Frontiers in neurology · 2025
Construction of an early warning model for venous thromboembolism risk in patients with severe cerebral hemorrhage based on ultrasound spontaneous imaging.
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Ma Bei, Chen Chen, Wang Qin, Chen Xi
Аннотация
OBJECTIVE: To investigate the role of ultrasound spontaneous echo contrast (SEC) in venous thromboembolism (VTE) in patients with severe spontaneous cerebral hemorrhage (ICH) and to construct a clinical prediction model.
METHODS: A total of 69 critically ill ICH patients admitted to the Department of Critical Care Medicine of Liangjiang Hospital of Chongqing Medical University between January 2022 and March 2024 were included in the study. Datas were collected prospectively, including general information, test data, clinical outcomes, and lower limb vascular ultrasound images within 48 h of admission. The statistical analysis was conducted using SPSS 22.0, and the model was constructed using binary logistic regression analysis. The efficacy of the model was assessed using subject operating (ROC) curves and the Hosmer-Lemeshow goodness-of-fit test.
RESULTS: The SEC, Albumin and age were identified as independent risk factors for thrombosis in patients with severe ICH. The joint prediction model, constructed based on the indicators, is given by the following equation: Logit(P) = 0.477-0.216 * Albumin + 1.43 * SEC + 0.044 * age. The model demonstrated consistent predictive performance, exhibiting good discrimination (AUC = 0.900) and calibration (Hosmer-Lemeshow χ2 = 5.231, p = 0.733 > 0.05).
CONCLUSION: The ICH-VTE early warning model constructed on the basis of SEC, Albumin and age performs well and helps clinicians to dynamically assess the risk of VTE to determine the timing of anticoagulation, which provides therapeutic ideas to reduce the incidence of VTE and improve the clinical outcome of ICH.
METHODS: A total of 69 critically ill ICH patients admitted to the Department of Critical Care Medicine of Liangjiang Hospital of Chongqing Medical University between January 2022 and March 2024 were included in the study. Datas were collected prospectively, including general information, test data, clinical outcomes, and lower limb vascular ultrasound images within 48 h of admission. The statistical analysis was conducted using SPSS 22.0, and the model was constructed using binary logistic regression analysis. The efficacy of the model was assessed using subject operating (ROC) curves and the Hosmer-Lemeshow goodness-of-fit test.
RESULTS: The SEC, Albumin and age were identified as independent risk factors for thrombosis in patients with severe ICH. The joint prediction model, constructed based on the indicators, is given by the following equation: Logit(P) = 0.477-0.216 * Albumin + 1.43 * SEC + 0.044 * age. The model demonstrated consistent predictive performance, exhibiting good discrimination (AUC = 0.900) and calibration (Hosmer-Lemeshow χ2 = 5.231, p = 0.733 > 0.05).
CONCLUSION: The ICH-VTE early warning model constructed on the basis of SEC, Albumin and age performs well and helps clinicians to dynamically assess the risk of VTE to determine the timing of anticoagulation, which provides therapeutic ideas to reduce the incidence of VTE and improve the clinical outcome of ICH.