Development and Validation of a Machine Learning-Based Early Warning System for Predicting Venous Thromboembolism Risk in Hospitalized Lymphoma Patients Undergoing Chemotherapy: a multicentre and retrospective cohort study

Tingting Jiang¹Zailin Yang¹Xinyi Tang^1,2Na Fan³Zuhai Hu⁴Jieping Li¹Tingting Liu¹Yu Peng¹Shuang Chen¹Bingling Guo¹Xiaomei Zhang¹Yong Chen⁵Jun Li¹Dehong Huang¹Jun Liu¹Yakun Zhang^1,2Xuefen Liu⁵Xia Wei⁶Zhanshu Liu⁷Haike Lei⁸Yao Liu^1*

• ¹Department of Hematology and Oncology, Cancer Hospital, Chongqing University, Chongqing, China
• ²School of Medicine, Chongqing University, Chongqing, Chongqing Municipality, China
• ³Chongqing Public Health Medical Center, Chongqing, China
• ⁴College of Public Health, Chongqing Medical University, Chongqing, Chongqing Municipality, China
• ⁵Rongchang People's Hospital, Chongqing, China
• ⁶Third Affiliated Hospital of Chongqing Medical University, Chongqing, Sichuan Province, China
• ⁷Yongchuan Hospital of Chongqing Medical University, Chongqing, China
• ⁸Cancer Hospital, Chongqing University, Chongqing, Anhui, China

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).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, Ddimer 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.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.