To construct a risk prediction and column chart model for venous thromboembolism (VTE) in patients with nephrotic syndrome and provide reference for VTE prevention.

To use the retrospective cohort study design, the nephrotic syndrome patients who were hospitalized in Peking University Third Hospital from January 2018 to December 2022 were selected as the study subjects by convenient sampling method. Using univariate and multivariate Logistic regression analysis to analyze the risk factors for VTE in patients with nephrotic syndrome, establish a risk prediction model, and draw a column chart. The receiver operating characteristic (ROC) working curve and Hosmer Lemeshow test were used to verify the predictive performance of the model.

Among the 279 collected patients,187 males and 92 females, aged (54.25 ± 16.29) years, 43 cases developed thrombosis, with an incidence rate of 15.4%. The results of univariate analysis showed that different genders, ages, activity ability, alcohol consumption history, use of diuretics, albumin, hematocrit, fibrinolytic products, activated partial thromboplastin, D-dimer quantification and glomerular filtration rate showed differences in the occurrence of VTE in patients with nephrotic syndrome (χ²=4.22, 4.62, 12.30, Z values were -5.73 to 6.07, t=-2.07,all P<0.05). The results of multivariate Logistic regression analysis showed that age, whether diuretics were used, activated partial thromboplastin, D-dimer and glomerular filtration rate were independent influencing factors for VTE (OR values were 0.913- 3.285, all P<0.05). The above factors were five independent variables to construct a column chart. The area under the ROC curve of the model was 0.810, and the maximum value of the Jordan index was 0.518, the sensitivity was 66.67% and the specificity was 85.15%. The Hosmer-Lemeshow goodness-of-fit test showed that the model fit well (χ²=12.00, P=0.151).

The constructed column chart can personalized predict the risk of thrombosis in patients with nephrotic syndrome and help nursing staff in quickly identifying high-risk patients for thrombosis and taking corresponding intervention measures in a timely manner.

Nephrotic syndrome; Nomograms; ROC curve; Venous thromboembolism; Risk prediction model