Machine learning-based prediction model for hypoalbuminemia in ICU patients with venous thromboembolism and acute kidney injury
- 1Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China
Abstract
Background: Hypoalbuminemia 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.
Methods: In 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).
Results: The 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.
Conclusion: The 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.
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BibTeX
@article{qian2026machine,
title = {{Machine learning-based prediction model for hypoalbuminemia in ICU patients with venous thromboembolism and acute kidney injury}},
author = {Zelong Qian and Guoqing Li and Junjian Tang},
journal = {Frontiers in Medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1922098},
url = {https://doi.org/10.3389/fmed.2026.1922098},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine learning-based prediction model for hypoalbuminemia in ICU patients with venous thromboembolism and acute kidney injury
AU - Zelong Qian
AU - Guoqing Li
AU - Junjian Tang
PY - 2026
DA - 2026/10/13
JO - Frontiers in Medicine
DO - 10.3389/fmed.2026.1922098
UR - https://doi.org/10.3389/fmed.2026.1922098
AB - Background: Hypoalbuminemia 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. Methods: In 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). Results: The 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. Conclusion: The 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.
SN - 2296-858X
ER -