Previously submitted to: JMIR Mental Health (no longer under consideration since Jan 26, 2023)
Date Submitted: Jan 26, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Machine learning-based prediction model for delirium in hospitalized patients with COVID-19: Korean multidisciplinary cohort for delirium prevention (KoMCoDe)
ABSTRACT
Background:
Coronavirus disease 2019 (COVID-19) infection is a risk factor for delirium that must be predicted and prevented to avoid adverse outcomes.
Objective:
We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19, and to identify modifiable factors to prevent delirium.
Methods:
The ML model was developed using training data from 757 patients at three medical centers and externally validated in 121 patients from a fourth medical center. The extreme gradient boosting (XGBoost) algorithm was used. A stratified K-fold approach was used to select model hyperparameters and predictor variables. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was selected as the evaluation metric.
Results:
The incidence of in-hospital delirium was 6.9% in the training cohort. Selected predictor variables for delirium were age, mechanical ventilation, medication (opioids, sedatives, antipsychotics, ambroxol, ceftriaxone, and piperacillin/tazobactam), sodium ion concentration, and white blood cell count (all p < 0.05). The stratified 5-fold AUC values for the training and test cohorts were 0.856 (95% confidence interval [CI] = 0.804–0.908) and 0.998 (CI = 0.989–1.000), respectively.
Conclusions:
We developed and externally validated the ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.
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