Susan E Andrade1, Mayura Shinde2, Tiffany A Moore Simas3, Steven T Bird4, Justin Bohn2, Kevin Haynes5, Lockwood G Taylor4, Julianne R Lauring3, Erin Longley6, Cheryl N McMahill-Walraven7, Connie M Trinacty8, Cassandra Saphirak1, Christopher Delude1, Sandra DeLuccia2, Tancy Zhang2, David V Cole2, Nina DiNunzio2, Autumn Gertz2, Elnara Fazio-Eynullayeva2, Danijela Stojanovic4. 1. The Meyers Primary Care Institute, a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health, University of Massachusetts Medical School, Worcester, Massachusetts, USA. 2. Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA. 3. Department of Obstetrics and Gynecology, University of Massachusetts Medical School/UMass Memorial Health Care, Worcester, Massachusetts, USA. 4. Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA. 5. Department of Scientific Affairs, HealthCore, Inc., Wilmington, Delaware, USA. 6. Community Health Care Family Medicine Residency, Tacoma, Washington, USA. 7. CVS Health Clinical Trial Services, Part of the CVS Health Family of Companies, Blue Bell, Pennsylvania, USA. 8. Kaiser Permanente Center for Integrated Health Care Research Hawaii and Office of Public Health Studies, University of Hawai'i Manoa, Honolulu, Hawaii, USA.
Abstract
PURPOSE: To develop and validate an International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify cases of stillbirth using electronic healthcare data. METHODS: We conducted a retrospective study using claims data from three Data Partners (healthcare systems and insurers) in the Sentinel Distributed Database. Algorithms were developed using ICD-10-CM diagnosis codes to identify potential stillbirths among females aged 12-55 years between July 2016 and June 2018. A random sample of medical charts (N = 169) was identified for chart abstraction and adjudication. Two physician adjudicators reviewed potential cases to determine whether a stillbirth event was definite/probable, the date of the event, and the gestational age at delivery. Positive predictive values (PPVs) were calculated for the algorithms. Among confirmed cases, agreement between the claims data and medical charts was determined for the outcome date and gestational age at stillbirth. RESULTS: Of the 110 potential cases identified, adjudicators determined that 54 were stillbirth events. Criteria for the algorithm with the highest PPV (82.5%; 95% CI, 70.9%-91.0%) included the presence of a diagnosis code indicating gestational age ≥20 weeks and occurrence of either >1 stillbirth-related code or no other pregnancy outcome code (i.e., livebirth, spontaneous abortion, induced abortion) recorded on the index date. We found ≥90% agreement within 7 days between the claims data and medical charts for both the outcome date and gestational age at stillbirth. CONCLUSIONS: Our results suggest that electronic healthcare data may be useful for signal detection of medical product exposures potentially associated with stillbirth.
PURPOSE: To develop and validate an International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify cases of stillbirth using electronic healthcare data. METHODS: We conducted a retrospective study using claims data from three Data Partners (healthcare systems and insurers) in the Sentinel Distributed Database. Algorithms were developed using ICD-10-CM diagnosis codes to identify potential stillbirths among females aged 12-55 years between July 2016 and June 2018. A random sample of medical charts (N = 169) was identified for chart abstraction and adjudication. Two physician adjudicators reviewed potential cases to determine whether a stillbirth event was definite/probable, the date of the event, and the gestational age at delivery. Positive predictive values (PPVs) were calculated for the algorithms. Among confirmed cases, agreement between the claims data and medical charts was determined for the outcome date and gestational age at stillbirth. RESULTS: Of the 110 potential cases identified, adjudicators determined that 54 were stillbirth events. Criteria for the algorithm with the highest PPV (82.5%; 95% CI, 70.9%-91.0%) included the presence of a diagnosis code indicating gestational age ≥20 weeks and occurrence of either >1 stillbirth-related code or no other pregnancy outcome code (i.e., livebirth, spontaneous abortion, induced abortion) recorded on the index date. We found ≥90% agreement within 7 days between the claims data and medical charts for both the outcome date and gestational age at stillbirth. CONCLUSIONS: Our results suggest that electronic healthcare data may be useful for signal detection of medical product exposures potentially associated with stillbirth.
Authors: Monica L Bertoia; Kelesitse Phiri; C Robin Clifford; Michael Doherty; Li Zhou; Laura T Wang; Natalie A Bertoia; Florence T Wang; John D Seeger Journal: Pharmacoepidemiol Drug Saf Date: 2022-06-07 Impact factor: 2.732