Carson, Shaik, Tahseen, Flitcroft, Madelyn A., Yang, Xin, Taylor, Bradley W., Ghaferi, Amir A., Gould, Jon C., and Kothari, Anai N.

Published
2024
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10. Real-world use of nirmatrelvir-ritonavir in COVID-19 outpatients during BQ.1, BQ.1.1., and XBB.1.5 predominant omicron variants in three U.S. health systems: a retrospective cohort study

Author

Aggarwal, Neil R., Beaty, Laurel E., Bennett, Tellen D., Fish, Lindsey E., Jacobs, Jason R., Mayer, David A., Molina, Kyle C., Peers, Jennifer L., Richardson, Douglas B., Russell, Seth, Varela, Alejandro, Webb, Brandon J., Wynia, Matthew K., Xiao, Mengli, Carlson, Nichole E., and Ginde, Adit A.

Published
2024
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11. Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study

Author

Hill, Elaine L., Mehta, Hemalkumar B., Sharma, Suchetha, Mane, Klint, Singh, Sharad Kumar, Xie, Catherine, Cathey, Emily, Loomba, Johanna, Russell, Seth, Spratt, Heidi, DeWitt, Peter E., Ammar, Nariman, Madlock-Brown, Charisse, Brown, Donald, McMurry, Julie A., Chute, Christopher G., Haendel, Melissa A., Moffitt, Richard, Pfaff, Emily R., and Bennett, Tellen D.

Published
2023
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12. Ontologizing health systems data at scale: making translational discovery a reality

Author

Callahan, Tiffany J., Stefanski, Adrianne L., Wyrwa, Jordan M., Zeng, Chenjie, Ostropolets, Anna, Banda, Juan M., Baumgartner, Jr., William A., Boyce, Richard D., Casiraghi, Elena, Coleman, Ben D., Collins, Janine H., Deakyne Davies, Sara J., Feinstein, James A., Lin, Asiyah Y., Martin, Blake, Matentzoglu, Nicolas A., Meeker, Daniella, Reese, Justin, Sinclair, Jessica, Taneja, Sanya B., Trinkley, Katy E., Vasilevsky, Nicole A., Williams, Andrew E., Zhang, Xingmin A., Denny, Joshua C., Ryan, Patrick B., Hripcsak, George, Bennett, Tellen D., Haendel, Melissa A., Robinson, Peter N., Hunter, Lawrence E., and Kahn, Michael G.

Published
2023
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15. Decreasing Case Fatality Rates for Patients With Cirrhosis Infected With SARS-CoV-2: A National COVID Cohort Collaborative Study

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Far, Aryana T., Digitale, Jean C., Pletcher, Mark J., and Lai, Jennifer C.

Published
2024
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16. COVID-19 outcomes in persons with hemophilia: results from a US-based national COVID-19 surveillance registry

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J. W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O’Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O’Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Sharathkumar, Anjali, Wendt, Linder, Ortman, Chris, Srinivasan, Ragha, Chute, Christopher G., Chrischilles, Elizabeth, and Takemoto, Clifford M.

Published
2024
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18. The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research

Author

Heneghan, Julia A., Walker, Sarah B., Fawcett, Andrea, Bennett, Tellen D., Dziorny, Adam C., Sanchez-Pinto, L. Nelson, Farris, Reid W. D., Winter, Meredith C., Badke, Colleen, Martin, Blake, Brown, Stephanie R., McCrory, Michael C., Ness-Cochinwala, Manette, Rogerson, Colin, Baloglu, Orkun, Harwayne-Gidansky, Ilana, Hudkins, Matthew R., Kamaleswaran, Rishikesan, Gangadharan, Sandeep, Tripathi, Sandeep, Mendonca, Eneida A., Markovitz, Barry P., Mayampurath, Anoop, and Spaeder, Michael C.

Published
2024
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19. Nonelective coronary artery bypass graft outcomes are adversely impacted by Coronavirus disease 2019 infection, but not altered processes of care: A National COVID Cohort Collaborative and National Surgery Quality Improvement Program analysis

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Grimsley, Emily A., Torikashvili, Johnathan V., Janjua, Haroon M., Read, Meagan D., Kothari, Anai N., Verhagen, Nate B., Pietrobon, Ricardo, Kuo, Paul C., and Rogers, Michael P.

