Modelling disease progression in relapsing-remitting onset multiple sclerosis using multilevel models applied to longitudinal data from two natural history cohorts and one treated cohort

Modeling and simulation for prediction of multiple sclerosis progression

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Clinical impact of gender and age at onset on disease trajectory in primary progressive multiple sclerosis patients

BACKGROUND AND OBJECTIVES

Primary-progressive multiple sclerosis (PPMS) is characterized by gradual neurological deterioration without relapses. This study aimed to investigate the clinical impact of gender and age at disease onset on disease progression and disability accumulation in patients with this disease phenotype.

METHODS

Secondary data from the RelevarEM registry, a longitudinal database in Argentina, were analyzed. The cohort comprised patients with PPMS who met inclusion criteria. Statistical analysis with multilevel Bayesian robust regression modeling was conducted to assess the associations between gender, age at onset, and Expanded Disability Status Scale (EDSS) score trajectories.

RESULTS

We identified 125 patients with a confirmed diagnosis of PPMS encompassing a total of 464 observations. We found no significant differences in EDSS scores after 10 years of disease progression between genders (-0.08; credible interval (CI): -0.60, 0.42). A 20-year difference in age at onset did not show significant differences in EDSS score after 10 years of disease progression (0.281; CI: -0.251, 0.814). Finally, we also did not find any clinically relevant difference between gender EDSS score with a difference of 20 years in age at onset (-0.021; CI: -0.371, 0.319).

CONCLUSION

Biological plausibility of gender and age effects does not correlate with clinical impact measured by EDSS score.

Premorbid Sociodemographic Status and Multiple Sclerosis Outcomes in a Universal Health Care Context

Importance

Multiple sclerosis (MS) severity may be informed by premorbid sociodemographic factors.

Objective

To determine whether premorbid education, income, and marital status are associated with future MS disability and symptom severity, independent of treatment, in a universal health care context.

Design, Setting, and Participants

This nationwide observational cohort study examined data from the Swedish MS Registry linked to national population registries from 2000 to 2020. Participants included people with MS onset from 2005 to 2015 and of working age (aged 23 to 59 years) 1 year and 5 years preceding disease onset.

Exposures

Income quartile, educational attainment, and marital status measured at 1 and 5 years preceding disease onset.

Main Outcome and Measures

Repeated measures of Expanded Disability Status Scale (EDSS) scores and patient-reported Multiple Sclerosis Impact Scale (MSIS-29) scores. Models were adjusted for age, sex, relapses, disease duration, and treatment exposure. Secondary analyses further adjusted for comorbidity. All analyses were stratified by disease course (relapse onset and progressive onset).

Results

There were 4557 patients (mean [SD] age, 37.5 [9.3] years; 3136 [68.8%] female, 4195 [92.1%] relapse-onset MS) with sociodemographic data from 1-year preonset of MS. In relapse-onset MS, higher premorbid income and education correlated with lower disability (EDSS, -0.16 [95% CI, -0.12 to -0.20] points) per income quartile; EDSS, -0.47 [95% CI, -0.59 to -0.35] points if tertiary educated), physical symptoms (MSIS-29 physical subscore, -14% [95% CI, -11% to -18%] per income quartile; MSIS-29 physical subscore, -43% [95% CI, -35% to -50%] if tertiary educated), and psychological symptoms (MSIS-29 psychological subscore, -12% [95% CI, -9% to -16%] per income quartile; MSIS-29 psychological subscore, -25% [95% CI, -17% to -33%] if tertiary educated). Marital separation was associated with adverse outcomes (EDSS, 0.34 [95% CI, 0.18 to 0.51]; MSIS-29 physical subscore, 35% [95% CI, 12% to 62%]; MSIS-29 psychological subscore, 25% [95% CI, 8% to 46%]). In progressive-onset MS, higher income correlated with lower EDSS (-0.30 [95% CI, -0.48 to -0.11] points per income quartile) whereas education correlated with lower physical (-34% [95% CI, -53% to -7%]) and psychological symptoms (-33% [95% CI, -54% to -1%]). Estimates for 5-years preonset were comparable with 1-year preonset, as were the comorbidity-adjusted findings.

Conclusions and relevance

In this cohort study of working-age adults with MS, premorbid income, education, and marital status correlated with disability and symptom severity in relapse-onset and progressive-onset MS, independent of treatment. These findings suggest that socioeconomic status may reflect both structural and individual determinants of health in MS.

