BREMSO: a simple score to predict early the natural course of multiple sclerosis

First-year treatment response predicts the following 5-year disease course in patients with relapsing-remitting multiple sclerosis

Predicting long-term prognosis and choosing the appropriate therapeutic approach in patients with Multiple Sclerosis (MS) at the time of diagnosis is crucial in view of a personalized medicine. We investigated the impact of early therapeutic response on the 5-year prognosis of patients with relapsing-remitting MS (RRMS). We recruited patients from MSBase Registry covering the period between 1996 and 2022. All patients were diagnosed with RRMS and actively followed-up for at least 5 years to explore the following outcomes: clinical relapses, confirmed disability worsening (CDW) and improvement (CDI), EDSS 3.0, EDSS 6.0, conversion to secondary progressive MS (SPMS), new MRI lesions, Progression Independent of Relapse Activity (PIRA). Predictors included demographic, clinical and radiological data, and sub-optimal response (SR) within the first year of treatment. Female sex (HR 1.27; 95 ​% CI 1.16-1.40) and EDSS at baseline (HR 1.19; 95 ​% CI 1.15-1.24) were independent risk factors for the occurrence of relapses during the first 5 years after diagnosis, while high-efficacy treatment (HR 0.78; 95 ​% CI 0.67-0.91) and age at diagnosis (HR 0.83; 95 ​% CI 0.79-0.86) significantly reduced the risk. SR predicted clinical relapses (HR ​= ​3.84; 95 ​% CI 3.51-4.19), CDW (HR ​= ​1.74; 95 ​% CI 1.56-1.93), EDSS 3.0 (HR ​= ​3.01; 95 ​% CI 2.58-3.51), EDSS 6.0 (HR ​= ​1.77; 95 ​% CI 1.43-2.20) and new brain (HR ​= ​2.33; 95 ​% CI 2.04-2.66) and spinal (HR 1.65; 95 ​% CI 1.29-2.09) MRI lesions. This study highlights the importance of selecting the appropriate DMT for each patient soon after MS diagnosis, also providing clinicians with a practical tool able to calculate personalized risk estimates for different outcomes.

Prognostic factors for disease activity in newly diagnosed teriflunomide-treated patients with multiple sclerosis: a nationwide Danish study

BACKGROUND

Clinicians frequently rely on relapse counts, T2 MRI lesion load (T2L) and Expanded Disability Status Scale (EDSS) scores to guide treatment decisions for individuals diagnosed with multiple sclerosis (MS). This study evaluates how these factors, along with age and sex, influence prognosis during treatment with teriflunomide (TFL).

METHODS

We conducted a nationwide cohort study using data from the Danish Multiple Sclerosis Registry.Eligible participants had relapsing-remitting MS or clinically isolated syndrome and initiated TFL as their first treatment between 2013 and 2019. The effect of age, pretreatment relapses, T2L and EDSS scores on the risk of disease activity on TFL were stratified by sex.

RESULTS

In total, 784 individuals were included (57.4% females). A high number of pretreatment relapses (≥2) was associated with an increased risk of disease activity in females only (OR and (95% CI): 1.76 (1.11 to 2.81)). Age group 50+ was associated with a lower risk of disease activity in both sexes (OR females=0.28 (0.14 to 0.56); OR males=0.22 (0.09 to 0.55)), while age 35-49 showed a different impact in males and females (OR females=0.79 (0.50 to 1.23); OR males=0.42 (0.24 to 0.72)). EDSS scores and T2L did not show any consistent associations.

CONCLUSION

A high number of pretreatment relapses was only associated with an increased risk of disease activity in females, while age had a differential impact on the risk of disease activity according to sex. Clinicians may consider age, sex and relapses when deciding on TFL treatment.

Prognostic models in multiple sclerosis: progress and challenges in clinical integration

As a chronic inflammatory disease of the central nervous system, multiple sclerosis (MS) is of great individual health and socio-economic significance. To date, there is no prognostic model that is used in routine clinical care to predict the very heterogeneous course of the disease. Despite several research groups working on different prognostic models using traditional statistics, machine learning and/or artificial intelligence approaches, the use of published models in clinical decision making is limited because of poor model performance, lack of transferability and/or lack of validated models. To provide a systematic overview, we conducted a "Cochrane review" that assessed 75 published prediction models using relevant checklists (CHARMS, PROBAST, TRIPOD). We have summarized the relevant points from this analysis here so that the use of prognostic models for therapy decisions in clinical routine can be successful in the future.

