New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability

Abstract

The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e−06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.

Genetic model of MS severity predicts future accumulation of disability

“…(b) We observed a weak, but significant correlation between GeM‐MSS and the observed MS‐DSS in the validation cohort. MS‐DSS was calculated according to a published formula (Weideman et al., ) using clinical data from the last visit. (c) Relative influence of the 19 remaining variants in GeM‐MSS.…”

“…Unfortunately, multiple investigators have observed that MSSS and ARMSS do not predict future disability progression rates in moderately sized MS cohorts (Confavreux & Vukusic, ; Weideman et al. ), likely because EDSS is a discrete scale ranging from 0 to 10, which cannot reliably measure individualized disability progression rates in intervals shorter than 10 years. Using machine learning, we developed an MS severity outcome called the MS Disease Severity Scale (MS‐DSS) (Weideman et al., ) based on the data‐optimized, continuous Combinatorial Weight‐Adjusted Disability Scale (CombiWISE) (Kosa et al., ), ranging from 0 to 100), which also adjusts disability progression slopes for therapeutic effects of applied treatments.…”

“…), likely because EDSS is a discrete scale ranging from 0 to 10, which cannot reliably measure individualized disability progression rates in intervals shorter than 10 years. Using machine learning, we developed an MS severity outcome called the MS Disease Severity Scale (MS‐DSS) (Weideman et al., ) based on the data‐optimized, continuous Combinatorial Weight‐Adjusted Disability Scale (CombiWISE) (Kosa et al., ), ranging from 0 to 100), which also adjusts disability progression slopes for therapeutic effects of applied treatments. By explaining a much larger proportion of variance of future disability progression rates, MS‐DSS has more sensitivity in detecting biological modifiers of MS severity in comparison to MSSS or ARMSS.…”

Intrathecal B Cells in MS Have Significantly Greater Lymphangiogenic Potential Compared to B Cells Derived From Non-MS Subjects

“…To explore whether any of the differences identified between MS subjects and controls participate in CNS tissue destruction, we assessed correlation between patient-specific CSF B cells cytokine production measured by ELISA (computed as average concentration of specific cytokine derived from all BCL isolated from specific subject) and new, sensitive model of MS severity, MS-DSS ( 29 ). MS-DSS is derived from statistical learning.…”

“…Correlations between cytokines in the pilot cohort were assessed by Pearson correlation coefficients. In the validation cohort, we used Spearman correlation coefficients with a Bonferroni p -value adjustment to assess the correlation between cytokine concentrations and the MS disease severity scale (MS-DSS) ( 29 ), a new sensitive measure of MS disease severity.…”