Volume 30, Issue 9 pp. 1175-1183
ORIGINAL ARTICLE

Validation of an ICD-10-based algorithm to identify stillbirth in the Sentinel System

Susan E. Andrade

Corresponding Author

Susan E. Andrade

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

Correspondence

Susan E. Andrade, The Meyers Primary Care Institute 385 Grove Street, Worcester, MA 01605, USA.

Email: [email protected]

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Mayura Shinde

Mayura Shinde

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA

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Tiffany A. Moore Simas

Tiffany A. Moore Simas

Department of Obstetrics and Gynecology, University of Massachusetts Medical School/UMass Memorial Health Care, Worcester, Massachusetts, USA

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Steven T. Bird

Steven T. Bird

Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA

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Justin Bohn

Justin Bohn

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA

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Kevin Haynes

Kevin Haynes

Department of Scientific Affairs, HealthCore, Inc., Wilmington, Delaware, USA

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Lockwood G. Taylor

Lockwood G. Taylor

Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA

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Julianne R. Lauring

Julianne R. Lauring

Department of Obstetrics and Gynecology, University of Massachusetts Medical School/UMass Memorial Health Care, Worcester, Massachusetts, USA

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Erin Longley

Erin Longley

Community Health Care Family Medicine Residency, Tacoma, Washington, USA

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Cheryl N. McMahill-Walraven

Cheryl N. McMahill-Walraven

CVS Health Clinical Trial Services, Part of the CVS Health Family of Companies, Blue Bell, Pennsylvania, USA

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Connie M. Trinacty

Connie M. Trinacty

Kaiser Permanente Center for Integrated Health Care Research Hawaii and Office of Public Health Studies, University of Hawai'i Manoa, Honolulu, Hawaii, USA

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Cassandra Saphirak

Cassandra Saphirak

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

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Christopher Delude

Christopher Delude

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

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Sandra DeLuccia

Sandra DeLuccia

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA

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Tancy Zhang

Tancy Zhang

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA

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David V. Cole

David V. Cole

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA

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Nina DiNunzio

Nina DiNunzio

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA

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Autumn Gertz

Autumn Gertz

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA

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Elnara Fazio-Eynullayeva

Elnara Fazio-Eynullayeva

Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA

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Danijela Stojanovic

Danijela Stojanovic

Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA

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First published: 04 June 2021
Citations: 11

This work was presented at the 36th International Conference on Pharmacoepidemiology & Therapeutic Risk Management.

Funding information: U.S. Food and Drug Administration, Grant/Award Numbers: HHSF223201400030I, HHSF22301002T

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.

CONFLICT OF INTEREST

S.E.A. has received grant support from Pfizer Inc. and GlaxoSmithKline. S.T.B., L.G.T., and D.S. are employed by the U.S. Food and Drug Administration.

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.