Gao Jian-wei, Yang Shan-shan, Zhou Li-ye, Wang Xiao-cheng, Gao Cai-hong, Song Ping-ping, Yu Hong-mei
School of Public Health, Shanxi Medical University, Taiyuan 030001, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2012 May;33(5):470-3.
The aim of this study was to introduce the multi-state 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.
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.
At the level of 0.05, data from the multivariate analysis showed that gender (HR=1.23, 95%CI: 1.12-1.38), age (HR=1.37, 95%CI: 1.07-1.72), hypertension (HR=1.54, 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=1.22, 95%CI: 1.11-1.43), diabetes (HR=1.52, 95%CI: 1.12-2.00), ApoEe4 (HR=1.44, 95%CI: 1.09-1.68) were statistically significant for transition from state 2 to state 3. Based on the fitted model, the three-year transition probabilities during each state at average covariate level were estimated.
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-factors.
本研究旨在引入多状态马尔可夫模型来预测轻度认知障碍（MCI）向阿尔茨海默病（AD）的发展，并找出老年人中AD预防和早期干预的相关因素。
将MCI、中度至重度认知障碍和AD分别定义为状态1、2和3。根据六次随访的数据构建了一个具有离散状态和离散时间的三状态齐次模型，以探索从MCI到AD各个进展阶段的因素。在模型拟合评估后绘制转移概率和生存曲线。
在0.05的水平上，多变量分析的数据显示，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）对于从状态1向状态2的转变具有统计学意义；而年龄（HR = 0.78，95%CI：0.69 - 0.98）、教育水平（HR = 1.35，95%CI：1.09 - 1.86）和阅读（HR = 1.20，95%CI：1.01 - 1.41）对于从状态2向状态1的转变具有统计学意义；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）、ApoEe4（HR = 1.44，95%CI：1.09 - 1.68）对于从状态2向状态3的转变具有统计学意义。基于拟合模型，估计了平均协变量水平下每个状态的三年转移概率。
为延缓MCI的疾病进展，可根据各阶段的主要因素采取分阶段预防措施。多状态马尔可夫模型能够模拟疾病的自然史，在动态评估多状态、多因素慢性病的发展方面具有很大优势。
