Royal Melbourne Hospital, University of Melbourne
Department of Neurology

Author Of 3 Presentations

Biostatistical Methods Poster Presentation

P0018 - Variability of the response to immunotherapy among sub-groups of patients with multiple sclerosis (ID 1239)

Abstract

Background

Our current understanding of demographic and clinical modifiers of the effectiveness of multiple sclerosis (MS) therapies is limited.

Objectives

To assess whether patients’ response to disease modifying therapies (DMT) in MS varies by disease activity (annualised relapse rate, presence of new MRI lesions), disability, age, MS duration or disease phenotype.

Methods

Using the international MSBase registry, we selected patients with MS followed for ≥1 year, with ≥3 visits, ≥1 visit per year. Marginal structural models (MSMs) were used to compare the hazard ratios (HR) of 6-month confirmed worsening and improvement of disability (EDSS), and the incidence of relapses between treated and untreated periods. MSMs were continuously re-adjusted for patient age, sex, pregnancy, date, time from first symptom, prior relapse history, disability and MRI activity.

Results

Among 23 687 patients with relapsing MS, those on DMT experienced 20% greater chance of disability improvement [HR 1.20 (95% CI 1.0-1.5)], 47% lower risk of disability worsening [HR 0.53 (0.39-0.71)] and 51% reduction in relapses [HR 0.49 (0.43-0.55)]. The effect of DMT on relapses and EDSS worsening was attenuated with longer MS duration and higher prior relapse rate. The effect of DMT on EDSS improvement and relapses was more evident in low EDSS categories. DMT was associated with 51% EDSS improvement in patients without new MRI lesions [HR 1.51 (1.00-2.28)] compared to 4% in those with MRI activity [HR 1.04 (0.88-1.24)]. Among 26329 participants with relapsing or progressive MS, DMT was associated with 25% reduction in EDSS worsening and 42% reduction in relapses in patients with relapsing MS [HR 0.75 (0.65-0.86) and HR 0.58 (CI 0.54-62), respectively], while evidence for such beneficial effects of treatment in patients with progressive MS was not found [HR 1.11 (0.91-1.46) and HR 1.16 (0.91-1.46), respectively].

Conclusions

DMTs are associated with reduction in relapse frequency, progression of disability, and increased chance of recovery from disability. In general, the effectiveness of DMTs was most pronounced in subgroups with shorter MS duration, lower EDSS, lower relapse rate and relapsing MS phenotype.

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Clinical Trials Poster Presentation

P0240 - Therapeutic Decisions in MS Care: An International Study comparing Clinical Judgement vs. Information from Artificial Intelligence-Based Models (ID 752)

Abstract

Background

The rapidly evolving therapeutic landscape of multiple sclerosis (MS) can make treatment decisions challenging. Novel tools using artificial intelligence (AI) can provide estimations of MS disease progression, which may aid MS therapeutic decisions. However, whether neurologists are willing to utilize information provided by AI-based models when making therapeutic decisions is unknown.

Objectives

To assess whether neurologists rely on clinical judgment (CJ) or quantitative/ qualitative estimations of disease progression provided by hypothetical AI-based models (assuming these models can reliably identify patients at high vs. low risk of disease progression) in simulated MS case scenarios.

Methods

Overall, 231 neurologists with expertise in MS from 20 countries were randomized to receive qualitative (high/low) or quantitative (85-90% vs. 15-20%) information regarding the likelihood of disease progression. Participants were presented with simulated MS case scenarios, and initially made 7 treatment decisions based on the clinical information using CJ. After randomization, participants made 10 treatment decisions using CJ and estimations of disease progression provided by AI models. We evaluated concordance and discordance of therapeutic decisions based on CJ and AI. The primary outcome was the proportion of “optimal” treatment decisions defined as treatment escalation when there was evidence of disease progression or continuing the same treatment when clinically stable. Mixed models were used to determine the effect of randomization group, case risk level, and CJ/AI. Clinicaltrials.gov #NCT04035720

