Evidence map›Paper›PMID 34921902›Full record

Trial reportInternational journal of cardiology2022

Use of troponins in the classification of myocardial infarction from electronic health records. The Atherosclerosis Risk in Communities (ARIC) Study.

Anna M Kucharska-Newton, Matthew Shane Loop, Manuela Bullo, Carlton Moore, Stephanie W Haas, Lynne Wagenknecht, Eric A Whitsel, Gerardo Heiss

Open access · greenAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in International journal of cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 70% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

8 authors at 2 institutions in 2 countries.

Anna M Kucharska-NewtonDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, United States of America. Electronic address: anna_newton@unc.edu.
Matthew Shane LoopDivision of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
Manuela BulloHospital Ramos Mejia, Buenos Aires, Argentina.
Carlton MooreDepartment of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
Stephanie W HaasSchool of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
Lynne WagenknechtWake Forest University Population Health Sciences, Winston-Salem, NC, United States of America.
Eric A WhitselDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
Gerardo HeissDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
University of North Carolina at Chapel Hill · USHospital Ramos Mejía · AR

Funding

NHLBI NIH HHS HHSN268201700001CNHLBI NIH HHS HHSN268201700001INHLBI NIH HHS HHSN268201700002CNHLBI NIH HHS HHSN268201700002INHLBI NIH HHS HHSN268201700003CNHLBI NIH HHS HHSN268201700003INHLBI NIH HHS HHSN268201700004CNHLBI NIH HHS HHSN268201700004INHLBI NIH HHS HHSN268201700005CNHLBI NIH HHS HHSN268201700005I
6 · The paper itself

Abstract

objectiveElectronic health record (EHR) data are underutilized for abstracting classification criteria for heart disease. We compared extraction of EHR data on troponin I and T levels with human abstraction.

methodsUsing EHR for hospitalizations identified through the Atherosclerosis Risk in Communities (ARIC) Study in four US hospitals, we compared blood levels of troponins I and T extracted from EHR structured data elements with levels obtained through data abstraction by human abstractors to 3 decimal places. Observations were divided randomly 50/50 into training and validation sets. Bayesian multilevel logistic regression models were used to estimate agreement by hospital in first and maximum troponin levels, troponin assessment date, troponin upper limit of normal (ULN), and classification of troponin levels as normal (< ULN), equivocal (1-2× ULN), abnormal (>2× ULN), or missing.

resultsEstimated overall agreement in first measured troponin level in the validation data was 88.2% (95% credible interval: 65.0%-97.5%) and 95.5% (91.2-98.2%) for the maximum troponin level observed during hospitalization. The largest variation in probability of agreement was for first troponin measured, which ranged from 66.4% to 95.8% among hospitals.

conclusionExtraction of maximum troponin values during a hospitalization from EHR structured data is feasible and accurate.

Indexed as

AtherosclerosisMyocardial InfarctionBayes TheoremBiomarkersElectronic Health RecordsHumansTroponin ITroponin TBiomarkersTroponin ITroponin TAlgorithmic abstractionElectronic health recordsMyocardial infarctionTroponin

Identifiers

PMID34921902
PMCPMC8775766
OpenAlexW4200152707

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.