Department of Policy Analysis and Management, College of Human Ecology, Cornell University, Ithaca, NY 14853, USA.
Department of Preventive Medicine, School of Medicine, Ajou University, Suwon, Gyeonggi-do 16499, Korea.
Medicina (Kaunas). 2020 Jul 18;56(7):360. doi: 10.3390/medicina56070360.
This study aimed to group diseases classified by the International Classification of Diseases using principal component analysis, and discuss a systematic approach to reducing the preventable death rate from a perspective of public health. Using a 10-year follow-up analysis of the Korean Longitudinal Study of Aging (KLoSA) data, this study obtained de-identified data including participants' data of community-dwelling individuals aged ≥45 years from 2006 to 2016. Participants were randomly selected using a multistage, stratified probability sampling based on geographical area and housing type. We excluded 37 participants with missing information at baseline and included 10,217 study participants. This study used the principal component analysis to extract comorbidity patterns, and chi-square test and Cox proportional hazards models for analyzing the association between the factors of interest. Principal component 1 (diabetes, heart disease, and hypertension) was associated with an increased hazard ratio (HR) of 1.079 (95% confidence interval (CI) 1.031-1.129, = 0.001). Principal component 3 (psychiatric and cerebrovascular diseases) was related to an increased HR of 1.134 (95% CI 1.094-1.175, < 0.0001). Moreover, principal component 4 was associated with a high HR of 1.172 (95% CI 1.130-1.215, < 0.0001). However, among participants aged between 45 and 64 years, principal component 4 showed a meaningfully increased HR of 1.262 (95% CI 1.184-1.346, < 0.001). In this study, among the four principal components, three were statistically associated with increased mortality. The principal component analysis for predicting mortality may become a useful tool, and artificial intelligence (AI) will improve a value-based healthcare strategy, along with developing a clinical decision support model.
本研究旨在通过主成分分析对《国际疾病分类》中的疾病进行分组，并从公共卫生角度探讨一种降低可预防死亡率的系统方法。本研究使用韩国老龄化纵向研究（KLoSA）2006 至 2016 年的 10 年随访数据，获取了包括年龄≥45 岁的社区居民参与者数据的匿名数据。参与者采用基于地理区域和住房类型的多阶段、分层概率抽样随机选择。我们排除了基线时信息缺失的 37 名参与者，并纳入了 10217 名研究参与者。本研究使用主成分分析提取共病模式，并用卡方检验和 Cox 比例风险模型分析感兴趣因素之间的关联。主成分 1（糖尿病、心脏病和高血压）与更高的危险比（HR）1.079（95%置信区间（CI）1.031-1.129， = 0.001）相关。主成分 3（精神和脑血管疾病）与更高的 HR 1.134（95%CI 1.094-1.175，<0.0001）相关。此外，主成分 4 与较高的 HR 1.172（95%CI 1.130-1.215，<0.0001）相关。然而，在 45 至 64 岁的参与者中，主成分 4 显示出有意义的 HR 增加了 1.262（95%CI 1.184-1.346，<0.001）。在本研究中，四个主成分中有三个与死亡率增加统计学相关。预测死亡率的主成分分析可能成为一种有用的工具，人工智能（AI）将与开发临床决策支持模型一起，改善基于价值的医疗保健策略。