Published
2023
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21. The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

Author

Haendel, Melissa A, Chute, Christopher G, Bennett, Tellen D, Eichmann, David A, Guinney, Justin, Kibbe, Warren A, Payne, Philip RO, Pfaff, Emily R, Robinson, Peter N, Saltz, Joel H, Spratt, Heidi, Suver, Christine, Wilbanks, John, Wilcox, Adam B, Williams, Andrew E, Wu, Chunlei, Blacketer, Clair, Bradford, Robert L, Cimino, James J, Clark, Marshall, Colmenares, Evan W, Francis, Patricia A, Gabriel, Davera, Graves, Alexis, Hemadri, Raju, Hong, Stephanie S, Hripscak, George, Jiao, Dazhi, Klann, Jeffrey G, Kostka, Kristin, Lee, Adam M, Lehmann, Harold P, Lingrey, Lora, Miller, Robert T, Morris, Michele, Murphy, Shawn N, Natarajan, Karthik, Palchuk, Matvey B, Sheikh, Usman, Solbrig, Harold, Visweswaran, Shyam, Walden, Anita, Walters, Kellie M, Weber, Griffin M, Zhang, Xiaohan Tanner, Zhu, Richard L, Amor, Benjamin, Girvin, Andrew T, Manna, Amin, Qureshi, Nabeel, Kurilla, Michael G, Michael, Sam G, Portilla, Lili M, Rutter, Joni L, Austin, Christopher P, and Gersing, Ken R

Subjects
COVID-19, Computer Security, Data Analysis, Data Science, Ethics Committees, Research, Government Regulation, Humans, Information Dissemination, Intersectoral Collaboration, National Institutes of Health (U.S.), United States, N3C Consortium, EHR data, SARS-CoV-2, clinical data model harmonization, collaborative analytics, open science, Information and Computing Sciences, Engineering, Medical and Health Sciences, Medical Informatics
Abstract

ObjectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.ResultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.ConclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Published
2021

22. Distinct clinical phenotypes in paediatric cancer patients with sepsis are associated with different outcomes—an international multicentre retrospective study

Author

Caballero, Marina, Margarit, Adriana, Campos, Roi, Möller, Paula, Serpe, Carmela, Amigoni, Angela, Damps, Maria, Montaguti, Alessia, Tardini, Giacomo, Bubeck-Wardenburg, Juliane, Farris, Reid Farris, Hall, Mark, Chong, Grace, Shah, Sareen, Khemani, Robinder, Stroup, Emily, Wösten-van Asperen, Roelie M., la Roi-Teeuw, Hannah M., van Amstel, Rombout BE., Bos, Lieuwe DJ., Tissing, Wim JE., Jordan, Iolanda, Dohna-Schwake, Christian, Bottari, Gabriella, Pappachan, John, Crazzolara, Roman, Comoretto, Rosanna I., Mizia-Malarz, Agniezka, Moscatelli, Andrea, Sánchez-Martín, María, Willems, Jef, Rogerson, Colin M., Bennett, Tellen D., Luo, Yuan, Atreya, Mihir R., Faustino, E.Vincent S., Geva, Alon, Weiss, Scott L., Schlapbach, Luregn J., and Sanchez-Pinto, L Nelson

Published
2023
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24. ensr: R Package for Simultaneous Selection of Elastic Net Tuning Parameters

Author

DeWitt, Peter E. and Bennett, Tellen D.

Subjects
Statistics - Computation
Abstract

Motivation: Elastic net regression is a form of penalized regression that lies between ridge and least absolute shrinkage and selection operator (LASSO) regression. The elastic net penalty is a powerful tool controlling the impact of correlated predictors and the overall complexity of generalized linear regression models. The elastic net penalty has two tuning parameters: ${\lambda}$ for the complexity and ${\alpha}$ for the compromise between LASSO and ridge. The R package glmnet provides efficient tools for fitting elastic net models and selecting ${\lambda}$ for a given ${\alpha}.$ However, glmnet does not simultaneously search the ${\lambda} - {\alpha}$ space for the optional elastic net model. Results: We built the R package ensr, elastic net searcher. enser extends the functionality of glment to search the ${\lambda} - {\alpha}$ space and identify an optimal ${\lambda} - {\alpha}$ pair. Availability: ensr is available from the Comprehensive R Archive Network at https://cran.r-project.org/package=ensr

