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000144361 0247_ $$2doi$$a10.1093/ajhp/zxz119
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000144361 0247_ $$2ISSN$$a1079-2082
000144361 0247_ $$2ISSN$$a1535-2900
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000144361 037__ $$aDKFZ-2019-01814
000144361 041__ $$aeng
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000144361 1001_ $$aMuñoz, Monica A$$b0
000144361 245__ $$aPredicting medication-associated altered mental status in hospitalized patients: Development and validation of a risk model.
000144361 260__ $$aBethesda, Md.$$bSoc.$$c2019
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000144361 520__ $$aThis study presents a medication-associated altered mental status (AMS) risk model for real-time implementation in inpatient electronic health record (EHR) systems.We utilized a retrospective cohort of patients admitted to 2 large hospitals between January 2012 and October 2013. The study population included admitted patients aged ≥18 years with exposure to an AMS risk-inducing medication within the first 5 hospitalization days. AMS events were identified by a measurable mental status change documented in the EHR in conjunction with the administration of an atypical antipsychotic or haloperidol. AMS risk factors and AMS risk-inducing medications were identified from the literature, drug information databases, and expert opinion. We used multivariate logistic regression with a full and backward eliminated set of risk factors to predict AMS. The final model was validated with 100 bootstrap samples.During 194,156 at-risk days for 66,875 admissions, 262 medication-associated AMS events occurred (an event rate of 0.13%). The strongest predictors included a history of AMS (odds ratio [OR], 9.55; 95% confidence interval [CI], 5.64-16.17), alcohol withdrawal (OR, 3.34; 95% CI, 2.18-5.13), history of delirium or psychosis (OR, 3.25; 95% CI, 2.39-4.40), presence in the intensive care unit (OR, 2.53; 95% CI, 1.89-3.39), and hypernatremia (OR, 2.40; 95% CI, 1.61-3.56). With a C statistic of 0.85, among patients scoring in the 90th percentile, our model captured 159 AMS events (60.7%).The risk model was demonstrated to have good predictive ability, with all risk factors operationalized from discrete EHR fields. The real-time identification of higher-risk patients would allow pharmacists to prioritize surveillance, thus allowing early management of precipitating factors.
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000144361 7001_ $$aJeon, Nakyung$$b1
000144361 7001_ $$aStaley, Benjamin$$b2
000144361 7001_ $$aHenriksen, Carl$$b3
000144361 7001_ $$aXu, Dandan$$b4
000144361 7001_ $$0P:(DE-He78)bf5409aed74a0923d9402c2c7ad620aa$$aWeberpals, Janick$$b5$$udkfz
000144361 7001_ $$aWinterstein, Almut G$$b6
000144361 773__ $$0PERI:(DE-600)2057140-9$$a10.1093/ajhp/zxz119$$gVol. 76, no. 13, p. 953 - 963$$n13$$p953 - 963$$tAmerican journal of health system pharmacy$$v76$$x1535-2900$$y2019
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000144361 9141_ $$y2019
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