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

Monica A Muñoz; Nakyung Jeon; Benjamin Staley; Carl Henriksen; Dandan Xu; Janick Weberpals; Almut G Winterstein

doi:10.1093/ajhp/zxz119

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

Am J Health Syst Pharm. 2019 Jun 18;76(13):953-963. doi: 10.1093/ajhp/zxz119.

Authors

Monica A Muñoz^{1

2}, Nakyung Jeon³, Benjamin Staley⁴, Carl Henriksen², Dandan Xu², Janick Weberpals⁵, Almut G Winterstein^{2

6}

Affiliations

¹ Division of Pharmacovigilance I, U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Surveillance and Epidemiology, Silver Spring, MD.
² Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL.
³ Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT.
⁴ Department of Pharmacy Service, University of Florida Health Shands Hospital, Gainesville, FL.
⁵ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁶ Department of Epidemiology, College of Public Health and Health Professionals and College of Medicine, University of Florida, Gainesville, FL.

PMID: 31361885
DOI: 10.1093/ajhp/zxz119

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.

Keywords: altered mental status; electronic health records; prediction model; risk score.

Publication types

Multicenter Study
Validation Study

MeSH terms

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