Published
2019

25. Specialized interferon action in COVID-19

Author

Galbraith, Matthew D., Kinning, Kohl T., Sullivan, Kelly D., Araya, Paula, Smith, Keith P., Granrath, Ross E., Shaw, Jessica R., Baxter, Ryan, Jordan, Kimberly R., Russell, Seth, Dzieciatkowska, Monika, Reisz, Julie A., Gamboni, Fabia, Cendali, Francesca, Ghosh, Tusharkanti, Guo, Kejun, Wilson, Cara C., Santiago, Mario L., Monte, Andrew A., Bennett, Tellen D., Hansen, Kirk C., Hsieh, Elena W. Y., D’Alessandro, Angelo, and Espinosa, Joaquin M.

Published
2022

26. Hormone replacement therapy and COVID-19 outcomes in solid organ transplant recipients compared with the general population

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Vinson, Amanda J., Anzalone, Alfred, Schissel, Makayla, Dai, Ran, French, Evan T., Olex, Amy L., and Mannon, Roslyn B.

Published
2023
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29. Risk of severe acute respiratory syndrome coronavirus 2 infection among women with polycystic ovary syndrome

Author

Haendel, Melissa A., Chute, Christopher G., Walden, Anita, Gersing, Kenneth R., Misquitta, Leonie, Burgoon, Penny Wung, Bozzette, Samuel, Deacy, Mariam, Dillon, Christopher, Erwin-Cohen, Rebecca, Garbarini, Nicole, Gordon, Valery, Kurilla, Michael G., Marti, Emily Carlson, Michael, Sam G., Portilla, Lili, Schmitt, Clare, Temple-O'Connor, Meredith, Bennett, Tellen D., Eichmann, David A., Guinney, Justin, Kibbe, Warren A., Liu, Hongfang, Payne, Philip R.O., Pfaff, Emily R., Robinson, Peter N., Saltz, Joel H., Spratt, Heidi, Starren, Justin, Suver, Christine, Wilcox, Adam B., Williams, Andrew E., Wu, Chunlei, Gabriel, Davera, Hong, Stephanie S., Kostka, Kristin, Lehmann, Harold P., Moffitt, Richard A., Morris, Michele, Palchuk, Matvey B., Zhang, Xiaohan Tanner, Zhu, Richard L., Clark, Marshall, Girvin, Andrew T., Lee, Adam M., Miller, Robert T., Walters, Kellie M., Cooper, Will, Francis, Patricia A., Fuentes, Rafael, Graves, Alexis, McMurry, Julie A., Neumann, Andrew J., O'Neil, Shawn T., Sheikh, Usman, Zampino, Elizabeth, Bissell, Mark M., Bradwell, Katie Rebecca, Manna, Amin, Qureshi, Nabeel, Saltz, Mary Morrison, Volz, Andréa M., Bramante, Carolyn, Harper, Jeremy Richard, Hernandez, Wenndy, Koraishy, Farrukh M., Mariona, Federico, Saha, Amit, Vedula, Satyanarayana, Alur-Gupta, Snigdha, Boland, Mary Regina, and Dokras, Anuja

Published
2023
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33. Thirty-Day Mortality and Complication Rates in Total Joint Arthroplasty After a Recent COVID-19 Diagnosis: A Retrospective Cohort in the National COVID Cohort Collaborative (N3C)

Author

Pincavitch, Jami D., Pisquiy, John J., Wen, Sijin, Bryan, Nicole, Ammons, Jeffrey, Makwana, Priyal, Dietz, Matthew J., Abel, Amber, Eicher, Jennifer, Danley, Suzanne, Gabriel, Davera, Kasicky, Kathryn, Levitt, Eli, Patrick, Sharon, Russell, Michael, Mozingo, Casey, Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O’Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O’Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, and Zhang, Xiaohan Tanner

Published
2023
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34. Early Empiric Antibiotic Use in Patients Hospitalized With COVID-19: A Retrospective Cohort Study*

