Predicting subclinical leaflet thrombosis in self-expandable prosthesis: A multimodal machine learning analysis
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Le résumé fourni par la source
Objective: Subclinical leaflet thrombosis is a known finding after transcatheter aortic valve implantation, but its predictors remain poorly defined. Machine learning offers new opportunities for identifying complex, nonlinear relationships among clinical, anatomic, and hematological variables. Methods: We analyzed data from 118 patients who underwent transcatheter aortic valve implantation with self-expanding valves and scheduled multidetector computed tomography at 6 months. A total of 120 preprocedural and postprocedural variables were included. Three machine learning models, least absolute shrinkage and selection operator logistic regression, Random Forest, and Extreme Gradient Boosting, were trained and internally validated using stratified 5-fold cross-validation. Results: Subclinical leaflet thrombosis was identified in 22 patients (18.6%). Bicuspid aortic valve morphology emerged as one of the strongest predictors across all machine learning models (least absolute shrinkage and selection operator β = 1.33; Gini = 1.31; SHapley Additive exPlanations = 0.42). Other top predictors included serum creatinine (β = 0.29; Gini = 0.90), hemoglobin decrease (β = 0.05; Gini = 1.32; SHapley Additive exPlanations = 0.10), hematocrit decrease (β = 0.02; Gini = 1.43; SHapley Additive exPlanations = 0.11), and platelet nadir (SHapley Additive exPlanations = 0.09). All models demonstrated strong discriminative ability (area under the curve range, 0.84-0.89; Brier scores: 0.040-0.163). Conclusions: This is the first study to apply a multimodal machine learning framework to predict subclinical leaflet thrombosis after transcatheter aortic valve implantation. Bicuspid anatomy and perioperative hematological changes were consistently associated with subclinical leaflet thrombosis, highlighting the potential of machine learning to enhance postprocedural risk stratification. Incorporating routinely available variables into machine learning models may help guide early imaging and personalized antithrombotic strategies.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Predicting subclinical leaflet thrombosis in self-expandable prosthesis: A multimodal machine learning analysis
- Date Crossref
- 01/12/2025
- Éditeur
- Elsevier BV
- 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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Maria Eleonora Hospital Department of Cardiovascular Surgery pays non établi dans la noticeÉtablissement de santé
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Imperial College London Department of Surgery & Cancer pays non établi dans la noticeUniversité ou école supérieure
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Imperial College Healthcare NHS Trust Department of Cardiothoracic Surgery pays non établi dans la noticeÉtablissement de santé
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Hammersmith Hospital pays non établi dans la noticeÉtablissement de santé
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Jagiellonian University Center for Digital Medicine and Robotics pays non établi dans la noticeUniversité ou école supérieure
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Università degli Studi eCampus pays non établi dans la noticeUniversité ou école supérieure
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Università degli Studi di Enna Kore pays non établi dans la noticeUniversité ou école supérieure
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Simhub pays non établi dans la noticeInstitution
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eCampus University Department of Theoretical and Applied Sciences pays non établi dans la noticeUniversité ou école supérieure
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Kore University of Medicine pays non établi dans la noticeUniversité ou école supérieure
Department of Cardiovascular Surgery — Maria Eleonora Hospital, Department of Surgery & Cancer — Imperial College London et Department of Cardiothoracic Surgery — Imperial College Healthcare NHS Trust, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.