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Drug Safety

, Volume 42, Issue 4, pp 515–527 | Cite as

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

  • Sonal SinghEmail author
  • 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
  • Susan Andrade
Systematic Review
  • 162 Downloads

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.

Notes

Acknowledgements

Judy King provided administrative assistance during the conduct of the study.

Compliance with Ethical Standards

Ethical approval

The study did not require ethical approval as it was a systematic review of summary data.

Conflict of interest

Mano Selvan, Vinit Nair and Yunping Zhou report being employed by Humana during the conduct of the study. Sonal Singh, Hassan Fouyazi, Kathryn Anzuoni, Leah Goldman, Jea Young Min, Marie Griffin, Carlos G. Grijalva, James A. Morrow, Christine Whitmore, Charles E. Leonard, Sengwee Toh, Andrew Petrone, James Williams, Elnara Fazio-Eynullayeva, Richard Swain, D. Tyler Coyle and Susan Andrade have no conflicts of interest that are directly relevant to the contents of the study.

Funding

Funding for this study was made possible by the Food and Drug Administration through Contract Name and Number: Efforts to Develop the Sentinel Initiative | HHSF223200910006I and Task Order Name and Number: Foundational Elements 3 | HHSF22301012T. The views expressed in written materials are those of the authors and do not reflect the official policies of the Department of Health and Human Services or the Food and Drug Administration.

Supplementary material

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Supplementary material 1 (DOCX 29 kb)
40264_2018_754_MOESM2_ESM.docx (22 kb)
Supplementary material 2 (DOCX 22 kb)
40264_2018_754_MOESM3_ESM.docx (23 kb)
Supplementary material 3 (DOCX 23 kb)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sonal Singh
    • 1
    Email author
  • Hassan Fouayzi
    • 1
  • Kathryn Anzuoni
    • 1
  • Leah Goldman
    • 1
  • Jea Young Min
    • 2
  • Marie Griffin
    • 2
  • Carlos G. Grijalva
    • 2
  • James A. Morrow
    • 2
  • Christine C. Whitmore
    • 2
  • Charles E. Leonard
    • 3
  • Mano Selvan
    • 4
  • Vinit Nair
    • 4
  • Yunping Zhou
    • 4
  • Sengwee Toh
    • 5
  • Andrew Petrone
    • 5
  • James Williams
    • 5
  • Elnara Fazio-Eynullayeva
    • 5
  • Richard Swain
    • 6
  • D. Tyler Coyle
    • 6
  • Susan Andrade
    • 1
  1. 1.Department of Family Medicine and Community Health and Meyers Primary Care InstituteUniversity of Massachusetts Medical SchoolWorcesterUSA
  2. 2.Vanderbilt University Medical CenterNashvilleUSA
  3. 3.Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.Humana/Comprehensive Health Insights, Inc.LouisvilleUSA
  5. 5.Department of Population MedicineHarvard Medical School and Harvard Pilgrim Healthcare InstituteBostonUSA
  6. 6.Office of Surveillance and Epidemiology, Center for Drug Evaluation and ResearchUnited States Food and Drug AdministrationSilver SpringUSA

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