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1. Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort CollaborativeResearch in context

Author

Timothy Bergquist, Johanna Loomba, Emily Pfaff, Fangfang Xia, Zixuan Zhao, Yitan Zhu, Elliot Mitchell, Biplab Bhattacharya, Gaurav Shetty, Tamanna Munia, Grant Delong, Adbul Tariq, Zachary Butzin-Dozier, Yunwen Ji, Haodong Li, Jeremy Coyle, Seraphina Shi, Rachael V. Philips, Andrew Mertens, Romain Pirracchio, Mark van der Laan, John M. Colford, Jr., Alan Hubbard, Jifan Gao, Guanhua Chen, Neelay Velingker, Ziyang Li, Yinjun Wu, Adam Stein, Jiani Huang, Zongyu Dai, Qi Long, Mayur Naik, John Holmes, Danielle Mowery, Eric Wong, Ravi Parekh, Emily Getzen, Jake Hightower, Jennifer Blase, Ataes Aggarwal, Joseph Agor, Amera Al-Amery, Oluwatobiloba Aminu, Adit Anand, Corneliu Antonescu, Mehak Arora, Sayed Asaduzzaman, Tanner Asmussen, Mahdi Baghbanzadeh, Frazier Baker, Bridget Bangert, Laila Bekhet, Jenny Blase, Brian Caffo, Hao Chang, Zeyuan Chen, Jiandong Chen, Jeffrey Chiang, Peter Cho, Robert Cockrell, Parker Combs, Ciara Crosby, Ran Dai, Anseh Danesharasteh, Elif Yildirim, Ryan Demilt, Kaiwen Deng, Sanjoy Dey, Rohan Dhamdhere, Andrew Dickson, Phoebe Dijour, Dong Dinh, Richard Dixon, Albi Domi, Souradeep Dutta, Mirna Elizondo, Zeynep Ertem, Solomon Feuerwerker, Danica Fliss, Jennifer Fowler, Sunyang Fu, Kelly Gardner, Neil Getty, Mohamed Ghalwash, Logan Gloster, Phil Greer, Yuanfang Guan, Colby Ham, Samer Hanoudi, Jeremy Harper, Nathaniel Hendrix, Leeor Hershkovich, Junjie Hu, Yu Huang, Tongtong Huang, Junguk Hur, Monica Isgut, Hamid Ismail, Grant Izmirlian, Kuk Jang, Christianah Jemiyo, Hayoung Jeong, Xiayan Ji, Ming Jiang, Sihang Jiang, Xiaoqian Jiang, Yuye Jiang, Akin Johnson, Zach Analyst, Saarthak Kapse, Uri Kartoun, Dukka KC, Zahra Fard, Tim Kosfeld, Spencer Krichevsky, Mike Kuo, Dale Larie, Lauren Lederer, Shan Leng, Hongyang Li, Jianfu Li, Tiantian Li, Xinwen Liang, Hengyue Liang, Feifan Liu, Daniel Liu, Gang Luo, Ravi Madduri, Vithal Madhira, Shivali Mani, Farzaneh Mansourifard, Robert Matson, Vangelis Metsis, Pablo Meyer, Catherine Mikhailova, Dante Miller, Christopher Milo, Gourav Modanwal, Ronald Moore, David Morgenthaler, Rasim Musal, Vinit Nalawade, Rohan Narain, Saideep Narendrula, Alena Obiri, Satoshi Okawa, Chima Okechukwu, Toluwanimi Olorunnisola, Tim Ossowski, Harsh Parekh, Jean Park, Saaya Patel, Jason Patterson, Chetan Paul, Le Peng, Diana Perkins, Suresh Pokharel, Dmytro Poplavskiy, Zach Pryor, Sarah Pungitore, Hong Qin, Salahaldeen Rababa, Mahbubur Rahman, Elior Rahmani, Gholamali Rahnavard, Md Raihan, Suraj Rajendran, Sarangan Ravichandran, Chandan Reddy, Abel Reyes, Ali Roghanizad, Sean