St Mary's General Hospital, Kitchener, Ontario, Canada.
Int J Cardiol. 2012 Feb 23;155(1):75-80. doi: 10.1016/j.ijcard.2011.01.031. Epub 2011 Feb 3.
Though the NYHA functional classification is recommended in clinical settings, concerns have been raised about its reliability particularly among older patients. The RAI 2.0 is a comprehensive assessment system specifically developed for frail seniors. We hypothesized that a prognostic model for heart failure (HF) developed from the RAI 2.0 would be superior to the NYHA classification. The purpose of this study was to determine whether a HF-specific prognostic model based on the RAI 2.0 is superior to the NYHA functional classification in predicting mortality in frail older HF patients.
Secondary analysis of data from a prospective cohort study of a HF education program for care providers in long-term care and retirement homes. Univariate analyses identified RAI 2.0 variables predicting death at 6 months. These and the NYHA classification were used to develop logistic models.
Two RAI 2.0 models were derived. The first includes six items: "weight gain of 5% or more of total body weight over 30 days", "leaving 25% or more food uneaten", "unable to lie flat", "unstable cognitive, ADL, moods, or behavioural patterns", "change in cognitive function" and "needing help to walk in room"; the C statistic was 0.866. The second includes the CHESS health instability scale and the item "requiring help walking in room"; the C statistic was 0.838. The C statistic for the NYHA scale was 0.686.
These results suggest that data from the RAI 2.0, an instrument for comprehensive assessment of frail seniors, can better predict mortality than the NYHA classification.
尽管 NYHA 功能分类在临床环境中被推荐使用,但人们对其可靠性,尤其是在老年患者中,提出了一些担忧。RAI 2.0 是专门为虚弱的老年人开发的综合评估系统。我们假设,从 RAI 2.0 开发的心力衰竭(HF)预后模型将优于 NYHA 分类。本研究的目的是确定基于 RAI 2.0 的 HF 特定预后模型是否优于 NYHA 功能分类,以预测虚弱的老年 HF 患者的死亡率。
对长期护理和退休之家护理提供者 HF 教育计划的前瞻性队列研究数据进行二次分析。单变量分析确定了预测 6 个月时死亡的 RAI 2.0 变量。这些变量和 NYHA 分类被用于开发逻辑模型。
得出了两个 RAI 2.0 模型。第一个模型包括六个项目:“在 30 天内体重增加 5%或以上”、“剩余 25%或以上的食物未食用”、“无法平躺”、“认知、ADL、情绪或行为模式不稳定”、“认知功能改变”和“需要帮助在房间里行走”;C 统计量为 0.866。第二个模型包括 CHESS 健康不稳定量表和“需要帮助在房间里行走”项目;C 统计量为 0.838。NYHA 量表的 C 统计量为 0.686。
这些结果表明,来自 RAI 2.0 的数据(一种用于全面评估虚弱老年人的工具)可以比 NYHA 分类更好地预测死亡率。