Measuring Alzheimer Disease Progression with Transition Probabilities: Estimates from NACC-UDS

D.      Eldon Spackman; Srikanth      Kadiyala; Peter      J. Neumann; David      L. Veenstra; Sean      D. Sullivan

Abstract

Objectives: Estimate the probabilities, for Alzheimer's disease (AD) patients, of transitioning between stages of disease severity (mild, moderate, severe, dead) and care settings (community, institutional). Methods: Data were compiled by the National Alzheimer Coordinating Center. The main analyses were limited to 3,852 patients who were >50 years old, diagnosed with possible/probable AD and had at least two center visits. A multinomial logistic model accounting for patient and center level correlation was used to calculate transition probabilities between stages of the Clinical Dementia Rating (CDR). Separately we calculated the probabilities of being institutionalized based on CDR stage. Both analyses controlled for baseline age, time between visits, sex, marital status, whether white, whether Hispanic and number of years of education. Results: The annual probabilities of dying for patients in mild, moderate and severe health states were 5.5%, 21.5% and 48.0%, respectively, while the annual probabilities for institutionalization were 1.2%, 3.4% and 6.6%, respectively. The majority of mild and moderate patients remain in the same health state after one year, 77.4% and 50.1% respectively. Progressing patients are most likely to transition one stage, but 1.3% of mild patients become severe in one year. Some patients revert to lower severity stages, 7% from moderate to mild. Conclusions: Transition probabilities to higher CDR stages and to institutionalization are lower than those published previously, but the probability of death is higher. These results are useful for understanding AD progression and can be used in simulation models to evaluate costs and compare new treatments or policies.

Keywords: Alzheimer disease, Natural history study, Progression, Transition probabilities, cholinesterase inhibitor, multinomial regressions, pharmaceutical treatments.

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