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已发表论文
成人免疫性血小板减少症血栓预测新模型（ITP-THROMBO）的开发与验证
Authors Yan H, Hu X, Zhu L, Jiang Y, Luo C, Lv M, Wang Y, Tong J , Zheng C
Received 1 October 2025
Accepted for publication 10 January 2026
Published 21 January 2026 Volume 2026:17 570135
DOI https://doi.org/10.2147/JBM.S570135
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Chang Kim
Huiling Yan,1 Xing Hu,1 Lijun Zhu,1 Yuhan Jiang,2 Chen Luo,2 Mengya Lv,1 Yan Wang,3 Juan Tong,1 Changcheng Zheng1
1Department of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China; 2Department of Hematology, Anhui Provincial Hospital, WanNan Medical College, Wuhu, People’s Republic of China; 3Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China
Correspondence: Changcheng Zheng, Department of Hematology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Lujiang Road No. 17, Hefei, 230001, People’s Republic of China, Tel/Fax +86-551-62282990, Email zhengchch1123@ustc.edu.cn
Purpose: Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by bleeding, yet paradoxically, it can also predispose patients to thrombotic events; therefore, investigating high-risk factors for thrombosis in ITP patients and developing a predictive model is essential.
Patients and Methods: A total of 1112 patients were diagnosed with ITP between January 2018 and December 2023. Excluding 216 patients under the age of 18, a total of 896 adult ITP patients were included in this study (of whom 101 developed thrombosis). Patients were randomly allocated to the training set (n=628) and validation set (n=268) in a 7:3 ratio.
Results: Atrial fibrillation, peripheral vascular disease, venous thromboembolism history, pneumonia < 1 month, cerebrovascular events history, and D-dimer levels were identified as predictive factors for thrombosis in ITP patients. The six factors formed 57 unique combinations, providing robust predictive power for thrombosis in ITP under different clinical scenarios. In the training set, the area under the curve (AUC) for the nomogram was 0.656 (95% CI: 0.578– 0.735) to 0.931 (95% CI: 0.902– 0.960) and the AUC in the validation set was 0.539 (95% CI: 0.425– 0.653) to 0.893 (95% CI: 0.828– 0.957). The calibration curve demonstrated good concordance between the model’s predicted probabilities and actual observed probabilities, and the decision curve analysis indicated that the model had significant clinical utility.
Conclusion: This cohort study developed a simple and practical predictive model (ITP-THROMBO) for estimating thrombosis risk in ITP patients; this model facilitates rapid identification of ITP patients at high thrombotic risk, enabling timely decision-making support for personalized treatment planning.
Keywords: immune thrombocytopenia, ITP, thrombosis, prediction model
1Department of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China; 2Department of Hematology, Anhui Provincial Hospital, WanNan Medical College, Wuhu, People’s Republic of China; 3Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China
Correspondence: Changcheng Zheng, Department of Hematology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Lujiang Road No. 17, Hefei, 230001, People’s Republic of China, Tel/Fax +86-551-62282990, Email zhengchch1123@ustc.edu.cn
Purpose: Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by bleeding, yet paradoxically, it can also predispose patients to thrombotic events; therefore, investigating high-risk factors for thrombosis in ITP patients and developing a predictive model is essential.
Patients and Methods: A total of 1112 patients were diagnosed with ITP between January 2018 and December 2023. Excluding 216 patients under the age of 18, a total of 896 adult ITP patients were included in this study (of whom 101 developed thrombosis). Patients were randomly allocated to the training set (n=628) and validation set (n=268) in a 7:3 ratio.
Results: Atrial fibrillation, peripheral vascular disease, venous thromboembolism history, pneumonia < 1 month, cerebrovascular events history, and D-dimer levels were identified as predictive factors for thrombosis in ITP patients. The six factors formed 57 unique combinations, providing robust predictive power for thrombosis in ITP under different clinical scenarios. In the training set, the area under the curve (AUC) for the nomogram was 0.656 (95% CI: 0.578– 0.735) to 0.931 (95% CI: 0.902– 0.960) and the AUC in the validation set was 0.539 (95% CI: 0.425– 0.653) to 0.893 (95% CI: 0.828– 0.957). The calibration curve demonstrated good concordance between the model’s predicted probabilities and actual observed probabilities, and the decision curve analysis indicated that the model had significant clinical utility.
Conclusion: This cohort study developed a simple and practical predictive model (ITP-THROMBO) for estimating thrombosis risk in ITP patients; this model facilitates rapid identification of ITP patients at high thrombotic risk, enabling timely decision-making support for personalized treatment planning.
Keywords: immune thrombocytopenia, ITP, thrombosis, prediction model