Author

Widere, J. Christian, Davis, Claire Leilani, Loomba, Johanna Jean, Bell, Taison D., Enfield, Kyle B., Barros, Andrew Julio, Amor, Benjamin, Bissell, Mark M., Bradwell, Katie Rebecca, Girvin, Andrew T., Manna, Amin, Qureshi, Nabeel, Chute, Christopher G., Pfaff, Emily R., Gabriel, Davera, Hong, Stephanie S., Kostka, Kristin, Lehmann, Harold P., Moffitt, Richard A., Morris, Michele, Palchuk, Matvey B., Zhang, Xiaohan Tanner, Zhu, Richard L., Austin, Christopher P., Gersing, Kenneth R., Bozzette, Samuel, Deacy, Mariam, Garbarini, Nicole, Kurilla, Michael G., Michael, Sam G., Rutter, Joni L., Temple-O’Connor, Meredith, Haendel, Melissa A., Bennett, Tellen D., Chute, Christopher G., Eichmann, David A., Guinney, Justin, Kibbe, Warren A., Liu, Hongfang, Payne, Philip R.O., Pfaff, Emily R., Robinson, Peter N., Saltz, Joel H., Spratt, Heidi, Starren, Justin, Suver, Christine, Wilcox, Adam B., Williams, Andrew E., Wu, Chunlei, Pfaff, Emily R., Amor, Benjamin, Bissell, Mark M., Clark, Marshall, Girvin, Andrew T., Hong, Stephanie S., Kostka, Kristin, Lee, Adam M., Miller, Robert T., Morris, Michele, Palchuk, Matvey B., Walters, Kellie M., Haendel, Melissa A., Chute, Christopher G., Gersing, Kenneth R., Walden, Anita, Walden, Anita, Chae, Yooree, Cook, Connor, Dest, Alexandra, Dietz, Racquel R., Dillon, Thomas, Francis, Patricia A., Fuentes, Rafael, Graves, Alexis, McMurry, Julie A., Neumann, Andrew J., O’Neil, Shawn T., Sheikh, Usman, Volz, Andréa M., Zampino, Elizabeth, Saltz, Mary Morrison, Suver, Christine, Chute, Christopher G., Haendel, Melissa A., McMurry, Julie A., Volz, Andréa M., Walden, Anita, Bramante, Carolyn, Harper, Jeremy Richard, Hernandez, Wenndy, Koraishy, Farrukh M, Mariona, Federico, Saha, Amit, and Vedula, Satyanarayana

Published
2023
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35. Identifying who has long COVID in the USA: a machine learning approach using N3C data

Author

Bramante, Carolyn, Dorr, David, Morris, Michele, Parker, Ann M, Sidky, Hythem, Gersing, Ken, Hong, Stephanie, Niehaus, Emily, Pfaff, Emily R, Girvin, Andrew T, Bennett, Tellen D, Bhatia, Abhishek, Brooks, Ian M, Deer, Rachel R, Dekermanjian, Jonathan P, Jolley, Sarah Elizabeth, Kahn, Michael G, Kostka, Kristin, McMurry, Julie A, Moffitt, Richard, Walden, Anita, Chute, Christopher G, and Haendel, Melissa A

Published
2022
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37. Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

Author

Zhang, Xingmin Aaron, Yates, Amy, Vasilevsky, Nicole, Gourdine, JP, Callahan, Tiffany J, Carmody, Leigh C, Danis, Daniel, Joachimiak, Marcin P, Ravanmehr, Vida, Pfaff, Emily R, Champion, James, Robasky, Kimberly, Xu, Hao, Fecho, Karamarie, Walton, Nephi A, Zhu, Richard L, Ramsdill, Justin, Mungall, Christopher J, Köhler, Sebastian, Haendel, Melissa A, McDonald, Clement J, Vreeman, Daniel J, Peden, David B, Bennett, Tellen D, Feinstein, James A, Martin, Blake, Stefanski, Adrianne L, Hunter, Lawrence E, Chute, Christopher G, and Robinson, Peter N

Subjects
Health Services and Systems, Health Sciences, Clinical Research, Networking and Information Technology R&D (NITRD), 4.1 Discovery and preclinical testing of markers and technologies, Good Health and Well Being, Health services and systems
Abstract

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

Published
2019

40. The Prevalence of Post-Acute Sequelae of COVID-19 in Solid Organ Transplant Recipients: Evaluation of Risk in the National COVID Cohort Collaborative (N3C)

