A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.
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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.
grade of membership model
time to death
Aged, 80 and over
Predictive Value of Tests
Reproducibility of Results
Published Version (Please cite this version)10.3233/JAD-131142
Publication InfoAlbert, M; Blacker, D; Brandt, J; Kinosian, B; Razlighi, QR; Scarmeas, N; ... Yashin, Anatoli I (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.
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Research Professor in the SocialScience Research Institute
I am a Research Professor in the Social Science Research Institute and Co-Director of the Biodemography of Aging Research Unit at Duke University. I am a Member of the American Academy of Actuaries, a Fellow of the Conference of Consulting Actuaries, and an Associate of the Society of Actuaries. My research expertise includes modeling and forecasting for biomedical demography and health/LTC actuarial practice. My expertise in these areas is evidenced by my five books, five monographs, 151 sci
Research Professor in the Social Science Research Institute
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