Multi-state model in the evaluation of outcome on mild cognitive impairment to Alzheimer's disease

Abstract

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.