Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review

Sonal Singh (), Hassan Fouayzi (), Kathryn Anzuoni, Leah Goldman, Jea Young Min, Marie Griffin, Carlos G. Grijalva, James A. Morrow, Christine C. Whitmore, Charles E. Leonard, Mano Selvan, Vinit Nair, Yunping Zhou, Sengwee Toh, Andrew Petrone, James Williams, Elnara Fazio-Eynullayeva, Richard Swain, D. Tyler Coyle and Susan Andrade
Additional contact information
Sonal Singh: University of Massachusetts Medical School
Kathryn Anzuoni: University of Massachusetts Medical School
Leah Goldman: University of Massachusetts Medical School
Jea Young Min: Vanderbilt University Medical Center
Marie Griffin: Vanderbilt University Medical Center
Carlos G. Grijalva: Vanderbilt University Medical Center
James A. Morrow: Vanderbilt University Medical Center
Christine C. Whitmore: Vanderbilt University Medical Center
Charles E. Leonard: University of Pennsylvania
Mano Selvan: Humana/Comprehensive Health Insights, Inc.
Vinit Nair: Humana/Comprehensive Health Insights, Inc.
Yunping Zhou: Humana/Comprehensive Health Insights, Inc.
Sengwee Toh: Harvard Medical School and Harvard Pilgrim Healthcare Institute
Andrew Petrone: Harvard Medical School and Harvard Pilgrim Healthcare Institute
James Williams: Harvard Medical School and Harvard Pilgrim Healthcare Institute
Elnara Fazio-Eynullayeva: Harvard Medical School and Harvard Pilgrim Healthcare Institute
Richard Swain: United States Food and Drug Administration
D. Tyler Coyle: United States Food and Drug Administration
Susan Andrade: University of Massachusetts Medical School

Drug Safety, 2019, vol. 42, issue 4, No 4, 515-527

Abstract: Abstract Introduction Valid algorithms for identification of cardiovascular (CV) deaths allow researchers to reliably assess the CV safety of medications, which is of importance to regulatory science, patient safety, and public health. Objective The aim was to conduct a systematic review of algorithms to identify CV death in administrative health plan claims databases. Methods We searched MEDLINE, EMBASE, and Cochrane Library for English-language studies published between January 1, 2012 and October 17, 2017. We examined references in systematic reviews to identify earlier studies. Selection included any observational study using electronic health care data to evaluate the sensitivity, specificity, positive predictive value (PPV), or negative predictive value (NPV) of algorithms for CV death (sudden cardiac death [SCD], myocardial infarction [MI]-related death, or stroke-related death) among adults aged ≥ 18 years in the United States. Data were extracted by two independent reviewers, with disagreements resolved through further discussion and consensus. The Quality Assessment of Diagnostic Accuracy Studies-2 instrument was used to assess the risk of bias. Results Five studies (n = 4 on SCD, n = 1 on MI- and stroke-related death) were included after a review of 2053 citations. All studies reported algorithm PPVs, with incomplete reporting on other accuracy parameters. One study was at low risk of bias, three studies were at moderate risk of bias, and one study was at unclear risk of bias. Two studies identified community-occurring SCD: one identified events using International Classification of Disease, Ninth Revision (ICD-9) codes on death certificates and other criteria from medical claims (PPV = 86.8%) and the other identified events resulting in hospital presentation using first-listed ICD-9 codes on emergency department or inpatient medical claims (PPV = 92.3%). Two studies used death certificates alone to identify SCD (PPV = 27% and 32%, respectively). One study used medical claims to identify CV death (PPV = 36.4%), coronary heart disease mortality (PPV = 28.3%), and stroke mortality (PPV = 34.5%). Conclusion Two existing algorithms based on medical claims diagnoses with or without death certificates can accurately identify SCD to support pharmacoepidemiologic studies. Developing valid algorithms identifying MI- and stroke-related death should be a research priority. PROSPERO 2017 CRD42017078745.

Date: 2019
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DOI: 10.1007/s40264-018-0754-z

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