A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.

Razlighi, Qolamreza R

Stallard, Eric

Brandt, Jason

Blacker, Deborah

Albert, Marilyn

Scarmeas, Nikolaos

Kinosian, Bruce

Yashin, Anatoliy I

Stern, Yaakov

Netherlands

2017-06-08T18:38:38Z

2017-06-08T18:38:38Z

2014

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.

https://www.ncbi.nlm.nih.gov/pubmed/24064468

H05206T16TW72478

1875-8908

https://hdl.handle.net/10161/14898

eng

IOS Press

J Alzheimers Dis

10.3233/JAD-131142

Alzheimer's disease

full-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

A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.

Journal article

https://www.ncbi.nlm.nih.gov/pubmed/24064468

661

668

3

Center for Population Health & Aging

Duke

Duke Cancer Institute

Duke Population Research Center

Duke Population Research Institute

Institutes and Centers

Institutes and Provost's Academic Units

Sanford School of Public Policy

School of Medicine

Social Science Research Institute

University Institutes and Centers

Published

38