Journal of blood medicine · 2026
Development and Validation of a Novel Thrombosis Prediction Model for Adult Immune Thrombocytopenia (ITP-THROMBO).
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Yan Huiling, Hu Xing, Zhu Lijun, Jiang Yuhan, Luo Chen, Lv Mengya, Wang Yan, Tong Juan, Zheng Changcheng
Аннотация
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.
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.