Division of Nephrology, University of California, San Francisco.
Nephrology Section, San Francisco Veterans Affairs Medical Center, California.
J Gerontol A Biol Sci Med Sci. 2019 Feb 15;74(3):380-386. doi: 10.1093/gerona/gly206.
Understanding how components of frailty change over time and how they can be modeled as time-dependent predictors of mortality could lead to better risk prediction in the dialysis population.
We measured frailty at baseline, 12 months, and 24 months among 727 patients receiving hemodialysis in Northern California and Atlanta. We examined the likelihood of meeting frailty components (weight loss, exhaustion, low physical activity, weak grip strength, and slow gait speed) as a function of time in logistic regression analysis and association of frailty components with mortality in time-updated multivariable Cox models.
Physical activity and gait speed declined, exhaustion and grip strength did not change, and the odds of meeting the weight loss criterion declined with time. All five components were associated with higher mortality in multivariable analyses, but gait speed was the strongest individual predictor. All frailty components except physical inactivity were independently associated with mortality when all five components were included in the same model. The number of frailty components met was associated with mortality in a gradient that ranged from a hazard ratio of 2.73 for one component to 10.07 for five components met; the model including all five components was the best model based on Akaike information criterion.
Measurement of all frailty components was necessary for optimal mortality prediction, and the number of components met was strongly associated with mortality in this cohort.
了解衰弱的各个组成部分随时间的变化情况,以及如何将其建模为死亡率的时变预测因子,可能会提高透析人群的风险预测能力。
我们在加利福尼亚北部和亚特兰大的 727 名接受血液透析的患者中,在基线、12 个月和 24 个月时测量了衰弱情况。我们在逻辑回归分析中检查了随着时间的推移满足衰弱成分(体重减轻、疲惫、体力活动减少、握力弱和步态速度慢)的可能性,并在时间更新的多变量 Cox 模型中检查了衰弱成分与死亡率的关联。
体力活动和步态速度下降,疲惫和握力没有变化,满足体重减轻标准的几率随着时间的推移而下降。在多变量分析中,所有五个组成部分都与更高的死亡率相关,但步态速度是最强的个体预测因素。当所有五个组成部分都包含在同一个模型中时,除了体力活动不足外,所有衰弱组成部分都与死亡率独立相关。符合衰弱标准的组成部分数量与死亡率呈梯度相关,从一个组成部分的危险比 2.73 到五个组成部分的危险比 10.07;包含所有五个组成部分的模型是基于赤池信息量准则的最佳模型。
为了进行最佳的死亡率预测,需要测量所有的衰弱成分,并且符合衰弱标准的组成部分数量与该队列的死亡率密切相关。