Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Journal of the Academy of Consultation-Liaison Psychiatry

Background : The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts.

Objectives : To develop an incident delirium predictive model among coronavirus disease 2019 patients.

Methods : We applied supervised machine learning to electronic health records data available at the start coronavirus disease 2019 inpatients admissions at three hospitals to build an incident delirium predictive model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings.

Results : Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71-0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals.

Conclusion : Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.

Castro Victor M, Sacks Chana A, Perlis Roy H, McCoy Thomas H


COVID-19, cohort study, crisis standard of care, delirium, electronic health records, machine learning, predictive modeling