Incremental predictive value of intrinsic capacity and environmental characteristics in the risk prediction of incident disability.

JAR life Pub Date : 2025-01-31 eCollection Date: 2025-01-01 DOI:10.1016/j.jarlif.2025.100004
Ruby Yu, Grace Leung, Derek Lai, Lok-Yan Tam, Clara Cheng, Sara Kong, Cecilia Tong, Jean Woo
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Abstract

Objective: To examine the incremental value of intrinsic capacity (IC) and environmental characteristics in the risk prediction of disability.

Method: Secondary analysis was performed on a longitudinal sample of individuals aged 50 years or above. The selected subsample was ambulant and cognitively intact, and did not have any disabilities in instrumental activities of daily living (IADL) at baseline. A set of 18 indicators were first used to assess conditions associated with declines in IC and environmental characteristics. Participants were then followed up for approximately one year, and the IADL status (i.e., disabled or not) was treated as the outcome variable in the logistic regression models. The incremental predictive value of IC was examined by comparing the baseline model that only included traditional risk factors (e.g., health conditions and lifestyle factors), against the full model that also included the aforementioned 18 indicators. The comparison was performed using the change in area under the receiver operating characteristic curve (ROCAUC) and the continuous net reclassification index (NRI).

Results: Among 10,993 participants (mean age = 73.3, 82.1 % women), 680 (6.2 %) developed disability during the concerned period. The full model significantly outperformed the baseline model, with the ROCAUC improving from 0.707 to 0.729 (change = 0.021; 95 % CI: 0.013-0.030). The continuous NRI was 0.361 (95 % bootstrap CI: 0.280-0.450).

Conclusions: Measurements of IC and environmental characteristics have a significant incremental value in predicting disability. In practice, the full model can be implemented as a calculator for identifying older populations at risk of disability in the community settings.

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内在能力和环境特征在事故致残风险预测中的增量预测值。
目的:探讨内在能力(IC)和环境特征在残疾风险预测中的增量价值。方法:对50岁及以上的个体进行纵向抽样二次分析。所选的子样本是行走和认知完整的,并且在基线时没有任何日常生活工具活动(IADL)的残疾。一套18个指标首次用于评估与IC下降和环境特征相关的条件。然后对参与者进行大约一年的随访,并将IADL状态(即残疾与否)作为逻辑回归模型中的结果变量。通过将仅包括传统风险因素(如健康状况和生活方式因素)的基线模型与还包括上述18个指标的完整模型进行比较,来检查IC的增量预测值。采用受试者工作特征曲线下面积变化(ROCAUC)和连续净重分类指数(NRI)进行比较。结果:在10,993名参与者中(平均年龄为73.3岁,82.1%为女性),680名(6.2%)在研究期间出现残疾。完整模型显著优于基线模型,ROCAUC从0.707提高到0.729(变化= 0.021;95% ci: 0.013-0.030)。连续NRI为0.361 (95% bootstrap CI: 0.280-0.450)。结论:测量IC和环境特征在预测残疾方面具有显著的增量价值。在实践中,完整的模型可以作为在社区环境中识别有残疾风险的老年人口的计算器来实施。
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