Development and internal validation of a clinical prediction model for venous thromboembolism in patients with renal insufficiency

Renal insufficiency (RI) patients have an increased risk of venous thromboembolism (VTE), but validated risk assessment models tailored to this population are scarce. We conducted this study to provide a personalized VTE risk assessment for patients with RI. A total of 698 patients with RI hospitalized at the First Affiliated Hospital of Kunming Medical University from January 2022 to December 2023 were included in the study, including 142 with VTE and 556 without. Multiple imputation techniques and the Synthetic Minority Oversampling Technique were used to address the missing data and class imbalance, respectively. We used LASSO regression, random forest, and bidirectional stepwise logistic regression for feature selection and developed a logistic regression prediction model for VTE in RI patients. We identified age, length of hospital stays, lower mean arterial pressure, previous VTE, fever, lower limb pain, higher RDW-CV, D-dimer, and central venous catheterization as independent predictors for VTE in RI. The model demonstrated good discrimination and calibration, with a sensitivity of 0.79, specificity of 0.74, AUC of 0.82 (95% CI: 0.80–0.85), F1 score of 0.77, and Brier score of 0.17. The Spiegelhalter Z-test showed a p-value of 0.556, and decision curve analysis confirmed clinical applicability. Internal validation with Bootstrap resamples affirmed its stability. Our model helps identify high-risk individuals who may benefit from thromboprophylaxis. Large-scale, multicenter studies are needed to validate these findings.