BACKGROUND Approaches for quantifying physical resilience in older adults have not been described. METHODS We apply 2 conceptual approaches to defining physical resilience to existing longitudinal datasets in which outcomes are measured after an acute physical stressor. A "recovery phenotype" approach uses statistical methods to describe how quickly and completely a patient recovers. Statistical methods using a recovery phenotype approach can consider multiple outcomes simultaneously in a composite score (e.g., factor analysis, principal components analysis) or identify groups of patients with similar recovery trajectories across multiple outcomes (e.g., latent class profile analysis). An "expected recovery differential" approach quantifies how patients' actual outcomes compare to their predicted outcome based on a population-derived model and their individual clinical characteristics at the time of the stressor. RESULTS Application of the approaches identified different participants as being the most or least physically resilient. In the viral respiratory cohort (n=186) weighted kappa for agreement across resilience quartiles was 0.37 (0.27-0.47). The expected recovery differential approach identified a group with more co-morbidities and lower baseline function as highly resilient. In the hip fracture cohort (n=541), comparison of the expected recovery differentials across 10 outcome measures within individuals provided preliminary support for the hypothesis that there is a latent resilience trait at the whole-person level. CONCLUSIONS We posit that recovery phenotypes may be useful in clinical applications such as prediction models because they summarize the observed outcomes across multiple measures. Expected recovery differentials offer insight into mechanisms behind physical resilience not captured by age and other co-morbidities.

中文翻译：

---

纵向数据中物理弹性的两种分类和量化方法。


背景技术尚未描述用于量化老年人身体弹性的方法。方法 我们应用两种概念方法来定义现有纵向数据集的身体复原力，其中在急性身体压力源后测量结果。 “恢复表型”方法使用统计方法来描述患者恢复的速度和完全程度。使用恢复表型方法的统计方法可以在综合评分中同时考虑多个结果（例如，因子分析、主成分分析），或识别多个结果中具有相似恢复轨迹的患者组（例如，潜在类别概况分析）。 “预期恢复差异”方法量化了患者的实际结果与基于人群衍生模型及其在压力源时的个体临床特征的预测结果的比较。结果 这些方法的应用确定了不同的参与者的身体恢复能力最强或最弱。在病毒呼吸队列 (n=186) 中，弹性四分位数一致性的加权 kappa 为 0.37 (0.27-0.47)。预期恢复差异方法将具有更多合并症和较低基线功能的群体识别为具有高弹性。在髋部骨折队列中（n = 541），对个体内 10 种结果指标的预期恢复差异进行比较，为整个人水平上存在潜在复原力特征的假设提供了初步支持。结论我们认为恢复表型可能在预测模型等临床应用中有用，因为它们总结了多种措施中观察到的结果。 预期恢复差异提供了对年龄和其他合并症未捕获的身体恢复能力背后机制的深入了解。