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

Article type: Research Article

Authors: Song, Shangchen^a | Asken, Breton^{b; e; f} | Armstrong, Melissa J.^{c; e} | Yang, Yang^{d; 1} | Li, Zhigang^{a; 1; *} | for the Alzheimer’s Disease Neuroimaging Initiative²

Affiliations: [a] Department of Biostatistics, University of Florida College of Public Health & Health Professions and College of Medicine, Gainesville, FL, USA | [b] Department of Clinical and Health Psychology, University of Florida College of Public Health & Health Professions, Gainesville, FL, USA | [c] Departments of Neurology and Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA | [d] Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA, USA | [e] Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA | [f] University of Florida Center for Cognitive Aging and Memory, McKnight Brain Institute, Gainesville, FL, USA

Correspondence: [*] Correspondence to: Zhigang Li, PhD, Department of Biostatistics, 2004 Mowry Rd FL 5, Gainesville, FL 32611, USA. Tel.: +1 352 294 5915; Fax: +1 352 294 5915; E-mail: [email protected].

Note: [1] Senior authors

Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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.

Keywords: Alzheimer’s disease, dementia, machine learning, survival analysis

DOI: 10.3233/JAD-230208

Journal: Journal of Alzheimer's Disease, vol. 95, no. 2, pp. 535-548, 2023