Early prediction of unfavorable evolution after a first clinical episode suggestive of multiple sclerosis: the EUMUS score

BACKGROUND

Predicting disease progression in patients with the first clinical episode suggestive of multiple sclerosis (MS) is crucial for personalized therapeutic approaches. This study aimed to develop the EUMUS score for accurately estimating the risk of early evidence of disease activity and progression (EDA).

METHODS

Retrospective analysis was conducted on data from 221 patients with a first clinical MS episode collected from four Italian MS centers. Various variables including socio-demographics, clinical features, cerebrospinal fluid analysis, evoked potentials, and brain MRI were considered. A prognostic multivariate regression model was identified to develop the EUMUS score. The optimal cutoff for predicting the transition from no evidence of disease activity (NEDA3) to EDA was determined. The accuracy of the prognostic model and score were tested in a separate UK MS cohort.

RESULTS

After 12 months, 61.54% of patients experienced relapses and/or new MRI lesions. Younger age (OR 0.96, CI 0.93-0.99; p = 0.005), MRI infratentorial lesion(s) at baseline (OR 2.21, CI 1.27-3.87; p = 0.005), positive oligoclonal bands (OR 2.89, CI 1.47-5.69; p = 0.002), and abnormal lower limb somatosensory-evoked potentials (OR 2.77, CI 1.41-5.42; p = 0.003) were significantly associated with increased risk of EDA. The EUMUS score demonstrated good specificity (72%) and correctly classified 80% of patients with EDA in the independent UK cohort.

CONCLUSIONS

The EUMUS score is a simple and useful tool for predicting MS evolution within 12 months of the first clinical episode. It has the potential to guide personalized therapeutic approaches and aid in clinical decision-making.

Predicting disease severity in multiple sclerosis using multimodal data and machine learning

BACKGROUND

Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.

METHODS

We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.

RESULTS

We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts.

CONCLUSION

Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

A mixture model for differentiating longitudinal courses of multiple sclerosis

BACKGROUND

Multiple sclerosis has a broad spectrum of clinical courses. Early identification of patients at greater risk of accumulating disability is essential.

OBJECTIVES

Identify groups of patients with similar presentation through a mixture model and predict their trajectories over the years.

METHODS

Retrospective study of patients from 1994 to 2019. We performed a latent profile analysis followed by a latent transition analysis based on eight parameters: age, disease duration, EDSS, number of relapses, multi-topographic symptoms, motor impairment, sphincter impairment, and infratentorial lesions.

RESULTS

We included 629 patients, regardless of the phenotypical classification. We identified three distinct groups at the beginning and end of the follow-up. The three-classes model disclosed the "No disability regardless disease duration" (NDRDD) class with low EDSS and younger patients, the "Disability within a short disease duration" (DSDD) class with the worse disability besides short illness, and the "Disability within a long disease duration" (DLDD) class that achieved high EDSS over a long disease duration. EDSS, disease duration, and no sphincter impairment had the best entropy to distinguish classes at the initial presentation. Over time, the patients from NDRDD had a 52.1 % probability of changing to DLDD and 7.7 % of changing to DSDD.

CONCLUSIONS

We identified three groups of clinical presentations and their evolution over time based on considered prognostic factors. The most likely transition is from NDRDD to DLDD.

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet.

OBJECTIVES

To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS.

SEARCH METHODS

We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies.

SELECTION CRITERIA

We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression.

MAIN RESULTS

We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal.

AUTHORS' CONCLUSIONS

The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

Serum Neurofilaments and OCT Metrics Predict EDSS-Plus Score Progression in Early Relapse-Remitting Multiple Sclerosis

(1) Background: Early disability accrual in RRMS patients is frequent and is associated with worse long-term prognosis. Correctly identifying the patients that present a high risk of early disability progression is of utmost importance, and may be aided by the use of predictive biomarkers. (2) Methods: We performed a prospective cohort study that included newly diagnosed RRMS patients, with a minimum follow-up period of one year. Biomarker samples were collected at baseline, 3-, 6- and 12-month follow-ups. Disability progression was measured using the EDSS-plus score. (3) Results: A logistic regression model based on baseline and 6-month follow-up sNfL z-scores, RNFL and GCL-IPL thickness and BREMSO score was statistically significant, with χ2(4) = 19.542, p < 0.0001, R2 = 0.791. The model correctly classified 89.1% of cases, with a sensitivity of 80%, a specificity of 93.5%, a positive predictive value of 85.7% and a negative predictive value of 90.62%. (4) Conclusions: Serum biomarkers (adjusted sNfL z-scores at baseline and 6 months) combined with OCT metrics (RNFL and GCL-IPL layer thickness) and the clinical score BREMSO can accurately predict early disability progression using the EDSS-plus score for newly diagnosed RRMS patients.

