ABSTRACT
Dynamic survival analysis is a variant of traditional survival analysis where time-to-event predictions are updated as new information arrives about an individual over time. In this paper we propose a new approach to dynamic survival analysis based on learning a global parametric distribution, followed by individualization via truncating and renormalizing that distribution at different locations over time. We combine this approach with a likelihood-based loss that includes predictions at every time step within an individual's history, rather than just including one term per individual. The combination of this loss and model results in an interpretable approach to dynamic survival, requiring less fine tuning than existing methods, while still achieving good predictive performance. We evaluate the approach on the problem of predicting hospital mortality for a dataset with over 6900 COVID-19 patients.
ABSTRACT
Background: Neurological disability progression occurs across the spectrum of people living with multiple sclerosis (PwMS). Currently, no treatments exist that substantially modify the course of clinical progression in MS, one of the greatest unmet needs in clinical practice. Characterizing the determinants of clinical progression is essential for the development of novel therapeutic agents and treatment approaches that target progression in PwMS. Objectives: The overarching aim of CanProCo is to evaluate a wide spectrum of factors associated with the onset and rate of disease progression in MS, and to describe how these factors interact with one another to influence progression. Methods: CanProCo is a prospective, observational cohort study aiming to recruit 1000 individuals with radiologically-isolated syndrome (RIS), relapsing-remitting MS (RRMS), and primary-progressive MS (PPMS) within 10-15 years of disease onset, and 50 healthy controls (HCs) from five large academic MS centers in Canada. Participants undergo detailed clinical evaluations annually. A subset of participants enrolled within 5-10 years of disease onset (n=500) also have blood, cerebrospinal fluid, and MRIs collected facilitating study of biological measures (e.g. single-cell RNAsequencing[ scRNASeq]), MRI-based microstructural assessment, participant characteristics (self-reported, performance-based, clinician- assessed, health-system based), and environmental factors as determinants contributing to the differential progression in MS. Results: Recruitment commenced in April/May 2019 and n=536 patients have been recruited to date (RRMS=457, PPMS=35, RIS=25, HC=19). Baseline age, sex distribution, and Expanded Disability Status Scale (EDSS) scores (median, range) of each subgroup are: RRMS=38 years, 73% female, EDSS=1.5 (0-6.0);PPMS=52 years, 40% female, EDSS=4.0 (1.5-6.5);RIS=41 years, 68% female, EDSS=0 (0-3.0);HC=37 years, 63% female. Recruitment has surpassed the 50% target but has been paused due to the COVID-19 pandemic. scRNASeq on frozen blood samples has been validated. Conclusions: Halting the progression of MS is a fundamental clinical need to improve the lives of PwMS. Achieving this requires leveraging transdisciplinary approaches to better characterize mechanisms underlying clinical progression. CanProCo is the first prospective cohort study aiming to characterize these determinants to inform the development and implementation of efficacious and effective interventions.