Predicting Progression to Clinical Alzheimer’s Disease Dementia Using the Random Survival Forest

Author:

Song Shangchen1,Asken Breton234,Armstrong Melissa J.53,Yang Yang6,Li Zhigang6,

Affiliation:

1. Department of Biostatistics, University of Florida College of Public Health & Health Professions and College of Medicine, Gainesville, FL, USA

2. Department of Clinical and Health Psychology, University of Florida College of Public Health & Health Professions, Gainesville, FL, USA

3. Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA

4. University of Florida Center for Cognitive Aging and Memory, McKnight Brain Institute, Gainesville, FL, USA

5. Departments of Neurology and Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA

6. Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA, USA

Abstract

Background: Assessing the risk of developing clinical Alzheimer’s disease (AD) dementia, by machine learning survival analysis approaches, among participants registered in Alzheimer’s Disease Centers is important for AD dementia management. Objective: To construct a prediction model for the onset time of clinical AD dementia using the National Alzheimer Coordinating Center (NACC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) registered cohorts. Methods: A model was constructed using the Random Survival Forest (RSF) approach and internally and externally validated on the NACC cohort and the ADNI cohort. An R package and a Shiny app were provided for accessing the model. Results: We built a predictive model having the six predictors: delayed logical memory score (story recall), CDR® Dementia Staging Instrument - Sum of Boxes, general orientation in CDR®, ability to remember dates and ability to pay bills in the Functional Activities Questionnaire, and patient age. The C indices of the model were 90.82% (SE = 0.71%) and 86.51% (SE = 0.75%) in NACC and ADNI respectively. The time-dependent AUC and accuracy at 48 months were 92.48% (SE = 1.12%) and 88.66% (SE = 1.00%) respectively in NACC, and 90.16% (SE = 1.12%) and 85.00% (SE = 1.14%) respectively in ADNI. Conclusion: The model showed good prediction performance and the six predictors were easy to obtain, cost-effective, and non-invasive. The model could be used to inform clinicians and patients on the probability of developing clinical AD dementia in 4 years with high accuracy.

Publisher

IOS Press

Subject

Psychiatry and Mental health,Geriatrics and Gerontology,Clinical Psychology,General Medicine,General Neuroscience

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