Evidence map›Paper›PMID 31390681›Full record

ArticlePharmacoepidemiology and drug safety2019

A comparison of two algorithms to identify sudden cardiac deaths in computerized databases.

Jea Young Min, Carlos G Grijalva, James A Morrow, Christine C Whitmore, Robert E Hawley, Sonal Singh, Richard S Swain, Marie R Griffin

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Pharmacoepidemiology and drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jea Young MinVeterans Health Administration Tennessee Valley Healthcare System, Geriatric Research and Education Clinical Center (GRECC), HSR&D Center, Nashville, Tennessee, USA.
Carlos G GrijalvaVeterans Health Administration Tennessee Valley Healthcare System, Geriatric Research and Education Clinical Center (GRECC), HSR&D Center, Nashville, Tennessee, USA.
James A MorrowDepartment of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Christine C WhitmoreDepartment of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Robert E HawleyDepartment of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Sonal SinghDepartment of Family Medicine & Community Health, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Richard S SwainCenter for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA.
Marie R GriffinVeterans Health Administration Tennessee Valley Healthcare System, Geriatric Research and Education Clinical Center (GRECC), HSR&D Center, Nashville, Tennessee, USA.

Funding

Opioid selection and the risk of serious infections in older adultsR01AG043471 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GRIJALVA, CARLOS G · 2013 to 2017
$2.3M
FDA HHS HHSF223201400030IIntramural VA VA999999NIA NIH HHS R01 AG043471NIA NIH HHS R01AG043471
6 · The paper itself

Abstract

purposeTwo previously validated algorithms to identify sudden cardiac death using administrative data showed high positive predictive value. We evaluated the agreement between the algorithms using data from a common source population.

methodsWe conducted a cross-sectional study to assess the percent agreement between deaths identified by two sudden cardiac death algorithms using Tennessee Medicaid and death certificate data from 2007 through 2014. The source population included all deceased patients aged 18 to 64 years with Medicaid enrollment in the 6 months prior to death. To identify sudden cardiac deaths, algorithm 1 used only hospital/emergency department (ED) claims from encounters at the time of death, and algorithm 2 required death certificates and used claims data for specific exclusion criteria.

resultsWe identified 34 107 deaths in the source population over the study period. The two algorithms identified 4372 potential sudden cardiac deaths: Algorithm 1 identified 3117 (71.3%) and algorithm 2 identified 1715 (39.2%), with 460 (10.5%) deaths identified by both algorithms. Of the deaths identified by algorithm 1, 1943 (62.3%) had an underlying cause of death not specified in algorithm 2. Of the deaths identified by algorithm 2, 1053 (61.4%) had no record of a hospital or ED encounter at the time of death, and 202 (11.8%) had a discharge diagnosis code not specified in algorithm 1.

conclusionsWe found low agreement between the two algorithms for identification of sudden cardiac deaths because of differences in sudden cardiac death definitions and data sources.

Indexed as

Cause of DeathDeath CertificatesDeath, Sudden, CardiacAdministrative Claims, HealthcareAdolescentAdultAlgorithmsClinical CodingCross-Sectional StudiesDatabases, FactualData CollectionEmergency Service, HospitalFemaleHumansInternational Classification of DiseasesMalealgorithmsdatabasepharmacoepidemiologysudden cardiac death

Identifiers

PMID31390681
PMCPMC6810726

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.