Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?
Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?
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DOI：
10.1371/journal.pone.0146247
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Chiari L
中科院分区：
文献类型：
--
作者：
Palumbo P;Palmerini L;Bandinelli S;Chiari L
跌倒是一种常见的严重威胁老年人健康和自信的事情。评估跌倒风险是有效预防跌倒项目的一个重要方面。为了测试是否有可能优于当前的跌倒预后工具，我们分析了从976名老年受试者中收集的1010个与活动有关的变量（InCHIANTI研究）。我们训练并验证了一个数据驱动的模型，该模型可以对未来的跌倒进行概率预测。我们将模型与其他跌倒风险指标进行基准测试：跌倒史、步态速度、短物理性能电池（Guralnik et al. 1994）和基于文献的跌倒风险评估工具FRAT-up （Cattelani et al. 2015）。工具中包含的变量数量较少通常被认为是易于管理的代表。我们研究了变量数量的限制如何影响预测精度。所提出的模型与FRAT-up均具有相同的判别能力；多次跌倒的受试者工作特征（ROC）曲线下面积为0.71。他们的表现优于其他风险评分，后者报告的auc在0.64到0.65之间。因此，与常用的跌倒风险指标相比，数据驱动和基于文献的方法似乎都能更好地估计跌倒风险。准确度-简约性分析显示，具有少量预测因子（~ 1-5）的工具是次优的。增加变量的数量提高了预测精度，在~ 20-30达到平台，我们可以认为这是精度和简约之间的最佳权衡。获得这20-30个变量的值不会影响可用性，因为它们通常可用于全面的老年评估。
Falls are a common, serious threat to the health and self-confidence of the elderly. Assessment of fall risk is an important aspect of effective fall prevention programs. In order to test whether it is possible to outperform current prognostic tools for falls, we analyzed 1010 variables pertaining to mobility collected from 976 elderly subjects (InCHIANTI study). We trained and validated a data-driven model that issues probabilistic predictions about future falls. We benchmarked the model against other fall risk indicators: history of falls, gait speed, Short Physical Performance Battery (Guralnik et al. 1994), and the literature-based fall risk assessment tool FRAT-up (Cattelani et al. 2015). Parsimony in the number of variables included in a tool is often considered a proxy for ease of administration. We studied how constraints on the number of variables affect predictive accuracy. The proposed model and FRAT-up both attained the same discriminative ability; the area under the Receiver Operating Characteristic (ROC) curve (AUC) for multiple falls was 0.71. They outperformed the other risk scores, which reported AUCs for multiple falls between 0.64 and 0.65. Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators. The accuracy–parsimony analysis revealed that tools with a small number of predictors (~1–5) were suboptimal. Increasing the number of variables improved the predictive accuracy, reaching a plateau at ~20–30, which we can consider as the best trade-off between accuracy and parsimony. Obtaining the values of these ~20–30 variables does not compromise usability, since they are usually available in comprehensive geriatric assessments.