Predicting medication-associated altered mental status in hospitalized patients: Development and validation of a risk model

Abstract Purpose This study presents a medication-associated altered mental status (AMS) risk model for real-time implementation in inpatient electronic health record (EHR) systems. Methods We utilized a retrospective cohort of patients admitted to 2 large hospitals between January 2012 and October...

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Bibliographic Details
Published in:American journal of health-system pharmacy 2019-06, Vol.76 (13), p.953-963
Main Authors: Muñoz, Monica A, Jeon, Nakyung, Staley, Benjamin, Henriksen, Carl, Xu, Dandan, Weberpals, Janick, Winterstein, Almut G
Format: Article
Language:English
Subjects:
Adult
Aged
Comorbidity
Consciousness Disorders - chemically induced
Consciousness Disorders - epidemiology
Consciousness Disorders - prevention & control
Electronic Health Records - statistics & numerical data
Female
Florida
Hospitalization
Humans
Intensive Care Units - statistics & numerical data
Male
Mental Disorders - chemically induced
Mental Disorders - epidemiology
Mental Disorders - prevention & control
Middle Aged
Retrospective Studies
Risk Assessment - methods
Risk Factors
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Summary:Abstract Purpose This study presents a medication-associated altered mental status (AMS) risk model for real-time implementation in inpatient electronic health record (EHR) systems. Methods We utilized a retrospective cohort of patients admitted to 2 large hospitals between January 2012 and October 2013. The study population included admitted patients aged ≥18 years with exposure to an AMS risk–inducing medication within the first 5 hospitalization days. AMS events were identified by a measurable mental status change documented in the EHR in conjunction with the administration of an atypical antipsychotic or haloperidol. AMS risk factors and AMS risk–inducing medications were identified from the literature, drug information databases, and expert opinion. We used multivariate logistic regression with a full and backward eliminated set of risk factors to predict AMS. The final model was validated with 100 bootstrap samples. Results During 194,156 at-risk days for 66,875 admissions, 262 medication-associated AMS events occurred (an event rate of 0.13%). The strongest predictors included a history of AMS (odds ratio [OR], 9.55; 95% confidence interval [CI], 5.64–16.17), alcohol withdrawal (OR, 3.34; 95% CI, 2.18–5.13), history of delirium or psychosis (OR, 3.25; 95% CI, 2.39–4.40), presence in the intensive care unit (OR, 2.53; 95% CI, 1.89–3.39), and hypernatremia (OR, 2.40; 95% CI, 1.61–3.56). With a C statistic of 0.85, among patients scoring in the 90th percentile, our model captured 159 AMS events (60.7%). Conclusion The risk model was demonstrated to have good predictive ability, with all risk factors operationalized from discrete EHR fields. The real-time identification of higher-risk patients would allow pharmacists to prioritize surveillance, thus allowing early management of precipitating factors.
ISSN:1079-2082
1535-2900
DOI:10.1093/ajhp/zxz119