Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C)
Authors: Jones Sara E , Bradwell Katie R , Chan Lauren E , McMurry Julie A , Olson-Chen Courtney , Tarleton Jessica , Wilkins Kenneth J , Ly Victoria , Ljazouli Saad , Qin Qiuyuan , Faherty Emily Groene , Lau Yan Kwan , Xie Catherine , Kao Yu-Han , Liebman Michael N , Mariona Federico , Challa Anup P , Li Li , Ratcliffe Sarah J , Haendel Melissa A , Patel Rena C , Hill Elaine L ,   N3C Consortium, 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 , 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 , Awori Hayanga J W , Pincavitch Jami , Clark Jaylyn , Harper Jeremy Richard , Islam Jessica , Ge Jin , Gagnier Joel , Saltz Joel H , Loomba Johanna , Buse John , Mathew Jomol , Rutter Joni L , 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 , 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 
Published: August 2023

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.