Evidence map›Paper›PMID 41406929›Full record

ArticleJACC. Advances2026

Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs Study.

Annie E Bowles, Julie A Lynch, Francisca Bermudez, Gabrielle E Shakt, Tia DiNatale, Kathryn M Pridgen, Renae L Judy, Michael G Levin, Katherine Hartmann, Scott M Damrauer and 1 more

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Article in JACC. Advances, 2026. 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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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Annie E BowlesVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, Utah, USA. Electronic address: annie.bowles@va.gov.
Julie A LynchVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, Utah, USA; Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Francisca BermudezDepartment of Surgery, Georgetown University School of Medicine, Washington, DC, USA; Department of Surgery, Perelman School of Medicine and the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Gabrielle E ShaktDepartment of Surgery, Perelman School of Medicine and the University of Pennsylvania, Philadelphia, Pennsylvania, USA; Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA.
Tia DiNataleVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
Kathryn M PridgenVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
Renae L JudyDepartment of Surgery, Perelman School of Medicine and the University of Pennsylvania, Philadelphia, Pennsylvania, USA; Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA.
Michael G LevinCorporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA; Department of Medicine, Perelman School of Medicine and the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Katherine HartmannDepartment of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Scott M DamrauerDepartment of Surgery, Perelman School of Medicine and the University of Pennsylvania, Philadelphia, Pennsylvania, USA; Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA.
Patrick R AlbaVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, Utah, USA; Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.

Funding

BLRD VA IK2 BX006551
6 · The paper itself

Abstract

backgroundBicuspid aortic valve (BAV) is the most common congenital heart defect but often evades timely diagnosis due to variable clinical presentations. Prior to October 2024, no specific diagnosis code existed for BAV, limiting retrospective identification.

objectivesThe purpose of this study was to develop and validate a natural language processing (NLP) system for automated extraction of heart valve morphology from echocardiographic reports, with focus on BAV detection.

methodsWe developed a rule-based NLP system using MedSpaCy to analyze echocardiographic reports from the Veterans Affairs Corporate Data Warehouse. The system was trained on 555 manually annotated reports and validated on 170 held-out reports. System performance was evaluated on valve leaflet structure identification.

resultsThe NLP system achieved excellent performance for BAV detection with a precision of 0.925, a sensitivity of 0.939, and an F1-score of 0.932. When applied to 14,453,591 echocardiographic documents from 3,478,658 patients, the system identified 83,461 patients (2.40%) with affirmed BAV. Among patients identified by the International Classification of Diseases-10 code Q23.81, NLP showed 86.1% concordance, with manual review confirming NLP accuracy in discordant cases.

conclusionsThis NLP approach enables large-scale retrospective identification of BAV patients from clinical text, creating the largest BAV cohort to date and facilitating future cardiovascular research and clinical decision-making.

Indexed as

bicuspid aortic valvecongenital heart diseaseechocardiographyelectronic health recordsnatural language processing

Identifiers

PMID41406929
PMCPMC12869887

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LicenceCC BY-NC-ND
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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.