Albertinen-Haus Geriatrics Centre, University of Hamburg, Hamburg, Germany.
J Nutr Health Aging. 2013 Mar;17(3):264-9. doi: 10.1007/s12603-012-0390-8.
In-hospital falls in older patients are frequent, but the identification of patients at risk of falling is challenging. Aim of this study was to improve the identification of high-risk patients. Therefore, a simplified screening-tool was developed, validated, and compared to the STRATIFY predictive accuracy.
Retrospective analysis of 4,735 patients; evaluation of predictive accuracy of STRATIFY and its single risk factors, as well as age, gender and psychotropic medication; splitting the dataset into a learning and a validation sample for modelling fall-risk screening and independent, temporal validation.
Geriatric clinic at an academic teaching hospital in Hamburg, Germany.
4,735 hospitalised patients ≥65 years.
Sensitivity, specificity, positive and negative predictive value, Odds Ratios, Youden-Index and the rates of falls and fallers were calculated.
There were 10.7% fallers, and the fall rate was 7.9/1,000 hospital days. In the learning sample, mental alteration (OR 2.9), fall history (OR 2.1), and insecure mobility (Barthel-Index items 'transfer' + 'walking' score = 5, 10 or 15) (OR 2.3) had the most strongest association to falls. The LUCAS Fall-Risk Screening uses these risk factors, and patients with ≥2 risk factors contributed to the high-risk group (30.9%). In the validation sample, STRATIFY SENS was 56.8, SPEC 59.6, PPV 13.5 and NPV 92.6 vs. LUCAS Fall-Risk Screening was SENS 46.0, SPEC 71.1, PPV 14.9 and NPV 92.3.
Both the STRATIFY and the LUCAS Fall-Risk Screening showed comparable results in defining a high-risk group. Impaired mobility and cognitive status were closely associated to falls. The results do underscore the importance of functional status as essential fall-risk factor in older hospitalised patients.
老年患者住院期间经常发生跌倒，但识别跌倒高风险患者具有挑战性。本研究旨在提高识别高危患者的能力。因此，开发了一种简化的筛查工具，并对其进行了验证，并与 STRATIFY 的预测准确性进行了比较。
对 4735 名患者进行回顾性分析；评估 STRATIFY 及其单一危险因素（年龄、性别和精神药物）、年龄、性别和精神药物的预测准确性，以及将数据集分为学习样本和验证样本，用于建模跌倒风险筛查和独立、时间验证。
德国汉堡的一家学术教学医院的老年科诊所。
4735 名≥65 岁的住院患者。
计算敏感性、特异性、阳性和阴性预测值、优势比、Youden 指数以及跌倒和跌倒者的发生率。
有 10.7%的跌倒者，跌倒率为 7.9/1000 住院日。在学习样本中，精神状态改变（OR 2.9）、跌倒史（OR 2.1）和不稳定的活动能力（Barthel 指数项目“转移”+“行走”评分=5、10 或 15）（OR 2.3）与跌倒最相关。LUCAS 跌倒风险筛查使用这些危险因素，≥2 个危险因素的患者归入高风险组（30.9%）。在验证样本中，STRATIFY SENS 为 56.8%，SPEC 为 59.6%，PPV 为 13.5%，NPV 为 92.6%，而 LUCAS 跌倒风险筛查为 SENS 为 46.0%，SPEC 为 71.1%，PPV 为 14.9%，NPV 为 92.3%。
STRATIFY 和 LUCAS 跌倒风险筛查在定义高风险组方面都具有相当的结果。活动能力受损和认知状态与跌倒密切相关。这些结果强调了功能状态作为老年住院患者重要的跌倒危险因素的重要性。
