Predicting time to full-time care in AD: a new model.
OBJECTIVE
To develop a model to predict the length of time before patients with Alzheimer's disease (AD) of varying severity require full-time care (FTC).


METHODS
A predictive model (equation) of time to FTC (defined as being institutionalised or dependent) was developed based on the London and South-East Region (LASER-AD) epidemiological study using a discrete time representation of the Cox continuous time proportional hazards model and complementary log-log specification.


RESULTS
Of the 117 pre-FTC patients, 68 (58.1%) patients progressed to FTC during the 54-month follow-up period. Analysis of potential predictors showed that baseline cognitive state, impairment of activities of daily living (ADL) and neuropsychiatric symptoms were strong predictors of time to FTC. In addition, the rate of cognitive and ADL decline predicted time to FTC. The final model predicted 88.2% of observations.


CONCLUSIONS
The model simulates and predicts progression of pre-FTC AD patients until the need for FTC based on assessments for cognitive, functional and behavioural domains. The main application of the model is to assess the cost effectiveness of AD therapies as potential adjuncts to a background AD treatment including disease-modifying treatments. The applicability of the predictive model to a specific setting should be carefully assessed, i.e. the patient population being examined should have similar characteristics as patients in the LASER-AD cohort.
Rive
B
B
University Claude Bernard Lyon 1, France.
Le Reun
C
C
Grishchenko
M
M
Cochran
J
J
Katona
C
C
Livingston
G
G
Lamure
M
M
Toumi
M
M
François
C
C
D000367:Age Factors; D000368:Aged; D000544:Alzheimer Disease / Q000503:physiopathology*; D018450:Disease Progression; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D012720:Severity of Illness Index; D012737:Sex Factors; D012959:Socioeconomic Factors; D013997:Time Factors