Machine learning-based prediction model for hypoalbuminemia in ICU patients with venous thromboembolism and acute kidney injury

BackgroundHypoalbuminemia is a prevalent and critical complication among intensive care unit (ICU) patients with venous thromboembolism (VTE) and acute kidney injury (AKI), significantly associated with increased mortality and prolonged hospitalization. However, reliable tools for early prediction of hypoalbuminemia in this high-risk population remain limited. This study aimed to develop and validate a machine learning-based prediction model for hypoalbuminemia in ICU patients with coexisting VTE and AKI.MethodsIn this retrospective study, data from 585 ICU patients with VTE and AKI were analyzed. Patients were divided into a training cohort (n = 409) and a validation cohort (n = 176). Least absolute shrinkage and selection operator (LASSO) regression with standardization was applied for feature selection. Five machine learning models-logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and support vector machine (SVM)-were developed and evaluated using 5-fold cross-validation. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. Decision curve analysis (DCA), SHapley Additive exPlanations (SHAP), and feature importance were performed for the optimal model. The performance of the best machine learning model was compared with conventional ICU severity scores (APSIII, LODs, and OASIS).ResultsThe standardized LASSO regression identified ten optimal predictors: creatinine (cr), age, tumor/cancer, body mass index (BMI), low-density lipoprotein (LDL), urine output, D-dimer (D_D), Charlson comorbidity index, anticoagulant use, and alanine aminotransferase (ALT). Among the five models, XGBoost achieved the best predictive performance with a validation AUC of 0.882 [95% confidence interval (CI): 0.832–0.924], followed by RF (AUC = 0.878), LightGBM (AUC = 0.870), LR (AUC = 0.838), and SVM (AUC = 0.662). The 5-fold cross-validation AUC for XGBoost was 0.901 ± 0.042, with minimal overfitting (CV-test difference = −0.019). Decision curve analysis demonstrated favorable clinical net benefit of the XGBoost model across a wide range of threshold probabilities. SHAP analysis and feature importance consistently identified urine output, age, LDL, BMI, and D-dimer as the top contributors. Compared with conventional ICU severity scores, the XGBoost model significantly outperformed APSIII (AUC = 0.661), LODs (AUC = 0.652), and OASIS (AUC = 0.647). The logistic regression-based nomogram achieved a C-index of 0.838, providing a user-friendly bedside tool for clinicians.ConclusionThe machine learning-based XGBoost model, incorporating ten readily available clinical variables, demonstrated excellent predictive performance for hypoalbuminemia in ICU patients with VTE and AKI. This model offers a practical and interpretable tool for early risk stratification and may guide proactive nutritional and therapeutic interventions in this vulnerable population.