ArticleFrontiers in oncology2025
Development and validation of a machine learning-based early warning system for predicting venous thromboembolism risk in hospitalized lymphoma patients undergoing chemotherapy: a multicenter and retrospective cohort study.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictors and Risk Assessment Models for Venous Thromboembolism in Patients Diagnosed with Lymphoma: A Systematic Review.Current oncology (Toronto, Ont.) · 2026Pooled it
- A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development and external validation.BMC medical informatics and decision making · 2026Article
- Risk Assessment for Venous Thrombosis in Lymphoma and Emerging Biomarkers.International journal of molecular sciences · 2026Review
- Development and validation of machine learning models for assessing the risk of postoperative venous thromboembolism in cervical cancer patients.Scientific reports · 2025Article
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21 authors.
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Abstract
Background: Lymphoma patients hospitalized for chemotherapy are at increased risk of venous thromboembolism (VTE) due to prolonged treatment and bed rest. Early prediction of VTE in this group remains challenging. This study aimed to develop a machine learning-based early warning system (VTE-EWS) tailored to these patients. Methods: Data from 1,141 lymphoma patients hospitalized for chemotherapy were retrospectively collected across four academic medical centers between February 2020 and February 2024. Twelve clinical variables were included, and six machine learning algorithms were applied to build the VTE-EWS. Models were evaluated for accuracy, sensitivity, specificity, and area under the curve (AUC). Variable importance was assessed using permutation analysis, and a nomogram was created to visualize VTE risk. The system's performance was compared with the Khorana Score (KS). Results: The training set included 799 patients from Chongqing University Cancer Hospital, with 342 patients from three other centers forming the external validation set. In external validation, all six models demonstrated strong predictive performance, with accuracies ranging from 0.71 to 0.87 and AUCs from 0.78 to 0.84. Six key variables-white blood cell count, D-dimer levels, central venous catheter use, age, chemotherapy cycles, and ECOG performance status-were selected for the nomogram to predict VTE risk visually. Patients with a predicted probability >0.7 were classified as high-risk. The VTE-EWS identified more high-risk patients and provided greater clinical benefit than the KS. Conclusions: The VTE-EWS leverages simple clinical indicators to quickly and visually predict VTE risk, enabling precise and targeted interventions for lymphoma patients hospitalized undergoing chemotherapy.
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