| 高建伟,杨珊珊,周立业,王晓成,高彩虹,宋平平,余红梅.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[J].中华流行病学杂志,2012,33(5):470-473 |
| 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 |
| Multi-state model in the evaluation of outcome on mild cognitive impairment to Alzheimer's disease |
| 收稿日期:2011-11-29 出版日期:2014-09-18 |
| DOI： |
| 中文关键词: 阿尔茨海默病|轻度认知损害|多状态模型|转归 |
| 英文关键词: Alzhcimer's disease|Mild cognitive impairment|Malti—state model|Outcome |
| 基金项目:国家自然科学基金(30972545)；山西省留学回国人员科技活动择优项目；山西医科大学科技创新项目 |
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| 中文摘要: |
| 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fAD)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的预防和早期十预提供理论依据。方法利用MCI患者6次随访资料，以MCI为状态l，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d=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(月R=1。54，95％CI1。31～2。 19)对状态l—嗽态2转移有统计学意义；年龄(HR=O。78，95％CI：0。69—0。98)、文化程度(HR=1。35，95％CI：1。09一1。86)和常读书看(HR=1。20，95％CI：1。01一1。4I)对状态2一状态l转移有统计学意义；性别(HR=1。59，95％C1：1。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。69)对状态2一状态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。 |
| 英文摘要: |
| Objective The aim of this study was to introduce the multi—slate Markov modelfor the prediction of mild cognitive impairment(MCI)to AIzheimer’S diseaselAD)and to find out the related factors for AD preventionan dearly in tervention among tile 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 six follow—up visits was constructed to explore factors for various progressive stages from MCI to AD.Transition probability and survival curve were madeafter the model fit as sessment.Results At the level of 0.05,data from the multivariate analysis showed that gender(HR=1.23,95％c,：1.12一1.38).age(HR=l.37,95％G／:L071~72), hypertension (HR=1.54,95％C/：1.3一2. 19) were statistically significant for the transition from state I to state 2. while age(HR=O.78,95％C1：0.69—0.98).education level(HR=1.35. 95％Cl：1.09一lr86)and reading(HR=1.20,95％c,：1.01一1.41)were：statistically significant for transition from state 2 to state l,and gander(HR=1.59,95％C／：1.33一1.89), age(HR=1.33,95％C／：1.02-1.64), hypertension(HR=1.22,95％C／：1.1l一1.43).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 statc 2 to slate 3.Based 0n the fired modeI,the three-yeart ransition probabilities during each state at average covariate Ievel 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 naturalhistory of disease and showed great advantage in dynamically evaluating the development of chronic diseases with multi-states and multi-factors. |
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