The Development of Cladribine Tablets for the Treatment of Multiple Sclerosis: A Comprehensive Review

Cladribine is a purine nucleoside analog initially developed in the 1970s as a treatment for various blood cancers. Due to the molecule’s ability to preferentially reduce T and B lymphocytes, it has been developed into an oral formulation for the treatment of multiple sclerosis (MS). The unique proposed mechanism of action of cladribine allows for the therapy to be delivered orally over two treatment-week cycles per year, one cycle at the beginning of the first month and one cycle at the beginning of the second month of years 1 and 2, with the potential for no further cladribine treatment required in years 3 and 4. This review summarizes the clinical development program for cladribine tablets in patients with MS, including the efficacy endpoints and results from the 2-year phase III CLARITY study in patients with relapsing–remitting MS (RRMS), the 2-year CLARITY EXTENSION study, and the phase III ORACLE-MS study in patients with a first clinical demyelinating event at risk for developing MS. Efficacy results from the phase II ONWARD study, in which cladribine tablets were administered as an add-on to interferon-β therapy in patients with RRMS, are also summarized. A review of all safety data, including lymphopenia, infections, and malignancies, is provided based on data from all trials in patients with MS, including the initial parenteral formulation studies. Based on these data, cladribine tablets administered at 3.5 mg/kg over 2 years have been approved across the globe for various forms of relapsing MS.

Mapping of critical events in disease progression through binary classification: Application to amyotrophic lateral sclerosis

BACKGROUND

The progression of many degenerative diseases is tracked periodically using scales evaluating functionality in daily activities. Although estimating the timing of critical events (i.e., disease tollgates) during degenerative disease progression is desirable, the necessary data may not be readily available in scale records. Further, analysis of disease progression poses data challenges, such as censoring and misclassification errors, which need to be addressed to provide meaningful research findings and inform patients.

METHODS

We developed a novel binary classification approach to map scale scores into disease tollgates to describe disease progression leveraging standard/modified Kaplan-Meier analyses. The approach is demonstrated by estimating progression pathways in amyotrophic lateral sclerosis (ALS). Tollgate-based ALS Staging System (TASS) specifies the critical events (i.e., tollgates) in ALS progression. We first developed a binary classification predicting whether each TASS tollgate was passed given the itemized ALSFRS-R scores using 514 ALS patients' data from Mayo Clinic-Rochester. Then, we utilized the binary classification to translate/map the ALSFRS-R data of 3,264 patients from the PRO-ACT database into TASS. We derived the time trajectories of ALS progression through tollgates from the augmented PRO-ACT data using Kaplan-Meier analyses. The effects of misclassification errors, condition-dependent dropouts, and censored data in trajectory estimations were evaluated with Interval Censored Kaplan Meier Analysis and Multistate Model for Panel Data.

RESULTS

The approach using Mayo Clinic data accurately estimated tollgate-passed states of patients given their itemized ALSFRS-R scores (AUCs > 0.90). The tollgate time trajectories derived from the augmented PRO-ACT dataset provide valuable insights; we predicted that the majority of the ALS patients would have modified arm function (67%) and require assistive devices for walking (53%) by the second year after ALS onset. By the third year, most (74%) ALS patients would occasionally use a wheelchair, while 48% of the ALS patients would be wheelchair-dependent by the fourth year. Assistive speech devices and feeding tubes were needed in 49% and 30% of the patients by the third year after ALS onset, respectively. The onset body region alters some tollgate passage time estimations by 1-2 years.

CONCLUSIONS

The estimated tollgate-based time trajectories inform patients and clinicians about prospective assistive device needs and life changes. More research is needed to personalize these estimations according to prognostic factors. Further, the approach can be leveraged in the progression of other diseases.

Longitudinal analysis of disability outcomes among young people with MS

BACKGROUND

The age of onset of MS appears to influence the course of disease progression and people with younger age of onset might have a different disability trajectory.

OBJECTIVES

To identify longitudinal patterns of disability progression, as measured by changes in the Multiple Sclerosis Functional Composite (MSFC), of young people in MS drug trials and to estimate the extent to which disability progression differ in two age groups (≤25 years and 26 - 35 years).

METHODS

Data from the Multiple Sclerosis Outcomes Assessment Consortium (MSOAC) was used. Longitudinal patterns on the MSFC were identified using group-based trajectory models (GBTM). For difference between the expected and observed proportions of people with pediatric-onset MS chi-square statistic was used. Linear mixed models were used to estimate the average change in performance over time, age and sex.