Author

Vinson, Amanda J., primary, Schissel, Makayla, additional, Anzalone, Alfred J., additional, Dai, Ran, additional, French, Evan T., additional, Olex, Amy L., additional, Lee, Stephen B., additional, Ison, Michael, additional, Mannon, Roslyn B., additional, Wilcox, Adam B., additional, Lee, Adam M., additional, Graves, Alexis, additional, Anzalone, Alfred (Jerrod), additional, Manna, Amin, additional, Saha, Amit, additional, Olex, Amy, additional, Zhou, Andrea, additional, Williams, Andrew E., additional, Southerland, Andrew, additional, Girvin, Andrew T., additional, Walden, Anita, additional, Sharathkumar, Anjali A., additional, Amor, Benjamin, additional, Bates, Benjamin, additional, Hendricks, Brian, additional, Patel, Brijesh, additional, Alexander, Caleb, additional, Bramante, Carolyn, additional, Ward-Caviness, Cavin, additional, Madlock-Brown, Charisse, additional, Suver, Christine, additional, Chute, Christopher, additional, Dillon, Christopher, additional, Wu, Chunlei, additional, Schmitt, Clare, additional, Takemoto, Cliff, additional, Housman, Dan, additional, Gabriel, Davera, additional, Eichmann, David A., additional, Mazzotti, Diego, additional, Brown, Don, additional, Boudreau, Eilis, additional, Hill, Elaine, additional, Zampino, Elizabeth, additional, Marti, Emily Carlson, additional, Pfaff, Emily R., additional, French, Evan, additional, Koraishy, Farrukh M., additional, Mariona, Federico, additional, Prior, Fred, additional, Sokos, George, additional, Martin, Greg, additional, Lehmann, Harold, additional, Spratt, Heidi, additional, Mehta, Hemalkumar, additional, Liu, Hongfang, additional, Sidky, Hythem, additional, Hayanga, J.W. Awori, additional, Pincavitch, Jami, additional, Clark, Jaylyn, additional, Harper, Jeremy Richard, additional, Islam, Jessica, additional, Ge, Jin, additional, Gagnier, Joel, additional, Saltz, Joel H., additional, Saltz, Joel, additional, Loomba, Johanna, additional, Buse, John, additional, Mathew, Jomol, additional, Rutter, Joni L., additional, McMurry, Julie A., additional, Guinney, Justin, additional, Starren, Justin, additional, Crowley, Karen, additional, Bradwell, Katie Rebecca, additional, Walters, Kellie M., additional, Wilkins, Ken, additional, Gersing, Kenneth R., additional, Cato, Kenrick Dwain, additional, Murray, Kimberly, additional, Kostka, Kristin, additional, Northington, Lavance, additional, Pyles, Lee Allan, additional, Misquitta, Leonie, additional, Cottrell, Lesley, additional, Portilla, Lili, additional, Deacy, Mariam, additional, Bissell, Mark M., additional, Clark, Marshall, additional, Emmett, Mary, additional, Saltz, Mary Morrison, additional, Palchuk, Matvey B., additional, Haendel, Melissa A., additional, Adams, Meredith, additional, Temple-O'Connor, Meredith, additional, Kurilla, Michael G., additional, Morris, Michele, additional, Qureshi, Nabeel, additional, Safdar, Nasia, additional, Garbarini, Nicole, additional, Sharafeldin, Noha, additional, Sadan, Ofer, additional, Francis, Patricia A., additional, Burgoon, Penny Wung, additional, Robinson, Peter, additional, Payne, Philip R.O., additional, Fuentes, Rafael, additional, Jawa, Randeep, additional, Erwin-Cohen, Rebecca, additional, Patel, Rena, additional, Moffitt, Richard A., additional, Zhu, Richard L., additional, Kamaleswaran, Rishi, additional, Hurley, Robert, additional, Miller, Robert T., additional, Pyarajan, Saiju, additional, Michael, Sam G., additional, Bozzette, Samuel, additional, Mallipattu, Sandeep, additional, Vedula, Satyanarayana, additional, Chapman, Scott, additional, O'Neil, Shawn T., additional, Setoguchi, Soko, additional, Hong, Stephanie S., additional, Johnson, Steve, additional, Bennett, Tellen D., additional, Callahan, Tiffany, additional, Topaloglu, Umit, additional, Sheikh, Usman, additional, Gordon, Valery, additional, Subbian, Vignesh, additional, Kibbe, Warren A., additional, Hernandez, Wenndy, additional, Beasley, Will, additional, Cooper, Will, additional, Hillegass, William, additional, and Zhang, Xiaohan Tanner, additional

Published
2024
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41. Characterizing Long COVID: Deep Phenotype of a Complex Condition

