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.