A New Algorithm for Predicting Time to Disease Endpoints in Alzheimer's Disease Patients

Article type: Research Article

Authors: Razlighi, Qolamreza R.^a | Stallard, Eric^b | Brandt, Jason^{c; d} | Blacker, Deborah^e | Albert, Marilyn^c | Scarmeas, Nikolaos^a | Kinosian, Bruce^f | Yashin, Anatoliy I.^b | Stern, Yaakov^{a; *}

Affiliations: [a] Cognitive Neuroscience Division, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, USA | [b] Social Science Research Institute and Center for Population Health and Aging, Duke University, Durham, NC, USA | [c] Departments of Psychiatry and Behavioral Science, and Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA | [d] The Copper Ridge Institute, Sykesville, MD, USA | [e] Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA | [f] Philadelphia VA Medical Center, Philadelphia, PA, USA

Correspondence: [*] Correspondence to: Yaakov Stern, 630W 168th St, P&S Box 16, New York, NY 10032, USA. Tel.: +1 212 342 1350; Fax: +1 212 342 1838; E-mail: ys11@columbia.edu.

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.

Keywords: Alzheimer's disease, full-time care, grade of membership model, nursing home, prediction algorithm, time to death

DOI: 10.3233/JAD-131142

Journal: Journal of Alzheimer's Disease, vol. 38, no. 3, pp. 661-668, 2014