A data-driven model of disability progression in progressive multiple sclerosis

This study applies the Gaussian process progression model, a Bayesian data-driven disease progression model, to analyse the evolution of primary progressive multiple sclerosis. Utilizing data from 1521 primary progressive multiple sclerosis participants collected within the International Progressive Multiple Sclerosis Alliance Project, the analysis includes 18 581 longitudinal time-points (average follow-up time: 28.2 months) of disability assessments including the expanded disability status scale, symbol digit modalities, timed 25-foot-walk, 9-hole-peg test and of MRI metrics such as T1 and T2 lesion volume and normalized brain volume. From these data, Gaussian process progression model infers a data-driven description of the progression common to all individuals, alongside scores measuring the individual progression rates relative to the population, spanning ∼50 years of disease duration. Along this timeline, Gaussian process progression model identifies an initial steep worsening of the expanded disability status scale that stabilizes after ∼30 years of disease duration, suggesting its diminished utility in monitoring disease progression beyond this time. Conversely, it underscores the slower evolution of normalized brain volume across the disease duration. The individual progression rates estimated by Gaussian process progression model can be used to identify three distinct sub-groups within the primary progressive multiple sclerosis population: a normative group (76% of the population) and two 'outlier' sub-groups displaying either accelerated (13% of the population) or decelerated (11%) progression compared to the normative one. Notably, fast progressors exhibit older age at symptom onset (38.5 versus 35.0, <i>P</i> < 0.0001), a higher prevalence of males (61.1% versus 48.5%, <i>P</i> = 0.013) and a higher lesion volumes both in T1 (4.1 versus 0.6, <i>P</i> < 0.0001) and T2 (16.5 versus 7.9, <i>P</i> < 0.0001) compared to slow progressors. Prognostically, fast progressors demonstrate a significantly worse prognosis, with double the risk of experiencing a 3-month confirmed disease progression on expanded disability status scale compared to the normative population according to Cox proportional hazard modelling (HR = 2.09, 95% CI: 1.66-2.62, <i>P</i> < 0.0001) and a shorter median time from the onset of disease symptoms to reaching a confirmed expanded disability status scale 6 (95% CI: 5.83-7.68 years, <i>P</i> < 0.0001). External validation on a test set comprising 227 primary progressive multiple sclerosis participants from the SPI2 trial produced consistent results, with slow progressors exhibiting a reduced risk of experiencing 3-month confirmed disease progression determined through expanded disability status scale (HR = 0.21), while fast progressors facing an increased risk (HR = 1.45). This study contributes to our understanding of disability accrual in primary progressive multiple sclerosis, integrating diverse disability assessments and MRI measurements. Moreover, the identification of distinct sub-groups underscores the heterogeneity in progression rates among patients, offering invaluable insights for patient stratification and monitoring in clinical trials, potentially facilitating more targeted and personalized interventions.

Authors

Institutions

Publication Details

Journal
Brain Communications
Published
2024-12-03
DOI
https://doi.org/10.1093/braincomms/fcae434
Citations
3
Primary Topic
Multiple Sclerosis Research Studies
Type
article
Field-Weighted Citation Impact
1.27

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A data-driven model of disability progression in progressive multiple sclerosis

Francesca Bovis, Bruce Cree, Carmen Tur, Sara Garbarino et al.
3 citations
Brain Communications
Multiple Sclerosis Research Studies
1.27
article

A data-driven model of disability progression in progressive multiple sclerosis

Francesca Bovis, Bruce Cree, Carmen Tur, Sara Garbarino, Antonio Uccelli, Maria Pia Sormani, Marco Lorenzi, Douglas Lorne Arnold, Matteo Pardini, Michele Piana
article en
3 citations

Abstract

This study applies the Gaussian process progression model, a Bayesian data-driven disease progression model, to analyse the evolution of primary progressive multiple sclerosis. Utilizing data from 1521 primary progressive multiple sclerosis participants collected within the International Progressive Multiple Sclerosis Alliance Project, the analysis includes 18 581 longitudinal time-points (average follow-up time: 28.2 months) of disability assessments including the expanded disability status scale, symbol digit modalities, timed 25-foot-walk, 9-hole-peg test and of MRI metrics such as T1 and T2 lesion volume and normalized brain volume. From these data, Gaussian process progression model infers a data-driven description of the progression common to all individuals, alongside scores measuring the individual progression rates relative to the population, spanning ∼50 years of disease duration. Along this timeline, Gaussian process progression model identifies an initial steep worsening of the expanded disability status scale that stabilizes after ∼30 years of disease duration, suggesting its diminished utility in monitoring disease progression beyond this time. Conversely, it underscores the slower evolution of normalized brain volume across the disease duration. The individual progression rates estimated by Gaussian process progression model can be used to identify three distinct sub-groups within the primary progressive multiple sclerosis population: a normative group (76% of the population) and two 'outlier' sub-groups displaying either accelerated (13% of the population) or decelerated (11%) progression compared to the normative one. Notably, fast progressors exhibit older age at symptom onset (38.5 versus 35.0, <i>P</i> < 0.0001), a higher prevalence of males (61.1% versus 48.5%, <i>P</i> = 0.013) and a higher lesion volumes both in T1 (4.1 versus 0.6, <i>P</i> < 0.0001) and T2 (16.5 versus 7.9, <i>P</i> < 0.0001) compared to slow progressors. Prognostically, fast progressors demonstrate a significantly worse prognosis, with double the risk of experiencing a 3-month confirmed disease progression on expanded disability status scale compared to the normative population according to Cox proportional hazard modelling (HR = 2.09, 95% CI: 1.66-2.62, <i>P</i> < 0.0001) and a shorter median time from the onset of disease symptoms to reaching a confirmed expanded disability status scale 6 (95% CI: 5.83-7.68 years, <i>P</i> < 0.0001). External validation on a test set comprising 227 primary progressive multiple sclerosis participants from the SPI2 trial produced consistent results, with slow progressors exhibiting a reduced risk of experiencing 3-month confirmed disease progression determined through expanded disability status scale (HR = 0.21), while fast progressors facing an increased risk (HR = 1.45). This study contributes to our understanding of disability accrual in primary progressive multiple sclerosis, integrating diverse disability assessments and MRI measurements. Moreover, the identification of distinct sub-groups underscores the heterogeneity in progression rates among patients, offering invaluable insights for patient stratification and monitoring in clinical trials, potentially facilitating more targeted and personalized interventions.

Brain CommunicationsVol. 7(1)
Universitat Autònoma de Barcelona (ES), Institut national de recherche en sciences et technologies du numérique (FR), University of California, San Francisco (US), Université Côte d'Azur (FR), Ospedale Policlinico San Martino (IT), McGill University (CA), University of Genoa (IT)
National Multiple Sclerosis Society, Biogen, Teva Pharmaceutical Industries, Istituto Nazionale di Alta Matematica "Francesco Severi", International Progressive MS Alliance, Ministero della Salute, Ministerio de Ciencia e Innovación, MedDay Pharmaceuticals, Genentech, Gruppo Nazionale per il Calcolo Scientifico, Instituto de Salud Carlos III
Openalex Percentile: Top 19%
Multiple Sclerosis Research Studies
1.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.