Inter‐cohort staging efficacy of gaussian process progression model for Alzheimer’s disease

Authors: Damiano Archetti, Marco Lorenzi, Neil P. Oxtoby, Daniel C. Alexander, Giovanni B. Frisoni, Alberto Redolfi

Published: 2020-12-07

Abstract

AbstractBackgroundUnderstanding the path that leads from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) provides insights into dementia pathophysiology and can inform patient stratification in clinical trials. Our objective is to train statistical computational models for estimating an average disease trajectory to better understand how patients evolve on the basis of biomarkers of from the ADNI population, then validate the model on MCI subjects from the PharmaCog cohort.MethodThe disease trajectory was built via a Gaussian Process Progression Model (GPPM) (Lorenzi et al. 2017, DOI: 10.1016/j.neuroimage.2017.08.059) trained on longitudinal biomarker data from 338 subjects in the ADNI database who had converted from MCI to AD at the time of writing. Biomarkers related to cognitive scores, cerebrospinal fluid and T13D magnetic resonance imaging were used to build the disease model. Validation was performed by staging 139 MCI subjects from the PharmaCog database, 20 of whom progressed to AD, along the progression model timeline. Only baseline cross sectional measures of biomarkers were used to stage PharmaCog subjects in order to simulate clinical settings. Measures of sensitivity, specificity, balanced accuracy and area under curve (AUC) were used to measure the staging performance.ResultThe staging of PharmaCog subjects shows a clear separation between MCI stable (sMCI) subjects and MCI progressors (pMCI) on the disease timeline. On average, sMCIs were staged at year 72.7±3.6 while pMCIs are staged 3 years later (p‐value<0.01) at year 75.5±2.8. Classification of sMCI vs pMCI subjects returned a sensitivity equal to 0.68, specificity equal to 0.85, balanced accuracy equal to 0.77 and the ROC curve (Figure 1) had AUC equal to 0.75.ConclusionWe identified MCI converters to AD in a clinical data set (PharmaCog) using a data‐driven computational model of AD progression trained on research data (ADNI). Classification performance was comparable to other data‐driven tools (Young et al., 2014, DOI: 10.1093/brain/awu176), but performed in a more challenging clinical setting. This validates the staging efficacy of such models and shows their utility for future application in healthcare and clinical trials.