Artificial Intelligence-Enabled Electrocardiography for Preoperatively Detecting Atrial Fibrillation and Mortality Risk in Patients with Sinus Rhythm
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Le résumé fourni par la source
Background: Pre-existing atrial fibrillation (AF) and postoperative new-onset AF (NOAF) are independent perioperative risk factors associated with increased short-term mortality and adverse events.This study aimed to develop and validate an artificial intelligence (AI) model capable of detecting hidden AF, including both pre-existing AF and NOAF, from sinus rhythm electrocardiograms, to improve perioperative risks assessment.Methods: We trained and validated an AI model to detect hidden AF.Subsequent analysis confirmed the prognostic relevance of both pre-existing AF and NOAF in patients receiving non-cardiac surgery.The AI model was applied to patients without known AF to evaluate its predictive capability for NOAF and to stratify short-term clinical outcomes.Results: The AI model demonstrated an area under the receiver operating characteristic curve of 0.87 during the development phase for predicting AF.In an independent validation cohort, pre-existing AF and postoperative NOAF were significantly correlated with increased 30-day all-cause mortality.Patients without pre-existing AF who were classified as high-risk by the AI model had substantially higher 30-day all-cause mortality than their low-risk counterparts (HR 17.33, 95% CI 5.29-56.75).Furthermore, the model scores surpassed conventional clinical risk scores in predicting NOAF and 30-day all-cause mortality.Conclusions: This AI-based approach facilitated the accurate identification of patients with elevated perioperative AF-related risk.It will facilitate focused interventions that may enhance clinical outcomes.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Artificial Intelligence-Enabled Electrocardiography for Preoperatively Detecting Atrial Fibrillation and Mortality Risk in Patients with Sinus Rhythm
- Date Crossref
- 14/01/2026
- Éditeur
- Ivyspring International Publisher
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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National Defense Medical Center pays non établi dans la noticeÉtablissement de santé
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Cheng Hsin General Hospital pays non établi dans la noticeÉtablissement de santé
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National Defense Medical University Department of Internal Medicine pays non établi dans la noticeUniversité ou école supérieure
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College of Medicine Graduate Institute of Medical Sciences pays non établi dans la noticeUniversité ou école supérieure
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College of Biomedical Sciences Graduate Institute of Life Sciences pays non établi dans la noticeUniversité ou école supérieure
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School of Medicine Medical Technology Education Center pays non établi dans la noticeUniversité ou école supérieure
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Cheng Hsin Hospital Department of Cardiology pays non établi dans la noticeÉtablissement de santé
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Cheng Hsin Rehabilitation and Medical Center Division of Cardiovascular Surgery pays non établi dans la noticeÉtablissement de santé
National Defense Medical Center, Cheng Hsin General Hospital et Department of Internal Medicine — National Defense Medical University, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.