Author

Deer, Rachel R, Rock, Madeline A, Vasilevsky, Nicole, Carmody, Leigh, Rando, Halie, Anzalone, Alfred J, Basson, Marc D, Bennett, Tellen D, Bergquist, Timothy, Boudreau, Eilis A, Bramante, Carolyn T, Byrd, James Brian, Callahan, Tiffany J, Chan, Lauren E, Chu, Haitao, Chute, Christopher G, Coleman, Ben D, Davis, Hannah E, Gagnier, Joel, Greene, Casey S, Hillegass, William B, Kavuluru, Ramakanth, Kimble, Wesley D, Koraishy, Farrukh M, Köhler, Sebastian, Liang, Chen, Liu, Feifan, Liu, Hongfang, Madhira, Vithal, Madlock-Brown, Charisse R, Matentzoglu, Nicolas, Mazzotti, Diego R, McMurry, Julie A, McNair, Douglas S, Moffitt, Richard A, Monteith, Teshamae S, Parker, Ann M, Perry, Mallory A, Pfaff, Emily, Reese, Justin T, Saltz, Joel, Schuff, Robert A, Solomonides, Anthony E, Solway, Julian, Spratt, Heidi, Stein, Gary S, Sule, Anupam A, Topaloglu, Umit, Vavougios, George D., Wang, Liwei, Haendel, Melissa A, and Robinson, Peter N

Published
2021
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42. Long‐Term Mortality Following SARS‐CoV‐2 Infection in Rural Versus Urban Dwellers With Autoimmune or Inflammatory Rheumatic Disease: A Retrospective Cohort Analysis From the National COVIDCohort Collaborative

Author

Anzalone, A. Jerrod, Jackson, Lesley E., Singh, Namrata, Danila, Maria I., Reisher, Elizabeth, Patel, Rena C., Singh, Jasvinder A., Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward‐Caviness, Cavin, Madlock‐Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M, Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple‐O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin‐Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, and Zhang, Xiaohan Tanner

Abstract

Autoimmune or inflammatory rheumatic diseases (AIRDs) increase the risk for poor COVID‐19 outcomes. Although rurality is associated with higher post–COVID‐19 mortality in the general population, whether rurality elevates this risk among people with AIRD is unknown. We assessed associations between rurality and post–COVID‐19 all‐cause mortality, up to two years post infection, among people with AIRD using a large nationally sampled US cohort. This retrospective study used the National COVID Cohort Collaborative, a medical records repository containing COVID‐19 patient data. We included adults with two or more AIRD diagnostic codes and a COVID‐19 diagnosis documented between April 2020 and March 2023. Rural residency was categorized using patient residential zip codes. We adjusted for AIRD medications and glucocorticoid prescription, age, sex, race and ethnicity, tobacco or substance use, comorbid burden, and SARS‐CoV‐2 variant‐dominant periods. Multivariable Cox proportional hazards with inverse probability treatment weighting assessed associations between rurality and two‐year all‐cause mortality. Among the 86,467 SARS‐CoV‐2–infected persons with AIRD, we observed a higher risk for two‐year post–COVID‐19 mortality in rural versus urban dwellers. Rural‐residing persons with AIRD had higher two‐year all‐cause mortality risk (adjusted hazard ratio 1.24, 95% confidence interval 1.19–1.29). Glucocorticoid, immunosuppressive, and rituximab prescriptions were associated with a higher risk for two‐year post–COVID‐19 mortality, whereas risk with nonbiologic or biologic disease‐modifying antirheumatic drugs was lower. Rural residence in people with AIRD was independently associated with higher two‐year post–COVID‐19 mortality in a large US cohort after adjusting for background risk factors. Policymakers and health care providers should consider these findings when designing interventions to improve outcomes in people with AIRD following SARS‐CoV‐2 infection, especially among high‐risk rural residents.

Published
2025
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43. Associations of County-Level Social Determinants of Health with COVID-19 Related Hospitalization Among People with HIV: A Retrospective Analysis of the U.S. National COVID Cohort Collaborative (N3C).

Author

Islam, Jessica Y., Hurwitz, Eric, Li, Dongmei, Camacho-Rivera, Marlene, Sun, Jing, Safo, Sandra, Ross, Jennifer M., Wilkins, Kenneth, Hassan, Shukri, Hill, Elaine L., Nosyk, Bohdan, Varley, Cara, Fadul, Nada, Madlock-Brown, Charisse, Patel, Rena C., Haendel, Melissa A., Chute, Christopher G., Gersing, Kenneth R., Walden, Anita, and Bennett, Tellen D.

