Machine learning models to predict ICU admission of patients with COPD and depression: A retrospective cohort study

This study aimed to develop and validate a machine learning (ML)-based predictive model to identify risk factors associated with intensive care unit (ICU) admission among patients with comorbid Chronic Obstructive Pulmonary Disease (COPD) and depression. Data of patients diagnosed with both COPD and depression were extracted from the MIMIC-IV version 3.1 database. A total of 1121 patients with first-time hospitalization and comorbid COPD and depression were included. Feature selection was performed using the Boruta algorithm. Five ML algorithms were employed to construct prediction models. Model performance was evaluated using the receiver operating characteristic curve, area under the curve, calibration curves, and decision curve analysis. Shapley additive explanations (SHAP) plots were used to interpret the contribution of each feature to the model’s predictions. Sixteen variables identified by the Boruta algorithm were used to build the predictive models, including chloride, prothrombin time, phosphorus, bicarbonate, international normalized ratio, mean corpuscular hemoglobin (MCH) concentration (MCHC), mean corpuscular hemoglobin, hemoglobin, hematocrit (HCT), total calcium, paraplegia, cerebrovascular disease, smoker status, invasivevent, sepsis, and acute kidney injury (AKI). The Random Forest model performed the best: specificity (0.948), positive predictive value (0.924), precision (0.924), F1 score (0.887), balanced accuracy (0.901), sensitivity (0.853), negative predictive value (0.898), and recall (0.853). SHAP analysis indicated that AKI (0.212), sepsis (0.092), and invasivevent (0.073) were the most influential predictors, followed by anemia-related features such as hemoglobin (0.023), HCT (0.023), and MCHC (0.022). ML analysis revealed that AKI, sepsis, invasivevent, and anemia-related indicators are key risk factors for ICU admission among COPD patients with depression. However, it is important to note that the findings lack external validation, which warrants further investigation in diverse patient populations.