Evidence map›Paper›PMID 34127492›Full record

Observational studyBMJ open2021

Ascertaining Framingham heart failure phenotype from inpatient electronic health record data using natural language processing: a multicentre Atherosclerosis Risk in Communities (ARIC) validation study.

Carlton R Moore, Saumya Jain, Stephanie Haas, Harish Yadav, Eric Whitsel, Wayne Rosamand, Gerardo Heiss, Anna M Kucharska-Newton

Open access · goldAbstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.7field-weighted citation impact, top 8% 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

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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  5. Review
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 1 institution in 1 country.

Carlton R MooreMedicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA crmoore@med.unc.edu.ORCID 0000-0001-7732-4829
Saumya JainEpidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.ORCID 0000-0002-4083-3918
Stephanie HaasSchool of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Harish YadavSchool of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Eric WhitselEpidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Wayne RosamandEpidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Gerardo HeissEpidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Anna M Kucharska-NewtonEpidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
University of North Carolina at Chapel Hill · US

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

objectivesUsing free-text clinical notes and reports from hospitalised patients, determine the performance of natural language processing (NLP) ascertainment of Framingham heart failure (HF) criteria and phenotype. STUDY

designA retrospective observational study design of patients hospitalised in 2015 from four hospitals participating in the Atherosclerosis Risk in Communities (ARIC) study was used to determine NLP performance in the ascertainment of Framingham HF criteria and phenotype.

settingFour ARIC study hospitals, each representing an ARIC study region in the USA.

participantsA stratified random sample of hospitalisations identified using a broad range of International Classification of Disease, ninth revision, diagnostic codes indicative of an HF event and occurring during 2015 was drawn for this study. A randomly selected set of 394 hospitalisations was used as the derivation dataset and 406 hospitalisations was used as the validation dataset.

interventionUse of NLP on free-text clinical notes and reports to ascertain Framingham HF criteria and phenotype. PRIMARY AND SECONDARY OUTCOME MEASURES: NLP performance as measured by sensitivity, specificity, positive-predictive value (PPV) and agreement in ascertainment of Framingham HF criteria and phenotype. Manual medical record review by trained ARIC abstractors was used as the reference standard.

resultsOverall, performance of NLP ascertainment of Framingham HF phenotype in the validation dataset was good, with 78.8%, 81.7%, 84.4% and 80.0% for sensitivity, specificity, PPV and agreement, respectively.

conclusionsBy decreasing the need for manual chart review, our results on the use of NLP to ascertain Framingham HF phenotype from free-text electronic health record data suggest that validated NLP technology holds the potential for significantly improving the feasibility and efficiency of conducting large-scale epidemiologic surveillance of HF prevalence and incidence.

Indexed as

AtherosclerosisHeart FailureAlgorithmsElectronic Health RecordsHumansInpatientsNatural Language ProcessingPhenotypecardiac epidemiologyhealth informaticsheart failure

Identifiers

PMID34127492
PMCPMC8204176
OpenAlexW3171098325

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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 OpenQuestion graph.