title: [Multi-state model in the evaluation of outcome on mild cognitive impairment to Alzheimer's disease].
abstract: OBJECTIVE
  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.
  
  
  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=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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Public Health, Shanxi Medical University, Taiyuan 030001, China.
authors.forename: Jian-wei
authors.initials: JW
authors.lastname: Gao
authors.forename: Shan-shan
authors.initials: SS
authors.lastname: Yang
authors.forename: Li-ye
authors.initials: LY
authors.lastname: Zhou
authors.forename: Xiao-cheng
authors.initials: XC
authors.lastname: Wang
authors.forename: Cai-hong
authors.initials: CH
authors.lastname: Gao
authors.forename: Ping-ping
authors.initials: PP
authors.lastname: Song
authors.forename: Hong-mei
authors.initials: HM
authors.lastname: Yu
mesh_terms: D000368:Aged; D000544:Alzheimer Disease; D060825:Cognitive Dysfunction; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D015999:Multivariate Analysis