Background

To predict the clinical course of myelin oligodendrocyte glycoprotein (MOG)-antibody (Ab)-associated disease (MOGAD) is essential to guide treatment recommendations.

Objectives

We aimed to 1) compare clinical features and disease course, and 2) to evaluate the association of MOG-Ab dynamics and relapses, between children and adults with MOGAD.

Methods

Retrospective study evaluating clinical features of 98 children and 266 adults with MOGAD, between January 2014 and September 2019. To analyse relapses over the whole disease course, a Cox regression analysis for recurrent time-to-event data was performed, introducing treatment as time-dependent covariate. To evaluate dynamics, delta mean fluorescence intensity ratio signal (ΔMFIratio) of MOG-Ab was measured in patients with a minimum time elapsed between two samples of 4 months.

Results

Median age at onset of symptoms was 10.9 (interquartile range 5.4-14.3) years in children and 36.2 (27.7-47.6) in adults. Isolated optic neuritis was the most frequent clinical presentation both in children (40.8%) and adults (55.9%), p=0.013, and acute disseminated encephalomyelitis syndrome was more frequent in children (36.7% vs. 5.6%; p<0.001). Compared to adults, children displayed a better recovery (EDSS ≥3.0 at last follow-up reached only by 10 of 97 [10.3%] vs. 66/247 [26.7%], p<0.001).

In the multivariate analysis, adults were at higher risk of relapse than children (Hazard ratio 1.41, 95%Confidence interval [CI] 1.12-1.78; p=0.003). Among the 124 participants evaluated for MOG-Ab dynamics, 36.3% became seronegative, 60.5% decrease and 3.2% increase the ΔMFIratio. At two years, 64.2% (95%CI 40.9-86.5) of non-relapsing children became MOG-Ab negative compared to 14.1% (95%CI 4.7-38.3) of relapsing ones, log-rank p<0.001, with no differences observed between non-relapsing and relapsing adults, log-rank p=0.280.

Conclusions

MOGAD differs in its clinical presentation at onset, showing a progressive shift in the clinical features across age-groups. Compared to children, adults have a higher risk of relapses and a worse functional recovery. Finally, children with monophasic disease became MOG-Ab negative earlier than relapsing ones, but not in adults. Considering these differences, management and treatment guidelines should be considered independently in children and adults.

Background

Treatment response in multiple sclerosis (MS) is frequently suboptimal and, in many cases, different disease-modifying therapies need to be tested. The expanded disability status scale (EDSS) score and other markers of disease activity (such as relapses, new lesions, brain atrophy, etc.) are crucial for treatment decision making. However, EDSS suffers from poor reliability, repeatability, and high inter-rater variability. Therefore, automatic and objective disability scoring using MRI information could help to monitor disease progression reliably and optimize treatment.

Objectives

Develop a machine-learning model that learns relations between MRI-based brain volumes and clinical disability measured by EDSS.

Methods

Multi-center FLAIR and T1 MRI data from 325 MS patients were used. Individuals were rated in each center using EDSS within 0-89 days before or after the MRI scan. Automated image analysis was performed using icobrain, providing volumetric quantification of gray matter, white matter, whole brain, lateral ventricles, T1 hypointense and FLAIR hyperintense lesions. Moreover, other features, such as age, sex, and center, were available. A machine-learning model based on random forest regression was built for estimating EDSS automatically from these features. The model’s performance was assessed by means of mean squared error (MSE) and mean absolute error (MAE) evaluated overall, and on two EDSS subgroups, <=4 and >4,in a 100 repetition 10-fold-cross-validation fashion. Subsequently, the percentage of cases for which the automatic EDSS was within 1.5, 1 or 0.5 points, respectively, from the clinically reported EDSS was computed.

Results

The proposed automatic EDSS estimation model obtained MSE=2.36±0.03, MAE=1.24±0.89 for the overall interval, with MSE=1.83±0.04, MAE=1.11±0.76 for EDSS<=4 (N=200) and MSE=3.26±0.06, MAE=1.46±1.05 for EDSS>4 (N=118). The percentage of cases with absolute error strictly below 1.5, 1 and 0.5 EDSS points was 67%, 46% and 24%, respectively.

Conclusions

A good match between the automatic EDSS and the measured EDSS is only possible up to a certain extent, suggesting that the MRI-based EDSS score might also capture complementary information on disease activity compared to the clinically measured EDSS. Understanding such differences is a prerequisite for predicting future disability progression in MS.