Division of Pharmacovigilance I, U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Surveillance and Epidemiology, Silver Spring, MD.
Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL.
Am J Health Syst Pharm. 2019 Jun 18;76(13):953-963. doi: 10.1093/ajhp/zxz119.
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.
本研究提出了一个药物相关的精神状态改变（AMS）风险模型，以便在住院电子病历（EHR）系统中实时实施。
我们利用了 2012 年 1 月至 2013 年 10 月期间在 2 家大型医院住院的患者的回顾性队列。研究人群包括年龄≥18 岁、在住院前 5 天内接受过 AMS 风险诱导药物治疗的住院患者。AMS 事件通过 EHR 中记录的可测量的精神状态变化以及使用非典型抗精神病药物或氟哌啶醇来确定。AMS 危险因素和 AMS 诱导药物从文献、药物信息数据库和专家意见中确定。我们使用包含完整和向后消除的危险因素的多变量逻辑回归来预测 AMS。使用 100 个自举样本对最终模型进行验证。
在 66875 次入院的 194156 个高危日期间，发生了 262 次药物相关的 AMS 事件（发生率为 0.13%）。最强的预测因素包括 AMS 病史（比值比 [OR]，9.55；95%置信区间 [CI]，5.64-16.17）、酒精戒断（OR，3.34；95% CI，2.18-5.13）、谵妄或精神病病史（OR，3.25；95% CI，2.39-4.40）、入住重症监护病房（OR，2.53；95% CI，1.89-3.39）和高钠血症（OR，2.40；95% CI，1.61-3.56）。在 C 统计量为 0.85 的情况下，在得分在第 90 百分位的患者中，我们的模型捕捉到了 159 次 AMS 事件（60.7%）。
该风险模型具有良好的预测能力，所有危险因素都可以从离散的 EHR 字段中操作化。实时识别高风险患者将使药剂师能够优先进行监测，从而实现对诱发因素的早期管理。