Results

Of 300 neurologists invited to participate, 231 (77.0%) completed the study. Study participants had a mean age (SD) of 44 (±10) years. Of 2310 responses, 1702 (73.7%) were classified as optimal. Optimal decisions were more common for the high-risk vs. low-risk CJ group (84.5% vs 57.6%; p<0.001). There were no differences in the estimated odds of optimal responses between the quantitative vs. qualitative groups (OR 1.09; 95%CI 0.86, 1.39) after adjustment for pre-intervention responses. The estimated odds of optimal decisions for the high-risk vs low-risk CJ group was 2.96 (95%CI: 2.47, 3.56 ) after adjusting for group, pre-intervention responses, and AI-based estimations. For low-risk CJ cases, additional input by AI-based estimations was associated with a lower likelihood of optimal responses; being worse for high-risk vs. low-risk AI estimations (OR 0.235; 95%CI: 0.16, 0.340) adjusting for covariables.

Conclusions

Neurologists were more likely to make optimal treatment choices for high-risk simulated scenarios. The addition of hypothetical information provided by AI-based models- did not improve treatment decisions for low-risk cases. These results provide a framework for understanding therapeutic decision-making in MS neurologists, who are more reliant on their own CJ over AI-based tools.

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Imaging Poster Presentation

P0636 - Relationship of real-world brain atrophy to MS disability using icobrain: 4 centre pilot study (ID 716)

Abstract

Background

To date, no studies have explored the relationship between brain atrophy and MS disability using differing MRI protocols and scanners at multiple sites.

Objectives

To assess the association between brain atrophy and MS disability, as measured by EDSS and 6-month confirmed disability progression (CDP).

Methods

In this retrospective study at 4 MS centres, a total of 1300 patients had brain MRI imaging assessed by icobrain. Relapse-onset MS patients were included if they had two clinical MRIs 12 (±3) months apart and ≥2 EDSS scores post MRI-2, the first ≤3 months from MRI-2, with ≥6 months between first and last EDSS. Volumetric data were analysed if the alignment similarity between two images was as good as that of same-scanner scan-rescan images (normalised mutual information ≥0.2). The percentage brain volume change (PBVC), percentage grey matter change (PGMC), FLAIR lesion volume change, whole brain volume, grey matter volume, FLAIR lesion volume and T1 hypointense lesion volume at MRI-2 were calculated. Ordinal mixed effect models were used to determine the association between these volumetric MRI measures and all EDSS scores post MRI-2. Cox proportional hazards models were used for the 6-month CDP outcome, using a subset of patients with ≥3 EDSS. Models were adjusted for proportion of time spent on disease-modifying therapy during MRIs ± whole brain/grey matter volume at baseline MRI.

Results

Of the 260 relapse-onset MS patients included, 204 (78%) MRI pairs were performed in the same scanner and 56 (22%) pairs were from different scanners. During the follow-up period (median 3.8 years, range 1.3-8.9), 29 of 244 (12%) patients experienced 6-month CDP. There was no evidence for association between annualised PBVC or PGMC and CDP or EDSS (p>0.05). Cross-sectional whole brain and grey matter volume (at MRI-2) tended to associate with CDP (HR 0.99, 95% CI 0.98-1.00, p=0.06). Every 1ml of whole brain or grey matter volume lost represented a 1% higher chance of reaching 6-month CDP. Only whole brain volume (at MRI-2) was associated with EDSS score (β -0.03, SE 0.01, p<0.001) and the slope of EDSS change over time (β -0.001, SE 0.0003, p=0.02). On average, every 33ml reduction of brain volume was associated with a 1 step increase in EDSS.

Conclusions

In this real-world clinical setting where a fifth of the brain atrophy analysis were performed on different scanners, we found no association between individual brain atrophy and MS disability. However, there was an association between cross-sectional whole brain volume with EDSS and slope of EDSS change.

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