Background The World Falls Guidelines (WFG) propose an algorithm that classifies patients as low-, intermediate-, and high-risk. We evaluated different operationalizations of the WFG algorithm and compared its predictive performance to other screening tools for falls, namely: the American Geriatrics Society and British Geriatrics Society (AGS/BGS) algorithm, the 3KQ on their own and fall history on its own. Methods We included data from 1509 adults aged ≥65 years from the population-based Longitudinal Aging Study Amsterdam. The outcome was ≥1 fall during 1-year follow-up, which was ascertained using fall calendars. The screening tools’ items were retrospectively operationalized using baseline measures, using proxies where necessary. Results Sensitivity ranged between 30.9–48.0% and specificity ranged between 77.0–88.2%. Operationalizing the algorithm with the 3KQ instead of fall history yielded a higher sensitivity but lower specificity, whereas operationalization with the Clinical Frailty Scale (CFS) classification tree instead of Fried’s frailty criteria did not affect predictive performance. Compared to the WFG algorithm, the AGS/BGS algorithm and fall history on its own yielded similar predictive performance, whereas the 3KQ on their own yielded a higher sensitivity but lower specificity. Conclusion The WFG algorithm can identify patients at risk of a fall, especially when the 3KQ are included in its operationalization. The CFS and Fried’s frailty criteria may be used interchangeably in the algorithm’s operationalization. The algorithm performed similarly compared to other screening tools, except for the 3KQ on their own, which have higher sensitivity but lower specificity and lack clinical recommendations per risk category.

中文翻译：

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世界跌倒指南的回顾性评估 - 老年人的算法


背景 世界跌倒指南 （WFG） 提出了一种将患者分为低、中和高风险的算法。我们评估了 WFG 算法的不同操作化，并将其预测性能与其他跌倒筛查工具进行了比较，即：美国老年病学会和英国老年病学会 （AGS/BGS） 算法、3KQ 本身和跌倒历史。方法 我们纳入了来自基于人群的阿姆斯特丹纵向老龄化研究的 1509 名 ≥65 岁成年人的数据。结局是在 1 年随访期间 ≥1 次跌倒，这是使用秋季日历确定的。筛选工具的项目使用基线措施进行回顾性操作，必要时使用代理。结果灵敏度在 30.9-48.0% 之间，特异性在 77.0-88.2% 之间。使用 3KQ 而不是跌倒病史操作算法产生了更高的敏感性但较低的特异性，而使用临床衰弱量表 （CFS） 分类树而不是 Fried 的衰弱标准进行操作不会影响预测性能。与 WFG 算法相比，AGS/BGS 算法和跌倒病史本身产生了相似的预测性能，而 3KQ 本身产生了更高的敏感性，但特异性较低。结论WFG 算法可以识别有跌倒风险的患者，尤其是当 3KQ 包含在其操作化中时。CFS 和 Fried 的衰弱标准可以在算法的可操作化中互换使用。与其他筛查工具相比，该算法的性能相似，除了 3KQ 本身，它们具有更高的敏感性但较低的特异性，并且缺乏每个风险类别的临床推荐。