Evidence map›Paper›PMID 41023656›Full record

ArticleBMC medical informatics and decision making2025

Prediction of atrial fibrillation admissions in arrhythmia naïve patients from structured electronic health record data.

Tanmay Gokhale, Nirav R Bhatt, Matthew Starr, Suresh Mulukutla, Floyd Thoma, Murat Akcakaya, Salah Al-Zaiti, Raul G Nogueira, Samir Saba

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Article in BMC medical informatics and decision making, 2025. 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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1 · What the graph read from it

What it found

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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

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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

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Tanmay GokhaleHeart and Vascular Institute, University of Pittsburgh Medical Center, 200 Lothrop Street, South Tower, Pittsburgh, PA, E355.6, USA.
Nirav R BhattNeurology Department, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Matthew StarrNeurology Department, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Suresh MulukutlaHeart and Vascular Institute, University of Pittsburgh Medical Center, 200 Lothrop Street, South Tower, Pittsburgh, PA, E355.6, USA.
Floyd ThomaHeart and Vascular Institute, University of Pittsburgh Medical Center, 200 Lothrop Street, South Tower, Pittsburgh, PA, E355.6, USA.
Murat AkcakayaSwanson School of Engineering at the University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Salah Al-ZaitiNursing School at the University of Rochester, Rochester, New York, USA.
Raul G NogueiraNeurology Department, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Samir SabaHeart and Vascular Institute, University of Pittsburgh Medical Center, 200 Lothrop Street, South Tower, Pittsburgh, PA, E355.6, USA. sabas@upmc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAtrial fibrillation (AF) is the most prevalent sustained arrhythmia, but its diagnosis is often elusive. In this study, we examined the role of machine learning (ML) algorithms in predicting AF in arrhythmia-naïve patients, based on structured domains of the electronic health records (EHR).

methodsPatients (N = 186,769) with no prior history of AF, who received at least 1 echocardiogram and who had a minimum of 3 months of follow-up, were included. Data from the EHR were grouped into domains (demographic; social determinants of health; past medical history, medications, electrocardiogram (EKG), and echocardiogram (Echo)) and tested incrementally for their ability to predict incident AF admission to the hospital.

resultsOf the overall cohort, 4,751 (2.5%) patients were admitted for AF over a median follow-up time of 35 months. Incremental EHR domains increased the area under the receiver-operator curve (AUROC) for all ML classifiers, with Gradient Boosting achieving an AUROC of 0.85 when all domains were included, but with a poor F1 score of 14% at the maximal Youden index. Using the EKG and Echo domains alone achieved comparable performance to when all EHR domains were included. These results were externally validated.

conclusionMore domains of structured EHR improve the ability to predict incident AF admissions but structured EKG and Echo domains realize the most gain. Although ML models exhibited good discrimination, the precision is poor due to the low event rate.

Indexed as

Atrial FibrillationElectronic Health RecordsHospitalizationMachine LearningAgedFemaleHumansMaleMiddle AgedAdmissionAtrial fibrillationElectronic health recordsMachine learningPrediction

Identifiers

PMID41023656
PMCPMC12482350

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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.