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.
ABSTRACT
Objective: To evaluate the progression of chronic kidney disease (CKD) in CKD patients and to establish a Markov model for graded prognosis of CKD. Methods: A total of 272 CKD patients were retrospectively investigated. A Markov model consisting of six states (CKD1 stage, CKD2 stage, CKD3 stage, CKD4 stage, CKD5 stage as well as death/ end-stage renal disease [ESRD] stage) was established. Results: The mean follow-up period was 2.0 years. Transition rates from CKD1 stage to CKD2 stage, from CKD2 stage to CKD3 stage, from CKD3 stage to CKD4 stage, from CKD4 stage to CKD5 stage and from CKD5 stage to death/ESRD stage were 9.2%/year, 10.9%/year, 13.2%/year, 16.1%/year, and 47.1%/year, respectively. The Markov model estimated that the mean duration of CKD1 stage, CKD2 stage, CKD3 stage, CKD4 stage, CKD5 stage and death/ESRD stage in our cohort were 11.1 years, 7.8 years, 5.4 years, 2.5 years and 1.0 years, respectively. The mean renal survival time or dialysis free period was 27.8 years. Conclusion: Evaluation of severity and the treatment of CKD patients should be done according to the prognoses of CKD patients at different stages.