Evidence map›Paper›PMID 30471046›Full record

SynthesisDrug safety2019

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 and 10 more

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.4field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
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  6. Article
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  8. Article
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  11. Data Consult Service: Can we use observational data to address immediate clinical needs?Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  12. Article
  13. Review
  14. Observational
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 6 institutions in 1 country.

Sonal SinghDepartment of Family Medicine and Community Health and Meyers Primary Care Institute, University of Massachusetts Medical School, 55 Lake Ave N, Worcester, MA, USA. Sonal.Singh@umassmemorial.org.ORCID 0000-0003-0912-941X
Hassan FouayziDepartment of Family Medicine and Community Health and Meyers Primary Care Institute, University of Massachusetts Medical School, 55 Lake Ave N, Worcester, MA, USA.
Kathryn AnzuoniDepartment of Family Medicine and Community Health and Meyers Primary Care Institute, University of Massachusetts Medical School, 55 Lake Ave N, Worcester, MA, USA.
Leah GoldmanDepartment of Family Medicine and Community Health and Meyers Primary Care Institute, University of Massachusetts Medical School, 55 Lake Ave N, Worcester, MA, USA.
Jea Young MinVanderbilt University Medical Center, Nashville, TN, USA.
Marie GriffinVanderbilt University Medical Center, Nashville, TN, USA.
Carlos G GrijalvaVanderbilt University Medical Center, Nashville, TN, USA.
James A MorrowVanderbilt University Medical Center, Nashville, TN, USA.
Christine C WhitmoreVanderbilt University Medical Center, Nashville, TN, USA.
Charles E LeonardDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Mano SelvanHumana/Comprehensive Health Insights, Inc., Louisville, KY, USA.
Vinit NairHumana/Comprehensive Health Insights, Inc., Louisville, KY, USA.
Yunping ZhouHumana/Comprehensive Health Insights, Inc., Louisville, KY, USA.
Sengwee TohDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA, USA.
Andrew PetroneDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA, USA.
James WilliamsDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA, USA.
Elnara Fazio-EynullayevaDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA, USA.
Richard SwainOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0002-7578-2620
D Tyler CoyleOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, USA.
Susan AndradeDepartment of Family Medicine and Community Health and Meyers Primary Care Institute, University of Massachusetts Medical School, 55 Lake Ave N, Worcester, MA, USA.
University of Massachusetts Chan Medical School · USVanderbilt University Medical Center · USHarvard University · USHumana (United States) · USUnited States Food and Drug Administration · USUniversity of Pennsylvania · US

Funding

U.S. Food and Drug Administration HHSF22301012T
6 · The paper itself

Abstract

introductionValid 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.

objectiveThe aim was to conduct a systematic review of algorithms to identify CV death in administrative health plan claims databases.

methodsWe 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.

resultsFive 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%).

conclusionTwo 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.

Indexed as

AlgorithmsCardiovascular SystemDatabases, FactualData CollectionDeath, Sudden, CardiacHumansInternational Classification of DiseasesObservational Studies as Topic

Identifiers

PMID30471046
OpenAlexW2901735968

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.