Predicting medication-associated altered mental status in hospitalized patients: Development and validation of a risk model. (18th June 2019)

• Record Type: Journal Article
• Authors: Muñoz, Monica A; Jeon, Nakyung; Staley, Benjamin; Henriksen, Carl; Xu, Dandan; Weberpals, Janick; Winterstein, Almut G
• Is Part Of: American journal of health-system pharmacy. Volume 76:Number 13(2019)
• Page Start: 953
• Page End: 963
• Publication Date: 2019-06-18
• Subjects: altered mental status -- electronic health records -- prediction model -- risk score; Hospital pharmacies -- United States -- Periodicals; 615.1
• DOI: 10.1093/ajhp/zxz119
• Languages: English
• ISSNs: 1079-2082

Abstract

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.