Rouffa, Xiaoyang Ruan, Arpita Saha, Sahil Sawant, Melody Schiaffino, Diego Seira, Saurav Sengupta, Ruslan Shalaev, Linh Shinguyen, Karnika Singh, Soumya Sinha, Damien Socia, Halen Stalians, Charalambos Stavropoulos, Jan Strube, Devika Subramanian, Jiehuan Sun, Ju Sun, Chengkun Sun, Prathic Sundararajan, Salmonn Talebi, Edward Tawiah, Jelena Tesic, Mikaela Thiess, Raymond Tian, Luke Torre-Healy; Ming-Tse Tsai, David Tyus, Madhurima Vardhan, Benjamin Walzer, Jacob Walzer, Junda Wang, Lu Wang, Will Wang, Jonathan Wang, Yisen Wang, Chad Weatherly, Fanyou Wu, Yifeng Wu, Hao Yan, Zhichao Yang, Biao Ye, Rui Yin, Changyu Yin, Yun Yoo, Albert You, June Yu, Martin Zanaj, Zachary Zaiman, Kai Zhang, Xiaoyi Zhang, Tianmai Zhang, Degui Zhi, Yishan Zhong, Huixue Zhou, Andrea Zhou, Yuanda Zhu, Sophie Zhu, Meredith Adams, Caleb Alexander, Benjamin Amor, Alfred Anzalone, Benjamin Bates, Will Beasley, Tellen Bennett, Mark Bissell, Eilis Boudreau, Samuel Bozzette, Katie Bradwell, Carolyn Bramante, Don Brown, Penny Burgoon, John Buse, Tiffany Callahan, Kenrick Cato, Scott Chapman, Christopher Chute, Jaylyn Clark, Marshall Clark, Will Cooper, Lesley Cottrell, Karen Crowley, Mariam Deacy, Christopher Dillon, David Eichmann, Mary Emmett, Rebecca Erwin-Cohen, Patricia Francis, Evan French, Rafael Fuentes, Davera Gabriel, Joel Gagnier, Nicole Garbarini, Jin Ge, Kenneth Gersing, Andrew Girvin, Valery Gordon, Alexis Graves, Justin Guinney, Melissa Haendel, J.W. Hayanga, Brian Hendricks, Wenndy Hernandez, Elaine Hill, William Hillegass, Stephanie Hong, Dan Housman, Robert Hurley, Jessica Islam, Randeep Jawa, Steve Johnson, Rishi Kamaleswaran, Warren Kibbe, Farrukh Koraishy, Kristin Kostka, Michael Kurilla, Adam Lee, Harold Lehmann, Hongfang Liu, Charisse Madlock-Brown; Sandeep Mallipattu, Amin Manna, Federico Mariona, Emily Marti, Greg Martin, Jomol Mathew, Diego Mazzotti, Julie McMurry, Hemalkumar Mehta, Sam Michael, Robert Miller, Leonie Misquitta, Richard Moffitt, Michele Morris, Kimberly Murray, Lavance Northington, Shawn O’Neil, Amy Olex, Matvey Palchuk, Brijesh Patel, Rena Patel, Philip Payne, Jami Pincavitch, Lili Portilla, Fred Prior, Saiju Pyarajan, Lee Pyles, Nabeel Qureshi, Peter Robinson, Joni Rutter, Ofer Sadan, Nasia Safdar, Amit Saha, Joel Saltz, Mary Saltz, Clare Schmitt, Soko Setoguchi, Noha Sharafeldin, Anjali Sharathkumar, Usman Sheikh, Hythem Sidky, George Sokos, Andrew Southerland, Heidi Spratt, Justin Starren, Vignesh Subbian, Christine Suver, Cliff Takemoto, Meredith Temple-O'Connor, Umit Topaloglu, Satyanarayana Vedula, Anita Walden, Kellie Walters, Cavin Ward-Caviness, Adam Wilcox, Ken Wilkins, Andrew Williams, Chunlei Wu, Elizabeth Zampino, Xiaohan Zhang, and Richard Zhu

