多状态模型在轻度认知损害向阿尔茨海默病转归研究中的应用

摘要

Objective The aim of this study was to introduce the multi-slate Markov model for the prediction of mild cognitive impairment (MCI) to Alzheimer' s disease (AD) and to find out the related factors for AD prevention and early intervention among the elderly.Methods MCI,moderate to severe cognitive impairment,and AD were defined as state 1,2 and 3,respectively.A three-state homogeneous model with discrete states and discrete times from data on six follow-up visits was constructed to explore factors for various progressive stages from MCI to AD.Transition probability and survival curve were made after the model fit assessment.Results At the level of 0.05,data from the multivariate analysis showed that gender (HR=I.23,95％CI:1.12-1.38),age (HR=I.37,95％ CI:1.07-1.72),hypertension (HR=l.54,95％ CI:1.31-2.19) were statistically significant for the transition from state 1 to state 2,while age (HR=0.78,95％ CI:0.69-0.98),education level (HR=1.35,95％ CI:1.09-1.86) and reading (HR=1.20,95％ CI:1.01-1.41 ) were statistically significant for transition from state 2 to state 1,and gender (HR=1.59,95％ CI:1.33-1.89),age (HR=1.33,95％ CI:1.02-1.64),hypertension (HR=l.22,95％ CI:1.11-1.43),diabetes (HR=1.52,95％CI:1.12-2.00),ApoEε4 (HR=1.44,95％CI:1.09-1.68) were statistically significant for transition from state 2 to state 3.Based on the fired model,the three-year transition probabilities during each state at average covariate level were estimated.Conclusion To delay the disease progression of MCI,phase by phase prevention measures could be adopted based on the main factors of each stage.Multi-state Markov model could imitate the natural history of disease and showed great advantage in dynamically evaluating the development of chronic diseases with multi-states and multi-faetors.%目的 将多状态Markov模型引入到轻度认知损害(MCI)向阿尔茨海默病(AD)转归研究中,探讨影响MCI转归的因素并进行转归预测,为老年人AD的预防和早期干预提供理论依据.方法 利用MCI患者6次随访资料,以MCI为状态1,中重度认知损害为状态2,AD为状态3,拟合一个时间离散、状态离散的三状态齐性Markov模型,分析MCI向AD转归不同发展阶段的影响因素.模型拟合优度评价后预测不同状态间的转移概率和生存曲线.结果 经多因素筛选,在α =0.05的检验水准下,性别(HR=1.23,95％CI:1.12～1.38)、年龄(HR=1.37,95％CI:1.07～1.72)、高血压(HR=1.54,95％CI:1.31～2.19)对状态1→状态2转移有统计学意义；年龄(HR=0.78,95％CI:0.69～0.98)、文化程度(HR=1.35,95％CI:1.09～1.86)和常读书看报(HR=1.20,95％CI:1.01～1.41)对状态2→状态1转移有统计学意义；性别(HR=1.59,95％CI:1.33～1.89)、年龄(HR=1.33,95％CI:1.02～1.64)、高血压(HR=1.22,95％CI:1.11～1.43)、糖尿病(HR=1.52,95％CI:1.12～2.00)、ApoEε4等位基因(HR=1.44,95％CI:1.09～1.68)对状态2→状态3转移有统计学意义.基于多状态Markov模型估计了协变量取值为平均水平下,从基线起到3年后的转移概率.结论 为延缓MCI疾病进程,应该根据各阶段转移的主要影响因素,开展分阶段重点疾病防治；多状态Markov 能够模拟疾病的自然史,在动态地评价多因素、多阶段的慢性疾病进展方面具有很大的优势.