ABSTRACT
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.
ABSTRACT
Objective To introduce the Multi-state Markov model in studying the outcome prediction of mild cognitive impairment (MCI). Methods Based on the intelligence quotient (IQ)changes that reflecting the trends in cognitive function in the patients under follow-up program, we constructed a four states model and used Multi-state Markov model to analyze the patients. Results Among 600 MCI patients, there were 174(29.0%) males and 426(71.0%) females, with age range of 65-90 years-old (average 69.7±6.6). For univariate analysis, gender, age, education level, marital status, smoking, household income, cerebral hemorrhage, hypertension, high cholesterol, diabetes,LDL-C, SBP and DBP were found to be associated with cognitive function. For multivariate analysis,female, older age, cerebral hemorrhage and higher SBP were shown to be the risk factors for transition from the state of cognitive stability to the state of severe deterioration, and their coefficients were 0.762,0.366,0.885, and 0.069, respectively. Age (0.038) could influence the transition from the state of cognitive stability to slight deterioration. Higher education level was shown to be the protective factor for these transitions (-0.219 and-0.297). Transition intensity from the state of cognitive stability to the state of slight and severe deterioration was 1.2 times that of transition to the state of improving. Transition intensity from state of slight deterioration to cognitive stability was 11.4times that of transition to severe deterioration. Conclusion Multi-state Markov model was an effective tool in dealing with longitudinal data.