Author Of 3 Presentations
LB1282 - Machine learning of deep grey matter volumes on MRI for predicting new disease activity after a first clinical demyelinating event (ID 2182)
Abstract
Background
Deep grey matter (DGM) atrophy is a feature in all multiple sclerosis (MS) phenotypes. Studies have shown a strong relationship between DGM atrophy and clinical worsening but the utility of DGM volumes for predicting disease activity is largely unexplored, especially in early disease. Machine learning (ML) is a computational approach that can identify patterns that predict disease outcomes. In ML, the study dataset is divided into training and test subsets. The training set contains known outcomes, which the ML algorithm uses to form a prediction model, which is then evaluated on the test set.
Objectives
To develop an ML model for predicting new disease activity (clinical or MRI) within 2 years of a first clinical demyelinating event, using baseline DGM volumes. The motivation is to identify individuals at higher risk of new disease activity.
Methods
3D T1-weighted MRIs acquired within 90 days of a first clinical event in 140 subjects from a completed placebo-controlled trial of minocycline were used. Eighty subjects had new disease activity within 2 years, 28 were stable, and 32 withdrew early (unknown outcome). The stable and unknown groups were combined into 1 for ML training. Advanced Normalization Tools and FMRIB Software Library were used to segment the thalami, putamina, globi pallidi, and caudate nuclei. A random forest ML model was trained to predict new disease activity with feature vectors composed of individual DGM nuclei volumes and several other variables (e.g., minocycline vs. placebo, mono-focal vs. multi-focal CIS, normalized brain volume, and sex). Model performance was evaluated using 3-fold cross-validation, with 80% of the data used for training, and the rest for testing.
Results
Sequential elimination of variables ranked the least important by the trained model resulted in improved classification accuracy. Therefore, the less predictive variables were pruned from the feature vector. The best model used DGM volumes alone and achieved 82.1% accuracy, 87% precision, 81% recall and F1-score of 0.84 with area under the curve (AUC) of 0.76.
Conclusions
ML can learn patterns predictive of new disease activity within 2 years after a first clinical demyelinating event from baseline DGM volumes. This approach can potentially augment the many other clinical and demographic variables used in a typical MS clinical work up. Further investigation with larger data sets is warranted to determine generalizability of the approach.
P0691 - Autologous Hematopoietic Stem Cell Transplantation in Neuromyelitis Optica – Year 5 Update (ID 1042)
Abstract
Background
Neuromyelitis Optica Spectrum Disorder (NMO/NMOSD) is an immune astrocytopathy characterized by disabling attacks of optic nerves, spine, the area postrema, hypothalamus and other CNS regions. Although new, targeted therapies have recently been approved, they require indefinite use, with data limited essentially to positive aquaporin-4 (AQP4) patients. As well, many patients may not have access to such agents and still rely on older, harsher immunosuppressants. The immunological features of NMO/NMOSD, coupled with its severity, make it an ideal candidate for trials of autologous hematopoietic stem cell transplantation (AHSCT), with the goal of disease remission and freedom from long-term treatment. Several small studies, with a variety of transplant regimens, have been presented thus far, with mixed results.
Objectives
To determine if NMO/NMOSD patients who have failed standard immune maintenance therapy, experience a reduction in relapse and disability without need for immune therapy after AHSCT.
Methods
Starting in 2010, patients were eligible and enrolled if they met Wingerchuk 2006 criteria for NMO, aged 18-65, with => 1 relapse in 12 months or => 2 in 24 months despite immunotherapy and EDSS < 6.5. Patients underwent non-ablative stem cell mobilization and infusion using cyclophosphamide (200mg/kg - divided as 50mg/kg/day over days -5 to -2), ATG and rituximab. The primary endpoint was a 50% reduction in relapse rate at year 3 (secondary at year 5). Additional outcomes included annualized relapse rate (ARR), EDSS, MRI, AQP4 serostatus, and optical coherence tomography over 5 years. Ten subjects were to be enrolled to provide 80% power.
Results
Between 2010-2015, 3 patients were enrolled and underwent AHSCT. A 28F, AQP4-, was transplanted in 2011. Her ARR dropped from 5 to 0 at both year 3 and 5, with her EDSS dropping from 4.0 to 2.0. A 36F, AQP4+, transplanted in 2012, had a reduction in her ARR from 4 to 0.67 at year 3, and 0.5 at year 5, with her EDSS dropping from 4.5 to 3.0 throughout the trial. She was treated with MMF after her two mild post-transplant relapses. The final patient, a 39M, AQP4+ was transplanted in 2014. He had a precipitous decline with an ARR increasing from 1.3 to 2 at year 3 and 5, and an EDSS increasing from 3.5 to 7.5 at year 3, with death from NMO at year 3.5. As well, both seropositive patients remained seropositive after transplant. The trial was closed in 2016 due to challenges in recruitment.
Conclusions
While the small cohort size limits interpretation, two of three patients had a marked improvement in their NMO activity and disability after AHSCT. Regimen selection and patient features may speak to the success and failures in this trial and other published studies, but it appears that AHSCT in NMO/NMOSD is a viable option worthy of further study and refinement.
P0919 - The Canadian Prospective Cohort (CanProCo) Study to Understand Progression in Multiple Sclerosis: Rationale and Baseline Characteristics (ID 1236)
Abstract
Background
Neurological disability progression occurs across the spectrum of people living with multiple sclerosis (PwMS). Currently, no treatments exist that substantially modify the course of clinical progression in MS, one of the greatest unmet needs in clinical practice. Characterizing the determinants of clinical progression is essential for the development of novel therapeutic agents and treatment approaches that target progression in PwMS.
Objectives
The overarching aim of CanProCo is to evaluate a wide spectrum of factors associated with the onset and rate of disease progression in MS, and to describe how these factors interact with one another to influence progression.
Methods
CanProCo is a prospective, observational cohort study aiming to recruit 1000 individuals with radiologically-isolated syndrome (RIS), relapsing-remitting MS (RRMS), and primary-progressive MS (PPMS) within 10-15 years of disease onset, and 50 healthy controls (HCs) from five large academic MS centers in Canada. Participants undergo detailed clinical evaluations annually. A subset of participants enrolled within 5-10 years of disease onset (n=500) also have blood, cerebrospinal fluid, and MRIs collected facilitating study of biological measures (e.g. single-cell RNA-sequencing[scRNASeq]), MRI-based microstructural assessment, participant characteristics (self-reported, performance-based, clinician-assessed, health-system based), and environmental factors as determinants contributing to the differential progression in MS.
Results
Recruitment commenced in April/May 2019 and n=536 patients have been recruited to date (RRMS=457, PPMS=35, RIS=25, HC=19). Baseline age, sex distribution, and Expanded Disability Status Scale (EDSS) scores (median, range) of each subgroup are: RRMS=38 years, 73% female, EDSS=1.5 (0-6.0); PPMS=52 years, 40% female, EDSS=4.0 (1.5-6.5); RIS=41 years, 68% female, EDSS=0 (0-3.0); HC=37 years, 63% female. Recruitment has surpassed the 50% target but has been paused due to the COVID-19 pandemic. scRNASeq on frozen blood samples has been validated.
Conclusions
Halting the progression of MS is a fundamental clinical need to improve the lives of PwMS. Achieving this requires leveraging transdisciplinary approaches to better characterize mechanisms underlying clinical progression. CanProCo is the first prospective cohort study aiming to characterize these determinants to inform the development and implementation of efficacious and effective interventions.