Evidence map›Paper›PMID 41460938›Full record

ArticleCirculation2026

Deep Learning-Based Continuous QT Monitoring to Identify High-Risk Prolongation Events After Class III Antiarrhythmic Initiation.

Rayan A Ansari, Sabyasachi Bandyopadhyay, Rishi K Trivedi, Kelly A Brennan, Xichong Liu, Prasanth Ganesan, J Weston Hughes, Alexander C Perino, Euan A Ashley, Paul J Wang and 5 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Circulation, 2026. 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
–field-weighted citation impact
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.

  1. Review
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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

15 authors.

Rayan A Ansari *Department of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0001-6153-7272
Sabyasachi Bandyopadhyay *Department of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0009-0003-4825-646X
Rishi K TrivediDepartment of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (R.T., D.O.).
Kelly A BrennanDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0002-7698-7353
Xichong LiuDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0002-8772-5623
Prasanth GanesanDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0002-1885-0690
J Weston HughesDepartment of Biomedical Informatics (J.W.H.), Columbia University Irving Medical Center, New York, NY.ORCID 0009-0001-9810-6094
Alexander C PerinoDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0001-5482-8055
Euan A AshleyDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0001-9418-9577
Paul J WangDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0002-5467-5877
Todd ColemanSchool of Medicine, Department of Bioengineering (T.C.), Stanford University, CA.ORCID 0000-0002-2144-2495
Marco V PerezDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0003-2746-0202
David OuyangDepartment of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (R.T., D.O.).ORCID 0000-0002-3813-7518
Sanjiv M NarayanDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0001-7552-5053
Albert J RogersDepartment of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.ORCID 0000-0001-6585-534X

Funding

The Dynamics of Human Atrial FibrillationR01HL083359 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Sanjiv M Narayan · 2008 to 2026
$7.9M
Machine Learning in Atrial FibrillationR01HL149134 · NHLBI · STANFORD UNIVERSITY · PI NARAYAN, SANJIV M · 2020 to 2024
$3.8M
Deep Learning Based Phenotyping and Outcomes Prediction for Valvular Heart DiseaseR01HL173526 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI David Ouyang · 2024 to 2026
$2.5M
Machine Learning for Ventricular ArrhythmiasR01HL162260 · NHLBI · STANFORD UNIVERSITY · PI Sanjiv M Narayan · 2023 to 2026
$2.5M
Computational Medicine in the Heart, Integrated Training ProgramT32HL166155 · NHLBI · STANFORD UNIVERSITY · PI Alison L Marsden, Sanjiv M Narayan · 2023 to 2026
$2.0M
Artificial Intelligence Imaging Biomarkers of Longitudinal Cardiovascular StressR01HL173487 · NHLBI · KAISER FOUNDATION RESEARCH INSTITUTE · PI David Ouyang · 2025 to 2026
$1.4M
Deep Learning Assessment of the Right Ventricle: Function, Etiology, and PrognosisR00HL157421 · NHLBI · KAISER FOUNDATION RESEARCH INSTITUTE · PI OUYANG, DAVID · 2023 to 2025
$747k
Electrical Mapping Signatures of Adverse Structural and Functional Remodeling in Ventricular ArrhythmiaK23HL166977 · NHLBI · STANFORD UNIVERSITY · PI Albert Joseph Rogers · 2023 to 2026
$680k
NHLBI NIH HHS K23 HL166977NHLBI NIH HHS R00 HL157421NHLBI NIH HHS R01 HL083359NHLBI NIH HHS R01 HL149134NHLBI NIH HHS R01 HL162260NHLBI NIH HHS R01 HL173487NHLBI NIH HHS R01 HL173526NHLBI NIH HHS T32 HL166155
6 · The paper itself

Abstract

backgroundDrug-induced QT prolongation after successful inpatient loading of class III antiarrhythmics may occur during routine outpatient care. Insertable cardiac monitors offer continuous signals but are limited by single-lead configuration. We hypothesized that a spatially aware deep learning system (3DRECON-QT) can reconstruct spatial information from a single lead vector to quantify QT/QTc and identify high-risk prolongation.

methodsWe developed 3DRECON-QT using a multitask encoder-decoder that ingests a 10-s single-lead signal, reconstructs 12 leads, and predicts QT/QTc. The model was developed using 12-lead ECGs with clinician-adjudicated QT/RR from a large health system and tested in an external center with different ECG hardware. Continuous monitoring performance was assessed in a public dofetilide-loading data set with serial ECGs. In a real-world cohort of outpatients on dofetilide or sotalol presenting to the hospital or emergency room for any reason, rates of ventricular arrhythmias and QT prolongation were assessed. Device validation was tested in patients with insertable cardiac monitor recordings paired with clinical 12-lead ECGs.

results3DRECON-QT classified prolonged QTc from single-lead signals with area under the receiver operating characteristics curve, 0.942 (mean absolute error, 17.5 ms) in the internal test set and 0.943 (mean absolute error, 21.1 ms) externally. During continuous dofetilide monitoring, predictions correlated with ground truth (

conclusionsA single-lead, deep-learning approach can achieve guideline-level measurement accuracy, enable continuous QTc surveillance from nonstandard ECG vectors, and identify clinically meaningful outpatient QTc prolongation associated with a >4-fold increase in serious ventricular arrhythmias. This strategy may enhance safety monitoring after class III antiarrhythmic initiation and support targeted intervention.

Indexed as

Anti-Arrhythmia AgentsDeep LearningElectrocardiographyLong QT SyndromeAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentSignal Processing, Computer-AssistedAnti-Arrhythmia Agentsanti-arrhythmia agentsarrhythmias, cardiacdeep learningdrug-related side effects and adverse reactionselectrocardiographymonitoring, ambulatoryTorsades de Pointes

Identifiers

PMID41460938
PMCPMC13155491

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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.