Application of machine learning for delirium prediction and analysis of associated factors in hospitalized COVID-19 patients: A comparative study using the Korean Multidisciplinary cohort for delirium prevention (KoMCoDe).

Background: The incidence of delirium in hospitalized coronavirus disease 2019 (COVID-19) patients is linked to adverse health outcomes. Predicting the occurrence and risk factors of delirium is key to preventing its sudden onset.

Aims: To explore the factors associated with delirium in hospitalized COVID-19 patients and to compare the performance of various machine learning (ML) techniques for future use in predicting delirium.

Methods: We analyzed a dataset of 1,031 cases from two healthcare centers, which included 178 variables such as demographics, clinical data, and medication information. The ML techniques used in this study were extreme gradient boosting (XGB), light gradient boosting machine (LGBM), logistic regression (LR), random forest (RF), and support vector machine (SVM).

Results: The RF model emerged as the most effective for predicting delirium, achieving an area under the curve (AUC) of 0.923. It showed a sensitivity of 0.639, accuracy of 0.900, specificity of 0.934, positive predictive value (PPV) of 0.561, negative predictive value (NPV) of 0.952, and an F1 score of 0.597. The RF model identified key variables related to delirium, including medication type (antipsychotic, sedative, opioid), duration of hospital stay, remdesivir usage, and patient age. The reliability of the model was affirmed through calibration plots and Brier score evaluations.

Conclusions: This research developed and validated an RF-based ML model for predicting delirium in hospitalized COVID-19 patients. The model demonstrates superior accuracy and reliability compared to other ML methods and would possibly serve as a valuable tool for managing and anticipating delirium in COVID-19 patients, with the potential to enhance patient outcomes.