A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.
Abstract
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. METHOD: 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. CONCLUSION: 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.
Type
Journal articleSubject
Alzheimer's diseasefull-time care
grade of membership model
nursing home
prediction algorithm
time to death
Aged
Aged, 80 and over
Algorithms
Alzheimer Disease
Cohort Studies
Female
Humans
Kaplan-Meier Estimate
Male
Middle Aged
Predictive Value of Tests
Reproducibility of Results
Sex Factors
Time Factors
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https://hdl.handle.net/10161/14898Published Version (Please cite this version)
10.3233/JAD-131142Publication Info
Razlighi, Qolamreza R; Stallard, Eric; Brandt, Jason; Blacker, Deborah; Albert, Marilyn;
Scarmeas, Nikolaos; ... Stern, Yaakov (2014). A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.
J Alzheimers Dis, 38(3). pp. 661-668. 10.3233/JAD-131142. Retrieved from https://hdl.handle.net/10161/14898.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Anatoli I. Yashin
Research Professor in the Social Science Research Institute

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