Choi Yoonyoung, Staley Benjamin, Henriksen Carl, Xu Dandan, Lipori Gloria, Brumback Babette, Winterstein Almut G
Center for Observational and Real-World Evidence (CORE), Merck & Co, Inc., North Wales, PA.
UF Health Shands Hospital, Gainesville, FL.
Am J Health Syst Pharm. 2018 Sep 1;75(17):1293-1303. doi: 10.2146/ajhp180013. Epub 2018 Jul 23.
Construction and validation of a fall risk prediction model specific to inpatients receiving fall risk-increasing drugs (FRIDs) are described.
In a retrospective cohort study of 75,036 admissions to 2 hospitals over a designated 22-month period that involved FRID exposure during the first 5 hospital days, factors influencing fall risk were investigated via logistic regression. The resultant risk prediction model was internally validated and its performance compared with that of a model based on Morse Fall Scale (MFS) scores.
A total of 220,904 patient-days of FRID exposure were evaluated. The three most commonly administered FRIDs were oxycodone (given on 79,697 patient-days, 36.08%), morphine (52,427, 23.73%) and hydromorphone (42,063, 19.04%). Within the 90th percentile of modeled risk scores, 144 of the 466 documented falls (30.9%) were captured by the developed risk prediction model (unbiased C statistic, 0.69), as compared with 94 falls (20.2%) captured using the MFS model (unbiased C statistic, 0.62). Strong predictors of inpatient falls included a history of falling (odds ratio [OR], 1.99; 95% confidence interval (CI), 1.42-2.80); overestimation of ability to ambulate (OR, 1.53; 95% CI, 1.12-2.09); and "comorbidity predisposition," a composite measure encompassing a history of falling and 11 past diagnoses (OR, 1.60; 95% CI, 1.30-1.97).
The proposed risk model for inpatient falls achieved superior predictive performance when compared with the MFS model. All risk factors were operationalized from discrete electronic health record fields, allowing full automation of real-time identification of high-risk patients.
描述针对接受增加跌倒风险药物（FRIDs）的住院患者的跌倒风险预测模型的构建与验证。
在一项对两家医院在指定的22个月期间内的75,036例入院病例进行的回顾性队列研究中，研究了在前5个住院日期间使用FRIDs的情况，并通过逻辑回归分析了影响跌倒风险的因素。对所得的风险预测模型进行了内部验证，并将其性能与基于莫尔斯跌倒量表（MFS）评分的模型进行了比较。
共评估了220,904个患者使用FRIDs的天数。三种最常用的FRIDs是羟考酮（79,697个患者使用天数，占36.08%）、吗啡（52,427个，占23.73%）和氢吗啡酮（42,063个，占19.04%）。在建模风险评分的第90百分位数范围内，已记录的466次跌倒中有144次（30.9%）被开发的风险预测模型捕捉到（无偏C统计量为0.69），而使用MFS模型捕捉到的跌倒为94次（20.2%）（无偏C统计量为0.62）。住院患者跌倒的强预测因素包括跌倒史（比值比[OR]，1.99；95%置信区间[CI]，1.42 - 2.80）；对行走能力的高估（OR，1.53；95% CI，1.12 - 2.09）；以及“合并症易感性”，这是一种综合指标，包括跌倒史和11种既往诊断（OR，1.60；95% CI，1.30 - 1.97）。
与MFS模型相比，所提出的住院患者跌倒风险模型具有更好的预测性能。所有风险因素均从离散的电子健康记录字段中得出，从而实现了对高危患者实时识别的完全自动化。
