Analysis of the risk factors of deep vein thrombosis in the patients with spontaneous intracerebral hemorrhage in ICU and construction of a predictive model

Zhang Yan; Xu Chunyang; Yu Sun; Wen Shun; Ye Hongwei

doi:10.3969/j.issn.1002-1949.2024.04.011

Abstract：Objective To explore the risk factors for deep vein thrombosis(DVT)in the patients with spontaneous intracerebral hemorrhage(sICH)in the intensive care unit(ICU)and to construct a predictive model for DVT.Methods Data of 357 patients with sICH admitted to ICU of the First People′s Hospital of Changshu City,Changshu Hospital Affiliated to Soochow University from January 2019 to January 2023 were retrospectively analyzed.The patients were randomly divided into a training set(n =254)and a validation set(n =103)in a ratio of 7 ∶3.Based on whether DVT was confirmed by ultrasound during hospitalization,the patients were divided into the DVT group and the non-DVT group.Logistic univariate and multivariate regression analysis were used to identify independent risk factors for DVT based on the training set data.A nomogram predictive model was constructed,and the discrimination and goodness-of-fit of the nomogram model were evaluated by using receiver operating characteristic(ROC)curves and calibration curves,respectively.Decision curve analysis(DCA)was used to evaluate the clinical applicability of the predictive model.Results① A total of 254 patients were included in the training set,including 157 males and 97 females.The DVT group had 27 cases with an incidence rate of 10.63%.② Univariate and multivariate Logistic regression analysis showed that mechanical ventilation(OR = 3.574,P = 0.018),hematoma volume(OR =4.280,P =0.002),catheterization of femoral vein(OR =3.892,P =0.012),and coagulation factor reaction time(R)in thromboelastography(OR =0.337,P =0.021)were independent risk factors for DVT in the patients with severe sICH.③ A nomogram predictive model for estimating the risk of DVT occurrence was constructed based on the four independent risk factors.After validation,the area under the ROC curve(AUC)of the model was 0.813(95%CI 0.730-0.896),and the calibration curve approached the ideal curve.The DCA curve indicated that the predictive model had clinical utility.Conclusions The nomogram predictive model for DVT risk in the patients with severe sICH constructed based on Logistic regression analysis has good predictive performance and can achieve quantification,individualization,and visualization of prediction.It provides a reference for early clinical intervention decision-making and anticoagulation strategies for severe sICH.