Subjects
Long COVID, PASC, Machine learning, COVID-19, Evaluation, Community challenge, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Published
2024
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2. 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 analysisCentral MessagePerspective

Author

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

Subjects
nonelective coronary artery bypass grafting, COVID-19, pandemic, outcomes, Diseases of the circulatory (Cardiovascular) system, RC666-701, Surgery, RD1-811
Abstract

Objective: The effects of Coronavirus disease 2019 (COVID-19) infection and altered processes of care on nonelective coronary artery bypass grafting (CABG) outcomes remain unknown. We hypothesized that patients with COVID-19 infection would have longer hospital lengths of stay and greater mortality compared with COVID-negative patients, but that these outcomes would not differ between COVID-negative and pre-COVID controls. Methods: The National COVID Cohort Collaborative 2020-2022 was queried for adult patients undergoing CABG. Patients were divided into COVID-negative, COVID-active, and COVID-convalescent groups. Pre-COVID control patients were drawn from the National Surgical Quality Improvement Program database. Adjusted analysis of the 3 COVID groups was performed via generalized linear models. Results: A total of 17,293 patients underwent nonelective CABG, including 16,252 COVID-negative, 127 COVID-active, 367 COVID-convalescent, and 2254 pre-COVID patients. Compared to pre-COVID patients, COVID-negative patients had no difference in mortality, whereas COVID-active patients experienced increased mortality. Mortality and pneumonia were higher in COVID-active patients compared to COVID-negative and COVID-convalescent patients. Adjusted analysis demonstrated that COVID-active patients had higher in-hospital mortality, 30- and 90-day mortality, and pneumonia compared to COVID-negative patients. COVID-convalescent patients had a shorter length of stay but a higher rate of renal impairment. Conclusions: Traditional care processes were altered during the COVID-19 pandemic. Our data show that nonelective CABG in patients with active COVID-19 is associated with significantly increased rates of mortality and pneumonia. The equivalent mortality in COVID-negative and pre-COVID patients suggests that pandemic-associated changes in processes of care did not impact CABG outcomes. Additional research into optimal timing of CABG after COVID infection is warranted.

Published
2023
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3. Evaluating COVID-19 vaccine effectiveness during pre-Delta, Delta and Omicron dominant periods among pregnant people in the U.S.: Retrospective cohort analysis from a nationally sampled cohort in National COVID Collaborative Cohort (N3C)

Author

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

Subjects
Public aspects of medicine, RA1-1270
Abstract

Objectives To evaluate the effectiveness of COVID-19 vaccinations (initial and booster) during pre-Delta, Delta and Omicron dominant periods among pregnant people via (1) COVID-19 incident and severe infections among pregnant people who were vaccinated versus unvaccinated and (2) post-COVID-19 vaccination breakthrough infections and severe infections among vaccinated females who were pregnant versus non-pregnant.Design Retrospective cohort study using nationally sampled electronic health records data from the National COVID Cohort Collaborative, 10 December 2020 –7 June 2022.Participants Cohort 1 included pregnant people (15–55 years) and cohort 2 included vaccinated females of reproductive age (15–55 years).Exposures (1) COVID-19 vaccination and (2) pregnancy.Main outcome measures Adjusted HRs (aHRs) for COVID-19 incident or breakthrough infections and severe infections (ie, COVID-19 infections with related hospitalisations).Results In cohort 1, 301 107 pregnant people were included. Compared with unvaccinated pregnant people, the aHRs for pregnant people with initial vaccinations during pregnancy of incident COVID-19 were 0.77 (95% CI 0.62 to 0.96) and 0.88 (95% CI 0.73 to 1.07) and aHRs of severe COVID-19 infections were 0.65 (95% CI 0.47 to 0.90) and 0.79 (95% CI 0.51 to 1.21) during the Delta and Omicron periods, respectively. Compared with pregnant people with full initial vaccinations, the aHR of incident COVID-19 for pregnant people with booster vaccinations was 0.64 (95% CI 0.58 to 0.71) during the Omicron period. In cohort 2, 934 337 vaccinated people were included. Compared with vaccinated non-pregnant females, the aHRs of severe COVID-19 infections for people with initial vaccinations during pregnancy was 2.71 (95% CI 1.31 to 5.60) during the Omicron periods.Conclusions Pregnant people with initial and booster vaccinations during pregnancy had a lower risk of incident and severe COVID-19 infections compared with unvaccinated pregnant people across the pandemic stages. However, vaccinated pregnant people still had a higher risk of severe infections compared with non-pregnant females.

