Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C)
Sara Jones, Katie R. Bradwell, Lauren Chan, Julie A. McMurry, Courtney Olson‐Chen, Jessica Tarleton, Kenneth J. Wilkins, Victoria Ly, Saad Ljazouli, Qiuyuan Qin, Emily A. Groene, Yan Kwan Lau, Catherine Xie, Yu-Han Kao, Michael Liebman, Federico Mariona, Anup P. Challa, Li Li, Sarah J. Ratcliffe, Melissa Haendel, Rena C. Patel, Elaine Hill, Adam Wilcox, Adam M Lee, Alexis Graves, Alfred Anzalone, Amin Manna, Amit Saha, Amy L. Olex, Andrea Zhou, Andrew E. Williams, Andrew M. Southerland, Andrew T. Girvin, Anita Walden, Anjali Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn T. Bramante, Cavin Ward‐Caviness, Charisse Madlock‐Brown, Christine Suver, Christopher G. Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David Eichmann, Diego R. Mazzotti, Donald D. Brown, Eilis Boudreau, Elizabeth Zampino, Emily Carlson Marti, Emily Pfaff, Evan French, Farrukh M. Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg S. Martin, Harold P. Lehmann, Heidi Spratt, Hemalkumar B. Mehta, Hongfang Liu, Hythem Sidky, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Harper, Jessica Y. Islam, Jin Ge, Joel Gagnier, Joel Saltz, Johanna Loomba, John B. Buse, Jomol Mathew, Joni L. Rutter, Justin Starren, Karen Crowley, Katie R. Bradwell, Kellie M Walters, Ken Wilkins, Kenneth Gersing, Kenrick 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 Saltz
Abstract
Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.