*These authors contributed equally to the design of the study and to the preparation of the article.
J Gerontol A Biol Sci Med Sci. 2013 Dec;68(12):1505-11. doi: 10.1093/gerona/glt053. Epub 2013 May 2.
Little is known about the contribution of frailty in improving patient-level prediction beyond readily available clinical information. The objective of this study is to compare the predictive ability of 129 combinations of seven frailty markers (cognition, energy, mobility, mood, nutrition, physical activity, and strength) and quantify their contribution to predictive accuracy beyond age, sex, and number of chronic diseases.
Two cohorts from the Established Populations for Epidemiologic Studies of the Elderly were used. The model with the best predictive fit in predicting 6-year incidence of disability was determined using the Akaike Information Criterion. Predictive accuracy was measured by the C statistic.
Incident disability was 23% in one cohort and 20% in the other cohort. The "best model" in each cohort was found to be a model including between five and seven frailty markers including cognition, mobility, nutrition, physical activity, and strength. Predictive accuracy of the 129 models ranged from 0.73 to 0.77 across both cohorts. Adding frailty markers to age, sex, and chronic disease increased predictive accuracy by up to 3% in both cohorts (p < .001). The contribution of frailty increased up to 9% in the oldest age group.
Adding frailty markers provided a modest increase in patient-level prediction of disability. Such a modest increase may still be worthwhile because while age, sex, and the number of chronic diseases are not modifiable, frailty may be. Further studies examining the contribution of frailty in improving prediction are needed before adopting frailty as a prognostic tool.
关于虚弱在改善患者水平预测方面的贡献，除了易于获得的临床信息外，人们知之甚少。本研究的目的是比较 7 种虚弱标志物（认知、能量、移动能力、情绪、营养、体力活动和力量）的 129 种组合的预测能力，并量化其对预测准确性的贡献，超越年龄、性别和慢性疾病数量。
使用来自老年人口流行病学研究的两个队列。使用赤池信息量准则确定预测残疾 6 年发生率的最佳预测拟合模型。预测准确性通过 C 统计量衡量。
一个队列的残疾发生率为 23%，另一个队列为 20%。在每个队列中，“最佳模型”都被发现是一个包含认知、移动能力、营养、体力活动和力量等 5 到 7 个虚弱标志物的模型。129 个模型的预测准确性在两个队列中均在 0.73 到 0.77 之间。在年龄、性别和慢性疾病的基础上添加虚弱标志物，在两个队列中均可提高预测准确性，最多可达 3%（p <.001）。在最年长的年龄组中，虚弱的贡献增加了 9%。
添加虚弱标志物为残疾的患者水平预测提供了适度的改善。这种适度的增加可能仍然是值得的，因为虽然年龄、性别和慢性疾病的数量是不可改变的，但虚弱可能是可以改变的。在将虚弱作为预后工具之前，需要进一步研究虚弱在改善预测方面的贡献。
