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
BackgroundLymphoma 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) tail...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1566905/full |
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| author | Tingting Jiang Zailin Yang Xinyi Tang Xinyi Tang Na Fan Zuhai Hu Jieping Li Tingting Liu Yu Peng Shuang Chen Bingling Guo Xiaomei Zhang Yong Chen Jun Li Dehong Huang Jun Liu Yakun Zhang Yakun Zhang Xuefen Liu Xia Wei Zhanshu Liu Haike Lei Yao Liu |
| author_facet | Tingting Jiang Zailin Yang Xinyi Tang Xinyi Tang Na Fan Zuhai Hu Jieping Li Tingting Liu Yu Peng Shuang Chen Bingling Guo Xiaomei Zhang Yong Chen Jun Li Dehong Huang Jun Liu Yakun Zhang Yakun Zhang Xuefen Liu Xia Wei Zhanshu Liu Haike Lei Yao Liu |
| author_sort | Tingting Jiang |
| collection | DOAJ |
| description | BackgroundLymphoma 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.MethodsData 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).ResultsThe 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.ConclusionsThe VTE-EWS leverages simple clinical indicators to quickly and visually predict VTE risk, enabling precise and targeted interventions for lymphoma patients hospitalized undergoing chemotherapy. |
| format | Article |
| id | doaj-art-3a4752076e3748cf9faa65bc44ef9937 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-3a4752076e3748cf9faa65bc44ef99372025-08-20T03:41:46ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-08-011510.3389/fonc.2025.15669051566905Development 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 studyTingting Jiang0Zailin Yang1Xinyi Tang2Xinyi Tang3Na Fan4Zuhai Hu5Jieping Li6Tingting Liu7Yu Peng8Shuang Chen9Bingling Guo10Xiaomei Zhang11Yong Chen12Jun Li13Dehong Huang14Jun Liu15Yakun Zhang16Yakun Zhang17Xuefen Liu18Xia Wei19Zhanshu Liu20Haike Lei21Yao Liu22Department of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaSchool of Medicine, Chongqing University, Chongqing, ChinaDepartment of Medical Administration, Chongqing Public Health Medical Center, Chongqing, ChinaSchool of Public Health, Chongqing Medical University, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Oncology, The People’s Hospital of Rongchang District, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaSchool of Medicine, Chongqing University, Chongqing, ChinaDepartment of Oncology, The People’s Hospital of Rongchang District, Chongqing, ChinaDepartment of Hematology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaThe Affiliated Department of Hematology, Yongchuan Hospital of Chongqing Medical University, Chongqing, ChinaChongqing Cancer Multi-Omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hematology-Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, ChinaBackgroundLymphoma 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.MethodsData 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).ResultsThe 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.ConclusionsThe VTE-EWS leverages simple clinical indicators to quickly and visually predict VTE risk, enabling precise and targeted interventions for lymphoma patients hospitalized undergoing chemotherapy.https://www.frontiersin.org/articles/10.3389/fonc.2025.1566905/fullvenous thromboembolismmachine learninglymphomapredictionearly warning |
| spellingShingle | Tingting Jiang Zailin Yang Xinyi Tang Xinyi Tang Na Fan Zuhai Hu Jieping Li Tingting Liu Yu Peng Shuang Chen Bingling Guo Xiaomei Zhang Yong Chen Jun Li Dehong Huang Jun Liu Yakun Zhang Yakun Zhang Xuefen Liu Xia Wei Zhanshu Liu Haike Lei Yao Liu 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 Frontiers in Oncology venous thromboembolism machine learning lymphoma prediction early warning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | venous thromboembolism machine learning lymphoma prediction early warning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1566905/full |
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