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1259 Development and Validation of a Machine Learning-Based Early Warning System for Predicting Venous Thromboembolism Risk in Hospitalized Lymphoma Patients Undergoing Chemotherapy: A Multicentre, Retrospective Cohort Study

Program: Oral and Poster Abstracts
Session: 332. Thrombosis and Anticoagulation: Clinical and Epidemiological: Poster I
Hematology Disease Topics & Pathways:
Research, Health outcomes research, Clinical Research, Patient-reported outcomes, Real-world evidence
Saturday, December 7, 2024, 5:30 PM-7:30 PM

Tingting Jiang1*, Zailin Yang2*, Xinyi Tang2*, Yakun Zhang2*, Shuang Chen2*, Yu Peng2*, Xiaomei Zhang, PhD2*, Li Jun, MD2*, Bingling Guo2*, Jieping Li2*, Tingting Liu3*, Haike Lei4*, Zuhai Hu5*, Na Fang6*, Xia Wei7*, Xuefen Liu8*, Yong Chen9* and Yao Liu10

1Department of Hematology-Oncology, Chongqing University Cancer Hospital, Chongqing, AL, China
2Department of Hematology-Oncology, Chongqing University Cancer Hospital, Chongqing, China
3Chongqing University Cancer Hospital, Department of Hematology-Oncology, Chongqing, China
4Department of Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, China
5School of Public Health, Chongqing Medical University, Chongqing, China
6Chonging Public Health Medical Center, Chongqing, China
7Department of Hematology, the Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
8The People's Hospital of Rongchang District, Chongqing, Chongqing, China
9The People’s Hospital of Rongchang District, Chongqing, Chongqing, China
10Chongqing University Cancer Hospital, Chongqing, China

Background: Hospitalized lymphoma patients undergoing chemotherapy face a heightened risk of Venous thromboembolism (VTE) due to prolonged treatment and extended bed rest. However, predicting VTE risk early in these patients is challenging. The objective of this study was to develop a machine learning-based early warning system for VTE tailored to this patient group.

Methods: Data were collected from 1,141 patients at four academic medical centers from January 2019 to February 2024. Twelve variables were included and six machine learning algorithms were utilized to develop a VTE early warning system. Models were evaluated based on accuracy, precision, sensitivity, specificity, and area under the curve (AUC). Subsequently, the variables importance of each model was analyzed and comprehensively evaluated by permutation importance analysis. Finally, we visualized the VTE early warning system (VTE-EWS) using a nomogram and compared its performance to the classic Khorana Score (KS) system.

Results: Among the 1141 patients, 799 from Chongqing University Cancer Hospital were evaluated as a training set, while 342 from other three academic hospitals were the external validation set. In the external validation set, all six models demonstrated strong predictive performance, with accuracy ranging from 0.71 to 0.87, AUC from 0.78 to 0.84, sensitivity from 0.62 to 0.73, and specificity from 0.73 to 0.90. Subsequently, we identified the six most important variables—white blood cell (WBC), D-dimer, central venous catheter (CVC), age, chemotherapy cycles, and Eastern Cooperative Oncology Group performance status (ECOG score)—to be included in the nomogram for scoring and visually predicting the risk of VTE. Finally, compared to the KS, VTE-EWS identified more patients at VTE high risk (24/37) and demonstrated a higher probability of clinical benefit within a range of 1% to 78%.

Conclusions: We developed a visual and online VTE-EWS based on machine learning algorithms. Based on simple indicators, VTE-EWS can accurately identify, visually predict, and interpret the risk of VTE in hospitalized lymphoma patients undergoing chemotherapy.

Disclosures: No relevant conflicts of interest to declare.

*signifies non-member of ASH