Published
2024
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4. Post-recovery COVID-19 and incident heart failure in the National COVID Cohort Collaborative (N3C) study

Author

Husam M. Salah, Marat Fudim, Shawn T. O’Neil, Amin Manna, Christopher G. Chute, and Melissa C. Caughey

Subjects
Science
Abstract

The relationship between post-recovery COVID-19 and incident heart failure has not been investigated at scale. Here, the authors use electronic health records for ~600,000 patients in the US and find a higher rate of post-discharge incident heart failure in those hospitalised with COVID-19 than without.

Published
2022
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6. Risk of severe acute respiratory syndrome coronavirus 2 infection among women with polycystic ovary syndrome

Author

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

Subjects
Reproductive Medicine, Obstetrics and Gynecology
Published
2023

7. Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs

Author

Peter Leese, Adit Anand, Andrew Girvin, Amin Manna, Saaya Patel, Yun Jae Yoo, Rachel Wong, Melissa Haendel, Christopher G Chute, Tellen Bennett, Janos Hajagos, Emily Pfaff, and Richard Moffitt

Subjects
Health Informatics
Abstract

Objective Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite “macrovisits.” Materials and Methods Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits. Results Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. Discussion Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data. Conclusion This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis.

Published
2023

8. Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset

Author

Katie R, Bradwell, Jacob T, Wooldridge, Benjamin, Amor, Tellen D, Bennett, Adit, Anand, Carolyn, Bremer, Yun Jae, Yoo, Zhenglong, Qian, Steven G, Johnson, Emily R, Pfaff, Andrew T, Girvin, Amin, Manna, Emily A, Niehaus, Stephanie S, Hong, Xiaohan Tanner, Zhang, Richard L, Zhu, Mark, Bissell, Nabeel, Qureshi, Joel, Saltz, Melissa A, Haendel, Christopher G, Chute, Harold P, Lehmann, and Richard A, Moffitt

Subjects
Cohort Studies, Data Collection, COVID-19, Electronic Health Records, Humans, Health Informatics
Abstract

Objective The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. Materials and Methods The National COVID Cohort Collaborative (N3C) table of laboratory measurement data—over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. Results Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors’ records lacked units). Discussion The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. Conclusion The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.

Published
2022

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

Author

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

Subjects
Obstetrics and Gynecology
Published
2023

10. Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative

Author

Emily R, Pfaff, Andrew T, Girvin, Davera L, Gabriel, Kristin, Kostka, Michele, Morris, Matvey B, Palchuk, Harold P, Lehmann, Benjamin, Amor, Mark, Bissell, Katie R, Bradwell, Sigfried, Gold, Stephanie S, Hong, Johanna, Loomba, Amin, Manna, Julie A, McMurry, Emily, Niehaus, Nabeel, Qureshi, Anita, Walden, Xiaohan Tanner, Zhang, Richard L, Zhu, Richard A, Moffitt, Melissa A, Haendel, Christopher G, Chute, William G, Adams, Shaymaa, Al-Shukri, Alfred, Anzalone, Ahmad, Baghal, Tellen D, Bennett, Elmer V, Bernstam, Mark M, Bissell, Brian, Bush, Thomas R, Campion, Victor, Castro, Jack, Chang, Deepa D, Chaudhari, Wenjin, Chen, San, Chu, James J, Cimino, Keith A, Crandall, Mark, Crooks, Sara J Deakyne, Davies, John, DiPalazzo, David, Dorr, Dan, Eckrich, Sarah E, Eltinge, Daniel G, Fort, George, Golovko, Snehil, Gupta, Janos G, Hajagos, David A, Hanauer, Brett M, Harnett, Ronald, Horswell, Nancy, Huang, Steven G, Johnson, Michael, Kahn, Kamil, Khanipov, Curtis, Kieler, Katherine Ruiz De, Luzuriaga, Sarah, Maidlow, Ashley, Martinez, Jomol, Mathew, James C, McClay, Gabriel, McMahan, Brian, Melancon, Stephane, Meystre, Lucio, Miele, Hiroki, Morizono, Ray, Pablo, Lav, Patel, Jimmy, Phuong, Daniel J, Popham, Claudia, Pulgarin, Carlos, Santos, Indra Neil, Sarkar, Nancy, Sazo, Soko, Setoguchi, Selvin, Soby, Sirisha, Surampalli, Christine, Suver, Uma Maheswara Reddy, Vangala, Shyam, Visweswaran, James von, Oehsen, Kellie M, Walters, Laura, Wiley, David A, Williams, and Adrian, Zai

