A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia

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Abstract

Background

Clinical trials in Alzheimer's disease need to enroll patients whose cognition will decline over time, if left untreated, in order to demonstrate the efficacy of an intervention. Machine learning models used to screen for patients at risk of progression to dementia should therefore favor specificity (detecting only progressors) over sensitivity (detecting all progressors), especially when the prevalence of progressors is low. Here, we explore whether such high-risk patients can be identified using cognitive assessments and structural neuroimaging by training machine learning tools in a high-specificity regime.

Results

A multimodal signature of Alzheimer's dementia was first extracted from the ADNI1 dataset. We then validated the predictive value of this signature on ADNI1 patients with mild cognitive impairment (N = 235). The signature was optimized to predict progression to dementia over 3 years with low sensitivity (55.1%) but high specificity (95.6%), resulting in only moderate accuracy (69.3%) but high positive predictive value (80.4%, adjusted for a “typical” 33% prevalence rate of true progressors). These results were replicated in ADNI2 (N = 235), with 87.8% adjusted positive predictive value (96.7% specificity, 47.3% sensitivity, 85.1% accuracy).

Conclusions

We found that cognitive measures alone could identify high-risk individuals, with structural measurements providing a slight improvement. The signature had comparable receiver operating characteristics to standard machine learning tools, yet a marked improvement in positive predictive value was achieved over the literature by selecting a high-specificity operating point. The multimodal signature can be readily applied for the enrichment of clinical trials.

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  1. Now published in GigaScience doi: 10.1093/gigascience/giz055

    Angela Tam 1Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, CA2Douglas Hospital Research Centre, McGill University, Montréal, CA3McGill University, Montréal, CAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Angela TamFor correspondence: angela.tam@mail.mcgill.ca pierre.bellec@criugm.qc.caChristian Dansereau 1Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, CA4Département d’Informatique et de recherche opérationnelle, Université de Montréal, Montréal, CAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteYasser Itturia-Medina 3McGill University, Montréal, CAFind this author on Google ScholarFind this …