A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.
BACKGROUND
The ability to predict the length of time to death and institutionalization has strong implications for Alzheimer's disease patients and caregivers, health policy, economics, and the design of intervention studies.


OBJECTIVE
To develop and validate a prediction algorithm that uses data from a single visit to estimate time to important disease endpoints for individual Alzheimer's disease patients.


METHODS
Two separate study cohorts (Predictors 1, N = 252; Predictors 2, N = 254), all initially with mild Alzheimer's disease, were followed for 10 years at three research centers with semiannual assessments that included cognition, functional capacity, and medical, psychiatric, and neurologic information. The prediction algorithm was based on a longitudinal Grade of Membership model developed using the complete series of semiannually-collected Predictors 1 data. The algorithm was validated on the Predictors 2 data using data only from the initial assessment to predict separate survival curves for three outcomes.


RESULTS
For each of the three outcome measures, the predicted survival curves fell well within the 95% confidence intervals of the observed survival curves. Patients were also divided into quintiles for each endpoint to assess the calibration of the algorithm for extreme patient profiles. In all cases, the actual and predicted survival curves were statistically equivalent. Predictive accuracy was maintained even when key baseline variables were excluded, demonstrating the high resilience of the algorithm to missing data.


CONCLUSIONS
The new prediction algorithm accurately predicts time to death, institutionalization, and need for full-time care in individual Alzheimer's disease patients; it can be readily adapted to predict other important disease endpoints. The algorithm will serve an unmet clinical, research, and public health need.
Cognitive Neuroscience Division, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, USA.
Qolamreza R
QR
Razlighi
Eric
E
Stallard
Jason
J
Brandt
Deborah
D
Blacker
Marilyn
M
Albert
Nikolaos
N
Scarmeas
Bruce
B
Kinosian
Anatoliy I
AI
Yashin
Yaakov
Y
Stern
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000401:mortality* / Q000503:physiopathology*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012737:Sex Factors; D013997:Time Factors
Alzheimer's disease; full-time care; grade of membership model; nursing home; prediction algorithm; time to death