Development and Validation of a Multimorbidity Index Predicting Mortality Among Older Chinese Adults.
Development and Validation of a Multimorbidity Index Predicting Mortality Among Older Chinese Adults.
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DOI：
10.3389/fnagi.2022.767240
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发表时间：
2022
影响因子：
4.8
通讯作者：
Xu B
中科院分区：
文献类型：
--
作者：
Luo Y;Huang Z;Liu H;Xu H;Su H;Chen Y;Hu Y;Xu B
这项研究的目的是利用自我报告的慢性疾病来开发和验证预测5年死亡风险的多病指数。我们分析了中国健康长寿纵向调查(CLHLS)的数据，包括11,853名65-84岁的社区老年人。限制性关联规则挖掘(ARM)用于基于13种慢性病识别与死亡率相关的疾病组合。数据被随机分成训练组(N=8,298)和验证组(N=3,555)。在训练组中，使用5年死亡率的风险比(HRs)开发了两个仅有单个疾病(MI)和疾病组合(MIDC)的多发病指数。我们通过一致性(C)统计量、综合判别改进(IDI)和净重分类指数(NRI)比较了使用条件计数、MI和MIDC的模型在验证集中的预测性能。总共确定了13种疾病组合。与条件计数(C-统计量：0.710)相比，MIDC(C-统计量：0.713)的判别能力显著增强(C-统计量：P=0.016；IDI：0.005，p<；0.001；NRI：0.038，p=0.478)。与MI(C统计量：0.711)相比，MIDC模型的C统计量显著高于MI(P=0.031)，而IDI>0，但无统计学意义(IDI：0.003，P=0.090)。尽管目前的多发病状态通常由个人慢性病来定义，但这项研究发现，结合疾病组合的多发病指数在预测社区老年人死亡率方面表现出最好的表现。这些发现表明，在医学研究和临床实践中衡量多发病时，需要考虑显著的疾病组合。
This study aimed to develop and validate a multimorbidity index using self-reported chronic conditions for predicting 5-year mortality risk. We analyzed data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and included 11,853 community-dwelling older adults aged 65–84 years. Restrictive association rule mining (ARM) was used to identify disease combinations associated with mortality based on 13 chronic conditions. Data were randomly split into the training (N = 8,298) and validation (N = 3,555) sets. Two multimorbidity indices with individual diseases only (MI) and disease combinations (MIDC) were developed using hazard ratios (HRs) for 5-year morality in the training set. We compared the predictive performance in the validation set between the models using condition count, MI, and MIDC by the concordance (C) statistic, the Integrated Discrimination Improvement (IDI), and the Net Reclassification Index (NRI). A total of 13 disease combinations were identified. Compared with condition count (C-statistic: 0.710), MIDC (C-statistic: 0.713) showed significantly better discriminative ability (C-statistic: p = 0.016; IDI: 0.005, p < 0.001; NRI: 0.038, p = 0.478). Compared with MI (C-statistic: 0.711), the C-statistic of the model using MIDC was significantly higher (p = 0.031), while the IDI was more than 0 but not statistically significant (IDI: 0.003, p = 0.090). Although current multimorbidity status is commonly defined by individual chronic conditions, this study found that the multimorbidity index incorporating disease combinations showed supreme performance in predicting mortality among community-dwelling older adults. These findings suggest a need to consider significant disease combinations when measuring multimorbidity in medical research and clinical practice.
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