Subjects
HEALTH services accessibility, SOCIAL determinants of health, RESEARCH funding, INTERPROFESSIONAL relations, HOSPITAL care, MULTIPLE regression analysis, HEALTH insurance, RETROSPECTIVE studies, DESCRIPTIVE statistics, MULTIVARIATE analysis, PSYCHOLOGY of HIV-positive persons, RACE, LONGITUDINAL method, MEDICAL records, ACQUISITION of data, CONFIDENCE intervals, COVID-19
Abstract

Individually, the COVID-19 and HIV pandemics have differentially impacted minoritized groups due to the role of social determinants of health (SDoH) in the U.S. Little is known how the collision of these two pandemics may have exacerbated adverse health outcomes. We evaluated county-level SDoH and associations with hospitalization after a COVID-19 diagnosis among people with (PWH) and without HIV (PWOH) by racial/ethnic groups. We used the U.S. National COVID Cohort Collaborative (January 2020-November 2023), a nationally-sampled electronic health record repository, to identify adults who were diagnosed with COVID-19 with HIV (n = 22,491) and without HIV (n = 2,220,660). We aggregated SDoH measures at the county-level and categorized racial/ethnic groups as Non-Hispanic (NH) White, NH-Black, Hispanic/Latinx, NH-Asian and Pacific Islander (AAPI), and NH-American Indian or Alaskan Native (AIAN). To estimate associations of county-level SDoH with hospitalization after a COVID-19 diagnosis, we used multilevel, multivariable logistic regressions, calculating adjusted relative risks (aRR) with 95% confidence intervals (95% CI). COVID-19 related hospitalization occurred among 11% of PWH and 7% of PWOH, with the highest proportion among NH-Black PWH (15%). In evaluating county-level SDoH among PWH, we found higher average household size was associated with lower risk of COVID-19 related hospitalization across racial/ethnic groups. Higher mean commute time (aRR: 1.76; 95% CI 1.10–2.62) and higher proportion of adults without health insurance (aRR: 1.40; 95% CI 1.04–1.84) was associated with a higher risk of COVID-19 hospitalization among NH-Black PWH, however, NH-Black PWOH did not demonstrate these associations. Differences by race and ethnicity exist in associations of adverse county-level SDoH with COVID-19 outcomes among people with and without HIV in the U.S. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age

Author

Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred (Jerrod), Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O’Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O’Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, Zhang, Xiaohan Tanner, Lyu, Tianchu, Liang, Chen, Liu, Jihong, Hung, Peiyin, Zhang, Jiajia, Campbell, Berry, Ghumman, Nadia, Olatosi, Bankole, Hikmet, Neset, Zhang, Manting, Yi, Honggang, and Li, Xiaoming

Published
2023
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49. Data Science for Child Health

Author

Bennett, Tellen D., Callahan, Tiffany J., Feinstein, James A., Ghosh, Debashis, Lakhani, Saquib A., Spaeder, Michael C., Szefler, Stanley J., and Kahn, Michael G.

Published
2019
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50. The prevalence of postacute sequelae of coronavirus disease 2019 in solid organ transplant recipients: Evaluation of risk in the National COVID Cohort Collaborative

