Development, validation, and clinical utility of risk prediction models for cancer-associated venous thromboembolism: A retrospective and prospective cohort study
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Le résumé fourni par la source
Objectives This study aims to develop cancer-associated venous thromboembolism (CA-VTE) risk prediction models using survival machine learning (ML) algorithms. Methods This study employed a double-cohort study design (retrospective and prospective). The retrospective cohort ( n = 1036) was used as training set (70.0%, n = 725) and internal validation set (30.0%, n = 311); while the prospective cohort ( n = 321) was used as external validation set. Seven survival ML algorithms, including COX regression, classification, regression and survival tree, random survival forest, gradient boosting survival machine tree, extreme gradient boosting survival tree, survival support vector analysis, and survival artificial neural network, were applied to train CA-VTE models. Results Univariate analysis and LASSO-COX regression both selected five predictors: age, previous VTE history, ICU/CCU, CCI, and D-dimer. The seven survival ML models (C-index: 0.709–0.760; Brier Score: 0.212–0.243) all outperformed Khorana Score (C-index: 0.632; Brier Score: 0.260) in external validation set. Among all models, the COX_DD model (COX regression + D-dimer) performed best. However, ML models and Khorana Score predicted CA-VTE risk on ≥ 7 days of hospitalization with an increase in Brier Score ≥ 0.25, showing poor calibration. Conclusions In this study, the CA-VTE risk prediction models developed in seven survival ML algorithms outperformed Khorana Score. Combining with D-dimer can improve model performance. Applying the nomogram based on the optimal COX_DD model allows oncology nurse to reassess CA-VTE risk once a week. The prediction models developed using survival ML algorithms in this study may contribute to the dynamic and accurate risk assessment of CA-VTE for cancer survivors.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Development, validation, and clinical utility of risk prediction models for cancer-associated venous thromboembolism: A retrospective and prospective cohort study
- 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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Capital Medical University Department of Gastrointestinal Oncology Surgery pays non établi dans la noticeUniversité ou école supérieure
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Peking University pays non établi dans la noticeUniversité ou école supérieure
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Beijing Shijitan Hospital pays non établi dans la noticeÉtablissement de santé
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Shanxi Medical University Operating Room pays non établi dans la noticeUniversité ou école supérieure
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Second Hospital of Shanxi Medical University pays non établi dans la noticeÉtablissement de santé
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School of Nursing Department of Adult Care pays non établi dans la noticeUniversité ou école supérieure
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School of Public Health Department of Biostatistics pays non établi dans la noticeUniversité ou école supérieure
Department of Gastrointestinal Oncology Surgery — Capital Medical University, Peking University et Beijing Shijitan Hospital, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.