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