Missing-data–aware machine learning prediction of in-hospital major adverse cardiovascular events after primary percutaneous coronary intervention for ST-segment elevation myocardial infarction
Rattachement africain : ir, az, ru, ca. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Machine learning (ML) offers opportunities to improve prognostication after ST-segment elevation myocardial infarction (STEMI), but real-world registries frequently contain incomplete data, and inappropriate handling of missingness may degrade performance. We retrospectively evaluated 659 consecutive STEMI patients undergoing PCI during the index admission. The primary outcome was in-hospital major adverse cardiovascular events (MACE). Eighty clinical, electrocardiographic, laboratory, echocardiographic, and angiographic variables were analyzed, with up to 40% missingness in some predictors. A hybrid feature-selection approach incorporating CatBoost feature importance, SHAP values, variance filtering, and correlation screening identified the most informative predictors. Models were trained using stratified 5-fold cross-validation, comparing native missing-value handling in CatBoost with transformer-based imputation (TabImpute) and a pretrained tabular model (TabPFN). MACE occurred in 282 patients (42.8%). CatBoost trained directly on incompletely observed data achieved the best performance using approximately 8-10 predictors (AUC 0.73), with balanced accuracy 0.69, precision 0.68, and recall 0.53. Transformer-based imputation did not improve discrimination. SHAP analysis indicated that impaired pre-PCI TIMI flow, reduced LVEF, elevated troponin, greater ST-segment deviation, inflammation, and renal dysfunction were the strongest contributors. A class-weighted CatBoost model for in-hospital mortality achieved excellent accuracy (AUC = 0.93). These findings support the use of missing-data-aware ML methods for outcome prediction in STEMI registries and warrant external validation.
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
- Missing-data–aware machine learning prediction of in-hospital major adverse cardiovascular events after primary percutaneous coronary intervention for ST-segment elevation myocardial infarction
- Date Crossref
- 02/06/2026
- Éditeur
- Springer Science and Business Media LLC
- 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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Tabriz University of Medical Sciences Cardiovascular Research Center pays non établi dans la noticeUniversité ou école supérieure
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Azerbaijan Medical University Department of Pharmaceutical Chemistry pays non établi dans la noticeUniversité ou école supérieure
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Institute of Solution Chemistry pays non établi dans la noticeStructure de recherche
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University of Toronto pays non établi dans la noticeUniversité ou école supérieure
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University of Tabriz pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Applied Science and Engineering Department of Mechanical & Industrial Engineering pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Electrical and Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Pharmacy pays non établi dans la noticeUniversité ou école supérieure
Cardiovascular Research Center — Tabriz University of Medical Sciences, Department of Pharmaceutical Chemistry — Azerbaijan Medical University et Institute of Solution Chemistry, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.