ECG-based deep learning estimation of VO2 prognosticates outcomes in colorectal surgery
Rattachement africain : us, ch. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Cardiorespiratory fitness is a key determinant of perioperative risk and informs decisions regarding operative candidacy, surgical approach, and postoperative care. In routine practice, this is often assessed pragmatically through functional capacity—such as the ability to climb two flights of stairs—or through clinician- or performance status, particularly in oncologic care.1,2 While these approaches are valuable, cardiopulmonary exercise testing (CPET) provides an objective quantification of functional capacity be measuring peak VO2. Unfortunately, routine CPET use for risk stratification is not pragmatic as CPET is resource intensive and not widely accessible. Existing risk models rely largely on comorbidities and may incompletely capture this dimension of physiologic reserve. The 12-lead electrocardiogram (ECG) is nearly universal in preoperative evaluation and contains latent physiologic information not routinely used for risk stratification. Recent advances in deep learning enable extraction of clinically meaningful signals from ECG data, including features related to functional capacity.3–5 In this study, we apply an ECG-derived fitness metric to a colorectal cancer surgical population to test a pragmatic hypothesis: that a single preoperative ECG can provide an objective, scalable surrogate of functional capacity and stratify perioperative risk and outcomes. We therefore evaluate the association between ECG-derived fitness and postoperative complications, critical care utilization, and survival.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- ECG-based deep learning estimation of VO2 prognosticates outcomes in colorectal surgery
- Date Crossref
- 20/05/2026
- Éditeur
- Oxford University Press (OUP)
- 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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Harvard University pays non établi dans la noticeUniversité ou école supérieure
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Massachusetts General Hospital Department of Surgery pays non établi dans la noticeÉtablissement de santé
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Broad Institute Cardiovascular Disease Initiative pays non établi dans la noticeOrganisation à but non lucratif
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University of Lausanne Department of Cardiology pays non établi dans la noticeUniversité ou école supérieure
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Cardiovascular Research Center pays non établi dans la noticeStructure de recherche
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Mass General Heart and Vascular Institute Cardiovascular Performance Program pays non établi dans la noticeStructure de recherche
Harvard University, Department of Surgery — Massachusetts General Hospital et Cardiovascular Disease Initiative — Broad Institute, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.