Knight Silvin P, Ward Mark, Duggan Eoin, Xue Feng, Kenny Rose Anne, Romero-Ortuno Roman
The Irish Longitudinal Study on Ageing (TILDA), School of Medicine, Trinity College Dublin, D02 R590 Dublin, Ireland.
Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 R590 Dublin, Ireland.
Diagnostics (Basel). 2023 Aug 29;13(17):2801. doi: 10.3390/diagnostics13172801.
This study was carried out using a large cohort ( = 4265; 416 deceased) of older, community-dwelling adults from The Irish Longitudinal Study on Ageing (TILDA). The study compared the performance of a new 3-item health index (HI) with two existing measures, the 32-item frailty index (FI) and the frailty phenotype (FP), in predicting mortality risk. The HI was based on the objective measurement of resting-state systolic blood pressure sample entropy, sustained attention reaction time performance, and usual gait speed. Mortality data from a 12-year follow up period were analyzed using Cox proportional regression. All data processing was performed using MATLAB and statistical analysis using STATA 15.1. The HI showed good discriminatory power (AUC = 0.68) for all-cause mortality, similar to FI (AUC = 0.68) and superior to FP (AUC = 0.60). The HI classified participants into Low-Risk (84%), Medium-Risk (15%), and High-Risk (1%) groups, with the High-Risk group showing a significant hazard ratio (HR) of 5.91 in the unadjusted model and 2.06 in the fully adjusted model. The HI also exhibited superior predictive performance for cardiovascular and respiratory deaths (AUC = 0.74), compared with FI (AUC = 0.70) and FP (AUC = 0.64). The HI High-Risk group had the highest HR (15.10 in the unadjusted and 5.61 in the fully adjusted models) for cardiovascular and respiratory mortality. The HI remained a significant predictor of mortality even after comprehensively adjusting for confounding variables. These findings demonstrate the effectiveness of the 3-item HI in predicting 12-year mortality risk across different causes of death. The HI performed similarly to FI and FP for all-cause mortality but outperformed them in predicting cardiovascular and respiratory deaths. Its ability to classify individuals into risk groups offers a practical approach for clinicians and researchers. Additionally, the development of a user-friendly MATLAB App facilitates its implementation in clinical settings. Subject to external validation in clinical research settings, the HI can be more useful than existing frailty measures in the prediction of cardio-respiratory risk.
本研究使用了来自爱尔兰老龄化纵向研究（TILDA）的一大群（n = 4265；416人已故）年龄较大的社区居住成年人进行。该研究比较了一种新的3项健康指数（HI）与两种现有测量方法，即32项衰弱指数（FI）和衰弱表型（FP）在预测死亡风险方面的表现。HI基于静息状态收缩压样本熵、持续注意力反应时间表现和通常步速的客观测量。使用Cox比例回归分析了12年随访期的死亡率数据。所有数据处理均使用MATLAB进行，统计分析使用STATA 15.1。HI对全因死亡率显示出良好的辨别力（AUC = 0.68），与FI（AUC = 0.68）相似，优于FP（AUC = 0.60）。HI将参与者分为低风险（84%）、中风险（15%）和高风险（1%）组，高风险组在未调整模型中的显著风险比（HR）为5.91，在完全调整模型中为2.06。与FI（AUC = 0.70）和FP（AUC = 0.64）相比，HI在预测心血管和呼吸死亡方面也表现出更好的预测性能。HI高风险组在心血管和呼吸死亡率方面的HR最高（未调整模型中为15.10，完全调整模型中为5.61）。即使在对混杂变量进行全面调整后，HI仍然是死亡率的显著预测指标。这些发现证明了3项HI在预测不同死因的12年死亡风险方面的有效性。HI在全因死亡率方面的表现与FI和FP相似，但在预测心血管和呼吸死亡方面优于它们。它将个体分类为风险组的能力为临床医生和研究人员提供了一种实用方法。此外，一个用户友好的MATLAB应用程序的开发便于其在临床环境中的实施。在临床研究环境中经过外部验证后，HI在预测心肺风险方面可能比现有的衰弱测量方法更有用。