RESULTS

GBTM results showed little variability in performance over time. Mixed modeling showed that the younger group performed better for gait speed, dexterity, and cognition. Men performed poorer on dexterity and cognition. Distribution of people with pediatric-onset MS differed from expected on dexterity, cognition, and the EDSS.

CONCLUSIONS

The combined use of trajectory models and linear mixed models provided rich information about the variability in function over time.

How Risky Is That Risk Sharing Agreement? Mean-Variance Tradeoffs and Unintended Consequences of Six Common Risk Sharing Agreements

Background. Pharmaceutical risk sharing agreements (RSAs) are commonly used to manage uncertainties in costs and/or clinical benefits when new drugs are added to a formulary. However, existing mathematical models of RSAs ignore the impact of RSAs on clinical and financial risk. Methods. We develop a model in which the number of patients, total drug consumption per patient, and incremental health benefits per patient are uncertain at the time of the introduction of a new drug. We use the model to evaluate the impact of six common RSAs on total drug costs and total net monetary benefit (NMB). Results. We show that, relative to not having an RSA in place, each RSA reduces expected total drug costs and increases expected total NMB. Each RSA also improves two measures of risk by reducing the probability that total drug costs exceed any threshold and reducing the probability of obtaining negative NMB. However, the effects on variance in both NMB and total drug costs are mixed. In some cases, relative to not having an RSA in place, implementing an RSA can increase variability in total drug costs or total NMB. We also show that, for some RSAs, when their parameters are adjusted so that they have the same impact on expected total drug cost, they can be rank-ordered in terms of their impact on variance in drug costs. Conclusions. Although all RSAs reduce expected total drug costs and increase expected total NMB, some RSAs may actually have the undesirable effect of increasing risk. Payers and formulary managers should be aware of these mean-variance tradeoffs and the potentially unintended results of RSAs when designing and negotiating RSAs.

Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis

Background

Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of motor EP that uses the whole time series, instead of a few variables, to predict disability progression after two years. Obtaining realistic performance estimates of this task has been difficult because of small data set sizes. We recently extracted a dataset of EPs from the Rehabiliation & MS Center in Overpelt, Belgium. Our data set is large enough to obtain, for the first time, a performance estimate on an independent test set containing different patients.

Methods

We extracted a large number of time series features from the motor EPs with the highly comparative time series analysis software package. Mutual information with the target and the Boruta method are used to find features which contain information not included in the features studied in the literature. We use random forests (RF) and logistic regression (LR) classifiers to predict disability progression after two years. Statistical significance of the performance increase when adding extra features is checked.

Results

Including extra time series features in motor EPs leads to a statistically significant improvement compared to using only the known features, although the effect is limited in magnitude (ΔAUC = 0.02 for RF and ΔAUC = 0.05 for LR). RF with extra time series features obtains the best performance (AUC = 0.75±0.07 (mean and standard deviation)), which is good considering the limited number of biomarkers in the model. RF (a nonlinear classifier) outperforms LR (a linear classifier).

Conclusions

Using machine learning methods on EPs shows promising predictive performance. Using additional EP time series features beyond those already in use leads to a modest increase in performance. Larger datasets, preferably multi-center, are needed for further research. Given a large enough dataset, these models may be used to support clinicians in their decision making process regarding future treatment.

Clinical Outcomes of Escalation vs Early Intensive Disease-Modifying Therapy in Patients With Multiple Sclerosis

Importance

Uncertainty remains about how aggressively to treat early multiple sclerosis. High-efficacy disease-modifying therapies (DMTs) are often reserved for individuals expressing poor prognostic features at baseline.

Objective

To analyze long-term outcomes in a population-based cohort according to initial treatment strategy.

Design, Setting and Participants

In this cohort study, data were derived from January 1998 to December 2016, and analysis was performed in January 2017. From a total of 720 patients prescribed a DMT, 592 (82%) were included in analysis. Reasons for exclusion were first treated elsewhere or privately (n = 39), clinical trial participant (n = 25), and insufficient clinical data (n = 45).

Exposures

Patients were classified according to first-line treatment strategy: high-efficacy (early intensive treatment [EIT]) or moderate-efficacy DMT (escalation [ESC]).