Author

Vinson, Amanda J., Schissel, Makayla, Anzalone, Alfred J., Dai, Ran, French, Evan T., Olex, Amy L., Lee, Stephen B., Ison, Michael, Mannon, Roslyn B., Wilcox, Adam B., Lee, Adam M., Graves, Alexis, Anzalone, Alfred Jerrod, Manna, Amin, Saha, Amit, Olex, Amy, Zhou, Andrea, Williams, Andrew E., Southerland, Andrew, Girvin, Andrew T., Walden, Anita, Sharathkumar, Anjali A., Amor, Benjamin, Bates, Benjamin, Hendricks, Brian, Patel, Brijesh, Alexander, Caleb, Bramante, Carolyn, Ward-Caviness, Cavin, Madlock-Brown, Charisse, Suver, Christine, Chute, Christopher, Dillon, Christopher, Wu, Chunlei, Schmitt, Clare, Takemoto, Cliff, Housman, Dan, Gabriel, Davera, Eichmann, David A., Mazzotti, Diego, Brown, Don, Boudreau, Eilis, Hill, Elaine, Zampino, Elizabeth, Marti, Emily Carlson, Pfaff, Emily R., French, Evan, Koraishy, Farrukh M., Mariona, Federico, Prior, Fred, Sokos, George, Martin, Greg, Lehmann, Harold, Spratt, Heidi, Mehta, Hemalkumar, Liu, Hongfang, Sidky, Hythem, Hayanga, J.W. Awori, Pincavitch, Jami, Clark, Jaylyn, Harper, Jeremy Richard, Islam, Jessica, Ge, Jin, Gagnier, Joel, Saltz, Joel H., Saltz, Joel, Loomba, Johanna, Buse, John, Mathew, Jomol, Rutter, Joni L., McMurry, Julie A., Guinney, Justin, Starren, Justin, Crowley, Karen, Bradwell, Katie Rebecca, Walters, Kellie M., Wilkins, Ken, Gersing, Kenneth R., Cato, Kenrick Dwain, Murray, Kimberly, Kostka, Kristin, Northington, Lavance, Pyles, Lee Allan, Misquitta, Leonie, Cottrell, Lesley, Portilla, Lili, Deacy, Mariam, Bissell, Mark M., Clark, Marshall, Emmett, Mary, Saltz, Mary Morrison, Palchuk, Matvey B., Haendel, Melissa A., Adams, Meredith, Temple-O'Connor, Meredith, Kurilla, Michael G., Morris, Michele, Qureshi, Nabeel, Safdar, Nasia, Garbarini, Nicole, Sharafeldin, Noha, Sadan, Ofer, Francis, Patricia A., Burgoon, Penny Wung, Robinson, Peter, Payne, Philip R.O., Fuentes, Rafael, Jawa, Randeep, Erwin-Cohen, Rebecca, Patel, Rena, Moffitt, Richard A., Zhu, Richard L., Kamaleswaran, Rishi, Hurley, Robert, Miller, Robert T., Pyarajan, Saiju, Michael, Sam G., Bozzette, Samuel, Mallipattu, Sandeep, Vedula, Satyanarayana, Chapman, Scott, O'Neil, Shawn T., Setoguchi, Soko, Hong, Stephanie S., Johnson, Steve, Bennett, Tellen D., Callahan, Tiffany, Topaloglu, Umit, Sheikh, Usman, Gordon, Valery, Subbian, Vignesh, Kibbe, Warren A., Hernandez, Wenndy, Beasley, Will, Cooper, Will, Hillegass, William, and Zhang, Xiaohan Tanner

Abstract

Postacute sequelae after the coronavirus disease (COVID) of 2019 (PASC) is increasingly recognized, although data on solid organ transplant (SOT) recipients (SOTRs) are limited. Using the National COVID Cohort Collaborative, we performed 1:1 propensity score matching (PSM) of all adult SOTR and nonimmunosuppressed/immunocompromised (ISC) patients with acute COVID infection (August 1, 2021 to January 13, 2023) for a subsequent PASC diagnosis using International Classification of Diseases, 10th Revision, Clinical Modification codes. Multivariable logistic regression was used to examine not only the association of SOT status with PASC, but also other patient factors after stratifying by SOT status. Prior to PSM, there were 8769 SOT and 1 576 769 non-ISC patients with acute COVID infection. After PSM, 8756 SOTR and 8756 non-ISC patients were included; 2.2% of SOTR (n = 192) and 1.4% (n = 122) of non-ISC patients developed PASC (Pvalue < .001). In the overall matched cohort, SOT was independently associated with PASC (adjusted odds ratio [aOR], 1.48; 95% confidence interval [CI], 1.09-2.01). Among SOTR, COVID infection severity (aOR, 11.6; 95% CI, 3.93-30.0 for severe vs mild disease), older age (aOR, 1.02; 95% CI, 1.01-1.03 per year), and mycophenolate mofetil use (aOR, 2.04; 95% CI, 1.38-3.05) were each independently associated with PASC. In non-ISC patients, only depression (aOR, 1.96; 95% CI, 1.24-3.07) and COVID infection severity were. In conclusion, PASC occurs more commonly in SOTR than in non-ISC patients, with differences in risk profiles based on SOT status.

Published
2024
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