A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time‐to‐event data. Issue 5 (4th January 2018)

• Record Type: Journal Article
• Authors: Li, Kan; O'Brien, Richard; Lutz, Michael; Luo, Sheng
• Is Part Of: Alzheimer's & dementia. Volume 14:Issue 5(2018)
• Page Start: 644
• Page End: 651
• Publication Date: 2018-01-04
• Subjects: Mild cognitive impairment -- Multivariate functional component analysis -- Prediction -- External validation -- ADNI; Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1016/j.jalz.2017.11.004
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Introduction: Characterizing progression in Alzheimer's disease is critically important for early detection and targeted treatment. The objective was to develop a prognostic model, based on multivariate longitudinal markers, for predicting progression‐free survival in patients with mild cognitive impairment. Methods: The information contained in multiple longitudinal markers was extracted using multivariate functional principal components analysis and used as predictors in the Cox regression models. Cross‐validation was used for selecting the best model based on Alzheimer's Disease Neuroimaging Initiative–1. External validation was conducted on Alzheimer's Disease Neuroimaging Initiative–2. Results: Model comparison yielded a prognostic index computed as the weighted combination of historical information of five neurocognitive longitudinal markers that are routinely collected in observational studies. The comprehensive validity analysis provided solid evidence of the usefulness of the model for predicting Alzheimer's disease progression. Discussion: The prognostic model was improved by incorporating multiple longitudinal markers. It is useful for monitoring disease and identifying patients for clinical trial recruitment. Highlights: A novel and efficient statistical method for prognosis of progression‐free survival in mild cognitive impairment patients using multiple longitudinal markers. The prognostic model was evaluated via both internal and external validations. The longitudinal profiles of multiple markers, all of which are routinely collected in observational studies, could enhance the prognostic accuracy of Alzheimer's disease, if they are properly included in the prognostic model. A prognostic index produced by the prognostic model is useful for monitoring disease progression and identifying patients for clinical trial recruitment.