Table 2.

Association between innate immune cell activation and EDSS progression

Predictors in the final model	Stepwise logistic regression
	Estimate	OR	P-value
All patients (n = 69)
EDSS at baseline	0.33	1.39	0.102
ARR during follow-up	3.41	1.41a	0.012*
Moderate efficacy DMTb	−0.92	0.4	0.228
High efficacy DMTc	−3.17	0.04	0.038*
DVR in NAWM	14.5	4.26a	0.048*
Patients not experiencing relapses during the follow-up (n = 51)
DVR in perilesional NAWM	15.2	4.57a	0.013*
Moderate efficacy DMTb	−1.92	0.15	0.044*
High efficacy DMTc	−0.65	0.52	0.616

EDSS progression was modelled using forward-type stepwise logistic regression. Here, testing was begun with no variables in the model and the addition of each variable was tested using the Akaike information criterion. Most significant improvement of the fit determined the inclusion of the variable. The process was repeated until no variable improved the model. The table shows the variables that remained in the model at the end. Among the entire multiple sclerosis cohort the first variable chosen by the Akaike information criterion to add to the model was EDSS at baseline and each of the other variables listed in the table further improved the model fit to predict progression. In the cohort with no relapses, the first variable to add to the model was DVR in the perilesional NAWM.

All variables considered in model building are listed in detail in the ‘Materials and methods’ section. Estimates are logarithmic odds ratios (OR).

^aFor DVR variables and ARR, the odds ratio is calculated as 0.1 unit increase due to the scale of the variables as explained in the ‘Materials and methods’ section.

^bClass of the DMT at baseline or at most 2 months before: moderate efficacy DMT versus no DMT.

^cClass of the DMT at baseline or at most 2 months before: high efficacy DMT versus no DMT.

^*Statistical significance at a level of P < 0.05.