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| 001 | 144361 | ||
| 005 | 20240229112621.0 | ||
| 024 | 7 | _ | |a 10.1093/ajhp/zxz119 |2 doi |
| 024 | 7 | _ | |a pmid:31361885 |2 pmid |
| 024 | 7 | _ | |a 1079-2082 |2 ISSN |
| 024 | 7 | _ | |a 1535-2900 |2 ISSN |
| 024 | 7 | _ | |a altmetric:62408477 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2019-01814 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Muñoz, Monica A |b 0 |
| 245 | _ | _ | |a Predicting medication-associated altered mental status in hospitalized patients: Development and validation of a risk model. |
| 260 | _ | _ | |a Bethesda, Md. |c 2019 |b Soc. |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1565098505_2959 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a This 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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| 700 | 1 | _ | |a Jeon, Nakyung |b 1 |
| 700 | 1 | _ | |a Staley, Benjamin |b 2 |
| 700 | 1 | _ | |a Henriksen, Carl |b 3 |
| 700 | 1 | _ | |a Xu, Dandan |b 4 |
| 700 | 1 | _ | |a Weberpals, Janick |0 P:(DE-He78)bf5409aed74a0923d9402c2c7ad620aa |b 5 |u dkfz |
| 700 | 1 | _ | |a Winterstein, Almut G |b 6 |
| 773 | _ | _ | |a 10.1093/ajhp/zxz119 |g Vol. 76, no. 13, p. 953 - 963 |0 PERI:(DE-600)2057140-9 |n 13 |p 953 - 963 |t American journal of health system pharmacy |v 76 |y 2019 |x 1535-2900 |
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| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 5 |6 P:(DE-He78)bf5409aed74a0923d9402c2c7ad620aa |
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| 914 | 1 | _ | |y 2019 |
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