Daily automated prediction of delirium risk in hospitalized patients: Model development and validation.

Background: Delirium is common in hospitalized patients and correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention.

Objective: To develop a machine learning algorithm to identify patients at highest risk of delirium in the hospital each day in an automated fashion based on data available in the electronic medical record, reducing the barrier to large-scale delirium screening.

Methods: We developed and compared multiple machine learning models on a retrospective dataset of all hospitalized adult patients with recorded Confusion Assessment Method (CAM) screens at a major academic medical center from April 2nd, 2016 to January 16th 2019, comprising 23006 patients. The patient's age, gender, and all available laboratory values, vital signs, prior CAM screens, and medication administrations were used as potential predictors. Four machine learning approaches were investigated: logistic regression with L1-regularization, multilayer perceptrons, random forests, and boosted trees. Model development used 80% of the patients; the remaining 20% were reserved for testing the final models. Lab values, vital signs, medications, gender, and age were used to predict a positive CAM screen in the next 24 hours.

Results: The boosted tree model achieved the greatest predictive power, with a 0.92 area under the receiver operator characteristic curve (AUROC) (95% Confidence Interval (CI) 0.913-9.22), followed by the random forest at 0.91 (95% CI 0.909-0.918), multilayer perceptron at 0.86 (95% CI 0.850-0.861), and logistic regression at 0.85 (95% CI 0.841-0.852). These AUROCs decreased to 0.78-0.82 and 0.74-0.80 when limited to patients not currently or never delirious, respectively.

Conclusions: A boosted tree machine learning model was able to identify hospitalized patients at elevated risk for delirium in the next 24 hours. This may allow for automated delirium risk screening and more precise targeting of proven and investigational interventions to prevent delirium.

Clinicaltrial: