Abstract：
OBJECTIVE: Frailty is a dynamic health state that changes over time. Our hypothesis was that there are identifiable subgroups of the older population that have specific patterns of deterioration. The objective of this study was to evaluate the application of joint latent class model (JLCM) in identifying trajectories of frailty progression over time and their group-specific risk of death in older people.
METHODS: The primary care records of UK patients, aged over 65 as of January 1st 2010, included in the CPRD: GOLD and AURUM databases, were analysed and linked to mortality data. Electronic frailty index (eFI) scores were calculated at baseline and annually in subsequent years (2010-2013). JLCM was used to divide the population into clusters with different trajectories and associated mortality hazard ratios (HR). The model was built in GOLD and validated in AURUM.
RESULTS: Five trajectory clusters were identified and characterised based on baseline and speed of progression: low-slow, low-moderate, low-rapid, high-slow and high-rapid. The high-rapid cluster had the highest average starting eFI score; 7.9, while low-rapid cluster had the steepest rate of eFI progression; 1.7. Taking the low-slow cluster as reference, low-rapid and high-rapid had the highest HRs: 3.73 (95%CI 3.71 to 3.76) and 3.63 (3.57 to 3.69), respectively. Good validation was found in the AURUM population.
CONCLUSIONS: Our research found that there are vulnerable subgroups of the older population who are currently frail or have rapid frailty progression. Such groups may be targeted for greater healthcare monitoring.
METHODS: The primary care records of UK patients, aged over 65 as of January 1st 2010, included in the CPRD: GOLD and AURUM databases, were analysed and linked to mortality data. Electronic frailty index (eFI) scores were calculated at baseline and annually in subsequent years (2010-2013). JLCM was used to divide the population into clusters with different trajectories and associated mortality hazard ratios (HR). The model was built in GOLD and validated in AURUM.
RESULTS: Five trajectory clusters were identified and characterised based on baseline and speed of progression: low-slow, low-moderate, low-rapid, high-slow and high-rapid. The high-rapid cluster had the highest average starting eFI score; 7.9, while low-rapid cluster had the steepest rate of eFI progression; 1.7. Taking the low-slow cluster as reference, low-rapid and high-rapid had the highest HRs: 3.73 (95%CI 3.71 to 3.76) and 3.63 (3.57 to 3.69), respectively. Good validation was found in the AURUM population.
CONCLUSIONS: Our research found that there are vulnerable subgroups of the older population who are currently frail or have rapid frailty progression. Such groups may be targeted for greater healthcare monitoring.
摘要：
目的：脆弱是一种随时间变化的动态健康状态。我们的假设是，老年人群中存在可识别的亚组，这些亚组具有特定的恶化模式。这项研究的目的是评估联合潜在类别模型（JLCM）在识别老年人中虚弱进展随时间的轨迹及其特定群体的死亡风险中的应用。
方法：英国患者的初级护理记录，截至2010年1月1日，65岁以上，包括在CPRD：GOLD和AURUM数据库中，进行了分析，并与死亡率数据相关联。电子虚弱指数(eFI)评分在基线时和随后年份(2010-2013年)每年进行计算。JLCM用于将人群划分为具有不同轨迹和相关死亡率风险比（HR）的集群。该模型在GOLD中建立，并在Aurum中进行了验证。
结果：根据基线和前进速度确定并表征了五个轨迹簇：低速，低-中度,低快速，高慢和高快。快速集群具有最高的平均起始eFI评分;7.9,而快速集群具有最陡的eFI进展率;1.7。以低慢速集群为参考，低快速和高快速的HR最高：3.73（95CI3.71至3.76）和3.63（3.57至3.69），分别。在AURUM人群中发现了良好的验证。
结论：我们的研究发现，老年人群中有一些脆弱的亚组，他们目前身体虚弱或有快速的虚弱进展。这样的群体可以针对更大的医疗保健监测。
方法：英国患者的初级护理记录，截至2010年1月1日，65岁以上，包括在CPRD：GOLD和AURUM数据库中，进行了分析，并与死亡率数据相关联。电子虚弱指数(eFI)评分在基线时和随后年份(2010-2013年)每年进行计算。JLCM用于将人群划分为具有不同轨迹和相关死亡率风险比（HR）的集群。该模型在GOLD中建立，并在Aurum中进行了验证。
结果：根据基线和前进速度确定并表征了五个轨迹簇：低速，低-中度,低快速，高慢和高快。快速集群具有最高的平均起始eFI评分;7.9,而快速集群具有最陡的eFI进展率;1.7。以低慢速集群为参考，低快速和高快速的HR最高：3.73（95CI3.71至3.76）和3.63（3.57至3.69），分别。在AURUM人群中发现了良好的验证。
结论：我们的研究发现，老年人群中有一些脆弱的亚组，他们目前身体虚弱或有快速的虚弱进展。这样的群体可以针对更大的医疗保健监测。
