A Gradient Boosting model outperformed traditional logistic regression in predicting SICU readmission, achieving an AUROC of 0.80 (95% CI: 0.73-0.86) in external validation.
Cohort (n=982)
Yes
Do machine learning models improve the prediction of surgical intensive care unit readmission compared to traditional regression methods in adult SICU patients?
A Gradient Boosting machine learning model accurately predicts surgical ICU readmission, outperforming traditional logistic regression methods.
Effect estimate: AUROC 0.80 (95% CI 0.73-0.86)
BACKGROUND Patients readmitted to the surgical intensive care unit (SICU) face a high risk of mortality and increased hospital costs. Identifying patients at risk of SICU readmission is crucial. This study aims to develop a machine-learning (ML) model to predict SICU readmission. METHODS This is a retrospective study based on data collected from the electronic healthcare records of Chang Gung Memorial Hospital. The model development cohort included adult patients admitted to the SICU from July 2020 to December 2022 at the Kaohsiung branch, while the external validation cohort consisted of patients admitted to the SICU from January 2023 to August 2023 at the Linkou branch. Various ML models, including Logistic Regression (LR), Random Forest, Gradient Boosting (GB), Artificial Neural Networks, and Support Vector Machines, were compared to determine the best model. RESULTS Of the 982 patients in the development cohorts, 68 (6.9%) experienced SICU readmission. The GB model outperformed other methods, achieving an AUROC of 0.82 (95% CI: 0.70-0.93) in the internal validation cohort. Eleven features significantly influence SICU readmission, with the central venous catheter usage days, the pre-ICU stay duration, blood urea nitrogen, and carbapenem usage days ranking as the top four important factors. The GB model also surpasses three previously published traditional logistic regression methods in the external validation cohort, with AUROCs of 0.80 (95% CI: 0.73-0.86), 0.73 (95% CI: 0.63-0.83), 0.70 (95% CI: 0.60-0.79), and 0.65 (95% CI: 0.50-0.73), respectively. CONCLUSION Machine learning models offer greater accuracy and reliability compared to traditional regression methods when predicting SICU readmission.
Lin et al. (Thu,) conducted a cohort in Surgical intensive care unit (SICU) readmission (n=982). Gradient Boosting machine-learning model vs. Traditional logistic regression methods was evaluated on Prediction of SICU readmission (AUROC) (AUROC 0.80, 95% CI 0.73-0.86). A Gradient Boosting model outperformed traditional logistic regression in predicting SICU readmission, achieving an AUROC of 0.80 (95% CI: 0.73-0.86) in external validation.