PDF(Pubmed)
Abstract：
BACKGROUND: Frailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.
METHODS: We conducted a cross-sectional and longitudinal study using data from the \"Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),\" which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).
RESULTS: At baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model\'s AUC was 0.943 (95%CI 0.913-0.974), indicating good discrimination, with Hosmer-Lemeshow (H-L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model\'s AUC was 0.722 (95%CI 0.656-0.788), indicating moderate discrimination, with H-L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.
CONCLUSIONS: Frailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.
METHODS: We conducted a cross-sectional and longitudinal study using data from the \"Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),\" which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).
RESULTS: At baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model\'s AUC was 0.943 (95%CI 0.913-0.974), indicating good discrimination, with Hosmer-Lemeshow (H-L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model\'s AUC was 0.722 (95%CI 0.656-0.788), indicating moderate discrimination, with H-L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.
CONCLUSIONS: Frailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.
摘要：
背景：虚弱是一种多因素综合征；通过这项研究，我们的目的是调查生理，心理,以及与社区居住老年人的虚弱和虚弱恶化相关的社会因素。
方法：我们使用来自“社区授权与福祉和健康长期护理：来自队列研究（CEC）的证据”的数据进行了横向和纵向研究。“重点是日本65岁及以上的社区居民。横断面研究的样本来自2014年进行的CEC研究，共有673名参与者。在排除基线评估（2014年）和3年随访（2017年）期间体弱者后，该研究包括373名参与者.脆弱评估是从Kihon清单中提取的，而社会关系使用社会互动指数(ISI)进行评估。使用最小绝对收缩和选择算子（LASSO）回归进行变量选择，并测试其预测能力。通过应用于贝叶斯网络（BNs）的最大最小爬升算法确定了与虚弱状态和恶化相关的因素。
结果：在基线时，14.1%(673人中有95人)的参与者身体虚弱，24.1%(373人中有90人)的参与者在3年随访时出现虚弱恶化.LASSO回归确定了脆弱的关键变量。对于脆弱识别(横截面),LASSO模型的AUC为0.943（95CI0.913-0.974），表明良好的歧视，Hosmer-Lemeshow(H-L)检验p=0.395。对于虚弱恶化(纵向),LASSO模型的AUC为0.722（95CI0.656-0.788），表明适度的歧视，H-L检验p=0.26。BN发现年龄，多浊度,功能状态,社会关系是与脆弱直接相关的父节点。它揭示了75岁或以上有身体功能障碍的人有85%的虚弱概率，多药，和低ISI分数；然而，如果他们的社会关系和多重用药状况得到改善，概率降低到50.0%。在纵向水平脆弱恶化模型中，75岁或以上的人的身体素质和ISI评分下降，其身体虚弱恶化的概率为75%；然而，如果身体功能和ISI改善，概率下降到25.0%。
结论：脆弱及其进展在社区居住的老年人中普遍存在，并受各种因素的影响，包括年龄,物理功能，和社会关系。神经网络有助于识别这些变量之间的相互关系，量化关键因素的影响。然而,需要进一步的研究来验证所提出的模型。
方法：我们使用来自“社区授权与福祉和健康长期护理：来自队列研究（CEC）的证据”的数据进行了横向和纵向研究。“重点是日本65岁及以上的社区居民。横断面研究的样本来自2014年进行的CEC研究，共有673名参与者。在排除基线评估（2014年）和3年随访（2017年）期间体弱者后，该研究包括373名参与者.脆弱评估是从Kihon清单中提取的，而社会关系使用社会互动指数(ISI)进行评估。使用最小绝对收缩和选择算子（LASSO）回归进行变量选择，并测试其预测能力。通过应用于贝叶斯网络（BNs）的最大最小爬升算法确定了与虚弱状态和恶化相关的因素。
结果：在基线时，14.1%(673人中有95人)的参与者身体虚弱，24.1%(373人中有90人)的参与者在3年随访时出现虚弱恶化.LASSO回归确定了脆弱的关键变量。对于脆弱识别(横截面),LASSO模型的AUC为0.943（95CI0.913-0.974），表明良好的歧视，Hosmer-Lemeshow(H-L)检验p=0.395。对于虚弱恶化(纵向),LASSO模型的AUC为0.722（95CI0.656-0.788），表明适度的歧视，H-L检验p=0.26。BN发现年龄，多浊度,功能状态,社会关系是与脆弱直接相关的父节点。它揭示了75岁或以上有身体功能障碍的人有85%的虚弱概率，多药，和低ISI分数；然而，如果他们的社会关系和多重用药状况得到改善，概率降低到50.0%。在纵向水平脆弱恶化模型中，75岁或以上的人的身体素质和ISI评分下降，其身体虚弱恶化的概率为75%；然而，如果身体功能和ISI改善，概率下降到25.0%。
结论：脆弱及其进展在社区居住的老年人中普遍存在，并受各种因素的影响，包括年龄,物理功能，和社会关系。神经网络有助于识别这些变量之间的相互关系，量化关键因素的影响。然而,需要进一步的研究来验证所提出的模型。