Main Outcomes and Measures

Primary outcome was 5-year change in Expanded Disability Status Scale score. Secondary outcome was time to sustained accumulation of disability (SAD). Models were adjusted for sex, age at treatment, year of starting DMT, and escalation to high-efficacy treatment in the ESC group.

Results

Mean (SD) age of 592 patients at symptom onset was 27.0 (9.4) years. Mean (SD) 5-year change in Expanded Disability Status Scale score was lower in the EIT group than the ESC group (0.3 [1.5] vs 1.2 [1.5]); this remained significant after adjustment for relevant covariates (β = -0.85; 95% CI, -1.38 to -0.32; P = .002). Median (95% CI) time to SAD was 6.0 (3.17-9.16) years for EIT and 3.14 (2.77-4.00) years for ESC (P = .05). For those within the ESC group who escalated to high-efficacy DMT as second-line treatment, median (95% CI) time to SAD was 3.3 years (1.8-5.6; compared with EIT group log-rank test P = .08). After adjustment for relevant covariates, there was no difference in hazard of SAD between the groups. However, 60% of those who escalated to high-efficacy DMTs were observed to develop SAD while still receiving initial moderate-efficacy treatment before escalation.

Conclusions and Relevance

In a real-life setting, long-term outcomes were more favorable following early intensive therapy vs first-line moderate-efficacy DMT. Contemporary surveillance strategies and escalation protocols may be insufficiently responsive. This finding is particularly relevant as patients in real-world practice are typically selected for an EIT approach to therapy on the basis of clinical and radiological features predictive of a poor outcome. These data support the need for a prospective randomized clinical trial.

Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study

BACKGROUND AND PURPOSE

Brain MRI-derived lesions and atrophy are related to multiple sclerosis (MS) disability. In the Serially Unified Multicenter MS Investigation (SUMMIT), from Brigham and Women's Hospital (BWH) and University of California, San Francisco (UCSF), we assessed whether MRI methodologic heterogeneity may limit the ability to pool multisite data sets to assess 5-year clinical-MRI associations.

METHODS

Patients with relapsing-remitting (RR) MS (n = 100 from each site) underwent baseline brain MRI and baseline and 5-year clinical evaluations. Patients were matched on sex (74 women each), age, disease duration, and Expanded Disability Status Scale (EDSS) score. MRI was performed with differences between sites in both acquisition (field strength, voxel size, pulse sequences), and postprocessing pipeline to assess brain parenchymal fraction (BPF) and T2 lesion volume (T2LV).

RESULTS

The UCSF cohort showed higher correlation than the BWH cohort between T2LV and disease duration. UCSF showed a higher inverse correlation between BPF and age than BWH. UCSF showed a higher inverse correlation than BWH between BPF and 5-year EDSS score. Both cohorts showed inverse correlations between BPF and T2LV, with no between-site difference. The pooled but not individual cohort data showed a link between a lower baseline BPF and the subsequent 5-year worsening in disability in addition to other stronger relationships in the data.

CONCLUSIONS

MRI acquisition and processing differences may result in some degree of heterogeneity in assessing brain lesion and atrophy measures in patients with MS. Pooling of data across sites is beneficial to correct for potential biases in individual data sets.

Development and usability testing of a patient decision aid for newly diagnosed relapsing multiple sclerosis patients

BACKGROUND

Multiple sclerosis (MS) patients often struggle with treatment decisions, in part due to the increasing number of approved disease modifying therapies, each with different characteristics, and also since physicians can struggle to identify which of these characteristics matter most to each individual patient. Decision uncertainty can contribute to late treatment initiation and treatment non-adherence-causes of 'undertreatment' in MS. An interactive online patient decision aid that informs patients of their options, considers their individual preferences and goals, and facilitates conversations with their physicians, could improve how patients with relapsing forms of MS make evidence-based treatment decisions.

OBJECTIVE

To develop and evaluate a prototype patient decision aid (PtDA) for first-line disease modifying therapies for relapsing-remitting multiple sclerosis.

METHODS

Informed by previous studies and International Patient Decision Aid Standards guidelines, a prototype PtDA was developed for patients with relapsing multiple sclerosis considering first line treatment. Patients with relapsing multiple sclerosis were recruited from the University of British Columbia's Multiple Sclerosis Clinic to participate in either an online survey or a focus group. Online survey participants completed the PtDA, followed by measures of acceptability, usability, and preparedness for decision-making, and provided general feedback. Focus group participants assessed usability of the revised PtDA. The analysis of qualitative and quantitative data led to improvements of the PtDA prototype.

