Ascension, St. Louis, MO, United States; Building Age-Friendly Healthcare Systems, The John A. Hartford Foundation, United States.
Clinical Quality & Advanced Analytics, Ascension Data Sciences Institute, Ascension, St. Louis, MO, United States.
Appl Nurs Res. 2020 Jun;53:151243. doi: 10.1016/j.apnr.2020.151243. Epub 2020 Feb 18.
To validate the psychometrics of the Hendrich II Fall Risk Model (HIIFRM) and identify the prevalence of intrinsic fall risk factors in a diverse, multisite population.
Injurious inpatient falls are common events, and hospitals have implemented programs to achieve "zero" inpatient falls.
Retrospective analysis of patient data from electronic health records at nine hospitals that are part of Ascension. Participants were adult inpatients (N = 214,358) consecutively admitted to the study hospitals from January 2016 through December 2018. Fall risk was assessed using the HIIFRM on admission and one time or more per nursing shift.
Overall fall rate was 0.29%. At the standard threshold of HIIFRM score ≥ 5, 492 falls and 76,800 non-falls were identified (fall rate 0.36%; HIIFRM specificity 64.07%, sensitivity 78.72%). Area under the receiver operating characteristic curve was 0.765 (standard error 0.008; 95% confidence interval 0.748, 0.781; p < 0.001), indicating moderate accuracy of the HIIFRM to predict falls. At a lower cut-off score of ≥4, an additional 74 falls could have been identified, with an improvement in sensitivity (90.56%) and reduction in specificity (44.43%).
Analysis of this very large inpatient sample confirmed the strong psychometric characteristics of the HIIFRM. The study also identified a large number of inpatients with multiple fall risk factors (n = 77,292), which are typically not actively managed during hospitalization, leaving patients at risk in the hospital and after discharge. This finding represents an opportunity to reduce injurious falls through the active management of modifiable risk factors.
验证 Hendrich II 跌倒风险模型(HIIFRM)的心理测量学特性,并确定在多样化的多地点人群中内在跌倒风险因素的流行率。
住院患者受伤性跌倒很常见,医院已实施了旨在实现“零”住院患者跌倒的计划。
对 Ascension 旗下九家医院的电子健康记录中的患者数据进行回顾性分析。参与者为 2016 年 1 月至 2018 年 12 月连续入住研究医院的成年住院患者(N=214358)。入院时和每次护理班次均使用 HIIFRM 评估跌倒风险。
总体跌倒率为 0.29%。在 HIIFRM 评分≥5 的标准阈值下,共发现 492 例跌倒和 76800 例非跌倒(跌倒率 0.36%;HIIFRM 特异性 64.07%,敏感性 78.72%)。受试者工作特征曲线下面积为 0.765(标准误 0.008;95%置信区间 0.748,0.781;p<0.001),表明 HIIFRM 预测跌倒的准确性中等。在较低的≥4 截断评分下,可以额外识别 74 例跌倒,敏感性(90.56%)提高,特异性(44.43%)降低。
对这一大规模住院患者样本的分析证实了 HIIFRM 的强大心理测量学特性。该研究还发现了大量具有多种跌倒风险因素的住院患者(n=77292),这些因素在住院期间通常未得到积极管理,使患者在医院和出院后仍处于风险之中。这一发现为通过积极管理可改变的风险因素来降低伤害性跌倒提供了机会。