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Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019.
Castro, Victor M; Sacks, Chana A; Perlis, Roy H; McCoy, Thomas H.
  • Castro VM; Center for Quantitative Health, Massachusetts General Hospital, Boston, MA.
  • Sacks CA; Department of Medicine, Massachusetts General Hospital, Boston, MA.
  • Perlis RH; Center for Quantitative Health, Massachusetts General Hospital, Boston, MA.
  • McCoy TH; Center for Quantitative Health, Massachusetts General Hospital, Boston, MA. Electronic address: thmccoy@partners.org.
J Acad Consult Liaison Psychiatry ; 62(3): 298-308, 2021.
Article in English | MEDLINE | ID: covidwho-1117177
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Delirium / COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: J Acad Consult Liaison Psychiatry Year: 2021 Document Type: Article Affiliation country: J.jaclp.2020.12.005

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Delirium / COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: J Acad Consult Liaison Psychiatry Year: 2021 Document Type: Article Affiliation country: J.jaclp.2020.12.005