Validating Predictors of Disease Progression in a Large Cohort of Primary-Progressive Multiple Sclerosis Based on a Systematic Literature Review

<div><p>Background</p><p>New agents with neuroprotective or neuroregenerative potential might be explored in primary-progressive Multiple Sclerosis (PPMS) - the MS disease course with leading neurodegenerative pathology. Identification of patients with a high short-term risk for progression may minimize study duration and sample size. Cohort studies reported several variables as predictors of EDSS disability progression but findings were partially contradictory.</p><p>Objective</p><p>To analyse the impact of published predictors on EDSS disease progression in a large cohort of PPMS patients.</p><p>Methods</p><p>A systematic literature research was performed to identify predictors for disease progression in PPMS. Individual case data from the Sylvia Lawry Centre (SLC) and the Hamburg MS patient database (HAPIMS) was pooled for a retrospective validation of these predictors on the annualized EDSS change.</p><p>Results</p><p>The systematic literature analysis revealed heterogeneous data from 3 prospective and 5 retrospective natural history cohort studies. Age at onset, gender, type of first symptoms and early EDSS changes were available for validation. Our pooled cohort of 597 PPMS patients (54% female) had a mean follow-up of 4.4 years and mean change of EDSS of 0.35 per year based on 2503 EDSS assessments. There was no significant association between the investigated variables and the EDSS-change.</p><p>Conclusion</p><p>None of the analysed variables were predictive for the disease progression measured by the annualized EDSS change. Whether PPMS is still unpredictable or our results may be due to limitations of cohort assessments or selection of predictors cannot be answered. Large systematic prospective studies with new endpoints are needed.</p></div

Median delta-EDSS in Women and Men.

<p>Boxplot with median, quartiles, 95% interval whiskers and outliers, delta-EDSS = annualized difference between first and last EDSS assessment, Wilcoxon rank-sum test: p = 0.1996. Two outliers with an annualized delta-EDSS >10 were excluded.</p

Descriptive statistics.

<p>Descriptive statistics for the pooled dataset and separately for the datasets from Hamburg (HH) respectively from the Sylvia Lawry Centre (SLC).</p

Association between Age at Onset and annualized EDSS progression.

<p>delta-EDSS = annualized difference between first and last EDSS assessment, Spearman's rank correlation r = −0.0359, p = 0.4508. One outlier with an annualized delta-EDSS >10 was excluded.</p

Association between Early and Late EDSS-Progression.

<p>Delta-EDSS in the first two years after baseline and delta-EDSS from year three on, delta-EDSS = annualized difference between first and last EDSS assessment, Spearman's rank correlation  =  −0.0445, p = 0.6536.</p

Median delta-EDSS and Type of First Symptoms.

<p>Groups split by absence or presence of motor symptoms at disease onset. Boxplot with median, quartiles and 95% interval whiskers, delta-EDSS = annualized difference between first and last EDSS assessment, Wilcoxon rank-sum test: p = 0.2418.</p

Boxplot showing relationship between walking speed and age.

<p>Boxplot showing relationship between walking speed and age.</p

Median (IQR) of gait parameters associated with age in open and closed datasets.

<p>Median (IQR) of gait parameters associated with age in open and closed datasets.</p

Median (IQR) of gait parameters associated with BMI in open and closed datasets.

<p>Median (IQR) of gait parameters associated with BMI in open and closed datasets.</p

Flow diagram of participant involvement and division of data into ‘open’ and‘closed’ datasets.

<p>Flow diagram of participant involvement and division of data into ‘open’ and‘closed’ datasets.</p