Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019.

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 record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction 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.

Our Next Pandemic Ethics Challenge? Allocating "Normal" Health Care Services.

The pandemic creates unprecedented challenges to society and to health care systems around the world. Like all crises, these provide a unique opportunity to rethink the fundamental limiting assumptions and institutional inertia of our established systems. These inertial assumptions have obscured deeply rooted problems in health care and deflected attempts to address them. As hospitals begin to welcome all patients back, they should resist the temptation to go back to business as usual. Instead, they should retain the more deliberative, explicit, and transparent ways of thinking that have informed the development of crisis standards of care. The key lesson to be learned from those exercises in rational deliberation is that justice must be the ethical foundation of all standards of care. Justice demands that hospitals take a safety-net approach to providing services that prioritizes the most vulnerable segments of society, continue to expand telemedicine in ways that improve access without exacerbating disparities, invest in community-based care, and fully staff hospitals and clinics on nights and weekends.