RESULTS

The prototype PtDA received high ratings for acceptability and usability, and after its use, participants reported high-levels of preparedness for decision-making. Analysis of all qualitative data identified three key themes: the need for credible information; the usefulness of the PtDA; and the importance of normalizing and sharing experiences. Nine content areas were identified for revision. Overall, participants found the PtDA to be a valuable tool for facilitating treatment decisions.

CONCLUSIONS

This mixed methods study has led to the development of a PtDA that can support patients with RRMS as they make treatment decisions. Future studies will assess the feasibility of implementation and the impact of the PtDA on both the timely treatment initiation and longer-term adherence.

Assessing the long-term effectiveness of interferon-beta and glatiramer acetate in multiple sclerosis: final 10-year results from the UK multiple sclerosis risk-sharing scheme

BACKGROUND

Because multiple sclerosis (MS) is a chronic disease causing disability over decades, it is crucial to know if the short-term effects of disease-modifying therapies reported in randomised controlled trials reduce long-term disability. This 10-year prospective observational study of disability outcomes (Expanded Disability Status Scale (EDSS) and utility) was set up, in conjunction with a risk-sharing agreement between payers and producers, to investigate this issue.

METHODS

The outcomes of the UK treated patients were compared with a modelled untreated control based on the British Columbia MS data set to assess the long-term effectiveness of these treatments. Two complementary analysis models were used: a multilevel model (MLM) and a continuous Markov model.

RESULTS

4862 patients with MS were eligible for the primary analysis (mean and median follow-up times 8.7 and 10 years). EDSS worsening was reduced by 28% (MLM), 7% (Markov) and 24% time-adjusted Markov in the total cohort, and by 31% (MLM) and 14% (Markov) for relapsing remitting patients. The utility worsening was reduced by 23%-24% in the total cohort and by 24%-31% in the RR patients depending on the model used. All sensitivity analyses showed a treatment effect. There was a 4-year (CI 2.7 to 5.3) delay to EDSS 6.0. An apparent waning of treatment effect with time was seen. Subgroup analyses suggested better treatment effects in those treated earlier and with lower EDSS scores.

CONCLUSIONS

This study supports a beneficial effect on long-term disability with first-line MS disease-modifying treatments, which is clinically meaningful. However the waning effect noted requires further study.

Short-term disability progression in two multiethnic multiple sclerosis centers in the treatment era

Background:

Short-term disease progression is well documented in clinical trials, but there are limited published data on disease course in real-life practice.

Methods:

Patient-derived Multiple Sclerosis Severity Score (PMSSS), a disease severity rank score, was computed at each visit for consecutive MS patients attending two large, ethnically diverse MS centers in New York metropolitan area. Disability was assessed via Patient-Determined Disease Steps (PDDS). Clinicians recorded disease subtype and relapse status at each visit, but did not rate disability. PMSSS change from the first to the last visit was calculated for the cohort as a whole and for subgroups of interest. Multivariable regression models were constructed for predicting final PMSSS based on readily available predictor variables collected at the initial visit and relapse history during follow up.

Results:

A total of 1740 consecutive patients from New York University (n = 1079) and Barnabas (n = 661) MS Care Centers were included. During follow up (mean 2.4 ± 0.82 years, range 1–4 years), mean PDDS score increased from 1.9 ± 2.2 to 2.3 ± 2.2 (p < 0.0001), while PMSSS remained roughly unchanged (initial PMSSS = 3.71 ± 2.73, last PMSSS = 3.81 ± 2.76, paired t test, p = 0.28). The only major predictor of final PMSSS was the initial PMSSS. Demographic variables (age, sex, race) or relapse status did not predict final severity score.

Conclusions:

Baseline disability in two MS clinics was much lower than in the reference population from which PMSSS was derived. We observed no discernable slowing of disability accumulation during the short-term follow up in our cohort compared with the reference cohort. Overwhelmingly the most important predictor of final disease severity rank score was the initial disease severity rank score.

Genome-wide association studies of multiple sclerosis

Large-scale genetic studies of multiple sclerosis have identified over 230 risk effects across the human genome, making it a prototypical common disease with complex genetic architecture. Here, after a brief historical background on the discovery and definition of the disease, we summarise the last fifteen years of genetic discoveries and map out the challenges that remain to translate these findings into an aetiological framework and actionable clinical understanding.