Subjects
Health Insurance Portability and Accountability Act, AcademicSubjects/SCI01060, Computer science, business.industry, COVID-19, Health Informatics, Context (language use), Data accuracy, Benchmarking, Research and Applications, Data science, Pipeline (software), United States, Data modeling, Cohort Studies, Analytics, Data quality, Informatics, Humans, Electronic Health Records, AcademicSubjects/SCI01530, Heuristics, business, AcademicSubjects/MED00580
Abstract

Objective In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Published
2022

11. The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction

Author

John Muschelli, Mark M. Bissell, Melissa A. Haendel, Seth Russell, Adit Anand, Anita Walden, Richard A. Moffitt, Michele I. Morris, Eli Levitt, Davera Gabriel, Brian T. Garibaldi, Tellen D. Bennett, Benjamin Amor, Heidi Spratt, Kenneth R. Gersing, Amin Manna, Janos Hajagos, Matvey B. Palchuk, Richard L Zhu, Nabeel Qureshi, Julie A. McMurry, Carolyn Bremer, Alina Denham, Elaine L. Hill, Kristin Kostka, Andrew T Girvin, Katie Rebecca Bradwell, Justin Guinney, Jacob T. Wooldridge, Andrew E. Williams, Emily R. Pfaff, Yun Jae Yoo, Stephanie S Hong, James Brian Byrd, Xiaohan Tanner Zhang, Joel H. Saltz, Peter E. DeWitt, Zhenglong Qian, Christopher P. Austin, Andrew J. Neumann, Christopher G. Chute, Hunter Jimenez, Ramakanth Kavuluru, Harold P Lehmann, and Sandeep K. Mallipattu

Subjects
medicine.medical_specialty, business.industry, MEDLINE, Vital signs, Retrospective cohort study, Predictive analytics, Health informatics, Clinical decision support system, Article, Family medicine, Cohort, medicine, Observational study, business
Abstract

BackgroundThe majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy.Methods and FindingsIn a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen ConclusionsThis is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

Published
2021

12. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment

Author

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

Subjects
Data Analysis, AcademicSubjects/SCI01060, Computer science, Population, Health Informatics, Research and Applications, 03 medical and health sciences, 0302 clinical medicine, Humans, 030212 general & internal medicine, education, Intersectoral Collaboration, AcademicSubjects/MED00580, Computer Security, 030304 developmental biology, 0303 health sciences, education.field_of_study, Information Dissemination, business.industry, Data Science, COVID-19, Collaborative learning, Data science, United States, Knowledge sharing, Data access, National Institutes of Health (U.S.), Data extraction, Analytics, Data quality, Government Regulation, Clinical and Translational Science Award, AcademicSubjects/SCI01530, business, Ethics Committees, Research
Abstract

Objective 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. Methods The Clinical and Translational Science Award (CTSA) 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. Organized in inclusive workstreams, in two months 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. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational 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. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets 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 clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

Published
2020
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13. Privacy‐preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience

Author

Umberto Tachinardi, Shaun J. Grannis, Sam G. Michael, Leonie Misquitta, Jayme Dahlin, Usman Sheikh, Abel Kho, Jasmin Phua, Sara S. Rogovin, Benjamin Amor, Maya Choudhury, Philip Sparks, Amin Mannaa, Saad Ljazouli, Joel Saltz, Fred Prior, Ahmen Baghal, Kenneth Gersing, and Peter J. Embi

Subjects
clinical research, data privacy, learning health systems, record linkage, translational research, Medicine (General), R5-920, Public aspects of medicine, RA1-1270
Abstract

Abstract Introduction Research driven by real‐world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID‐19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy‐preserving record linkage (PPRL) methods to ensure that patient‐level EHR data remains secure and private when governance‐approved linkages with other datasets occur. Methods Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using “deidentified tokens,” which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear‐text identifiers. Results These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross‐linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion This large‐scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.

Published
2024
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