Forecasting progression of mild cognitive impairment (MCI) and Alzheimer's disease (AD) with digital twins. (31st December 2021)

• Record Type: Journal Article
• Authors: Bertolini, Daniele; Loukianov, Anton D.; Smith, Aaron; Li‐Bland, David; Pouliot, Yannick; Walsh, Jonathan R.; Fisher, Charles K.
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 9
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: 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: 10.1002/alz.054414
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Machine learning models can leverage historical data to forecast disease progression. These predictions can be integrated in clinical trial design to reduce sample size or increase power, speeding up the evaluation of new drugs. This is especially critical in AD where trials face challenges with enrollment and where no new therapies have emerged in 18 years. Recently, there has been a shift towards evaluating drugs in earlier stages of the disease using Clinical Dementia Rating Sum‐of‐Boxes (CDR‐SB) as the primary endpoint. We present a model that includes CDR‐SB and spans a broad range of baseline disease severity from MCI to mild‐to‐moderate AD. Method: We used nearly 7, 000 clinical records from placebo arms of AD clinical trials (in the C‐Path Online Data Repository for AD) and from observational studies (in the AD Neuroimaging Initiative) to train Conditional Restricted Boltzmann Machines (CRBMs). A CRBM is a generative machine learning model that learns a multivariate distribution over the relevant variables, and is particularly suited to model clinical data. A CRBM generates Digital Twins ‐ synthetic clinical records with baseline characteristics matched to those of actual trial subjects describing their likely progression under standard‐of‐care (SOC) with/without placebo. These forecasts include all components of CDR‐SB, Alzheimer's Disease Assessment Scale ‐ Cognitive Subscale (ADAS‐Cog11) and Mini Mental State Examination (MMSE), along with other variables including labs, vitals, and biomarker status. We evaluated Digital Twins on a held‐out portion of the dataset (not included in the training dataset), stratified by baseline ADAS‐Cog11. For each variable, the mean change from baseline +/‐ 95% confidence interval error at 3‐month intervals was estimated by drawing 100 Digital Twins for each subject and averaging over the population. Result: CRBM‐generated predictions of ADAS‐Cog11, MMSE, and CDR‐SB progression up to 18 months were consistent within 95% confidence intervals with actual data across all scores, timepoints, and cohorts. Conclusion: Digital Twins accurately model MCI and AD subjects' progression on SOC with/without placebo across a broad range of baseline disease severity, and can be integrated into clinical trials as prognostic scores to increase power or reduce sample size required to achieve desired power.