Improving the decision to switch from first- to second-line therapy in multiple sclerosis: A dynamic scoring system

BACKGROUND

In relapsing-remitting multiple sclerosis (RRMS), early identification of suboptimal responders can prevent disability progression.

OBJECTIVE

We aimed to develop and validate a dynamic score to guide the early decision to switch from first- to second-line therapy.

METHODS

Using time-dependent propensity scores (PS) from a French cohort of 12,823 patients with RRMS, we constructed one training and two validation PS-matched cohorts to compare the switched patients to second-line treatment and the maintained patients. We used a frailty Cox model for predicting individual hazard ratios (iHRs).

RESULTS

From the validation PS-matched cohort of 348 independent patients with iHR ⩽ 0.69, we reported the 5-year relapse-free survival at 0.14 (95% confidence interval (CI) 0.09-0.22) for the waiting group and 0.40 (95% CI 0.32-0.51) for the switched group. From the validation PS-matched cohort of 518 independent patients with iHR > 0.69, these values were 0.37 (95% CI 0.30-0.46) and 0.44 (95% CI 0.37-0.52), respectively.

CONCLUSIONS

By using the proposed dynamic score, we estimated that at least one-third of patients could benefit from an earlier switch to prevent relapse.

BACE1 influences clinical manifestations and central inflammation in relapsing remitting multiple sclerosis

Neurodegenerative and inflammatory processes influence the clinical course of multiple sclerosis (MS). The β-site amyloid precursor protein cleaving enzyme 1 (BACE1) has been associated with cognitive dysfunction, amyloid deposition and neuroinflammation in Alzheimer's disease. We explored in a group of 50 patients with relapsing-remitting MS the association between the cerebrospinal fluid (CSF) levels of BACE1, clinical characteristics at the time of diagnosis and prospective disability after three-years follow-up. In addition, we assessed the correlations between the CSF levels of BACE 1, amyloid β (Aβ) 1-40 and 1-42, phosphorylated tau (pTau), lactate, and a set of inflammatory and anti-inflammatory molecules. BACE1 CSF levels were correlated positively with depression as measured with Beck Depression Inventory-Second Edition scale, and negatively with visuospatial memory performance evaluated by the Brief Visuospatial Memory Test-Revised. In addition, BACE CSF levels were positively correlated with Bayesian Risk Estimate for MS at onset, and with Expanded Disability Status Scale score assessed three years after diagnosis. Furthermore, a positive correlation was found between BACE1, amyloid β 42/40 ratio (Spearman's r = 0.334, p = 0.018, n = 50), pTau (Spearman's r = 0.304, p = 0.032, n = 50) and lactate concentrations (Spearman's r = 0.361, p = 0.01, n = 50). Finally, an association emerged between BACE1 CSF levels and a group of pro and anti-inflammatory molecules, including interleukin (IL)-4, IL-17, IL-13, IL-9 and interferon-γ. BACE1 may have a role in different key mechanisms such as neurodegeneration, oxidative stress and inflammation, influencing mood, cognitive disorders and disability progression in MS.

Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms

While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1305 proteins, measured blindly in the training dataset of untreated MS patients (N = 129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N = 24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p < 0.0001) in an independent longitudinal cohort (N = 98), uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making, it hopefully more efficient and successful.

Serum and CSF Biomarkers Predict Active Early Cognitive Decline Rather Than Established Cognitive Impairment at the Moment of RRMS Diagnosis

(1) Background: Cognitive impairment (CI) begins early in the evolution of multiple sclerosis (MS) but may only become obvious in the later stages of the disease. Little data is available regarding predictive biomarkers for early, active cognitive decline in relapse remitting MS (RRMS) patients. (2) Methods: 50 RRMS patients in the first 6 months following diagnosis were included. The minimum follow-up was one year. Biomarker samples were collected at baseline, 3-, 6- and 12-month follow-up. Cognitive performance was assessed at baseline and 12-month follow-up; (3) Results: Statistically significant differences were found for patients undergoing active cognitive decline for sNfL z-scores at baseline and 3 months, CSF NfL baseline values, CSF Aβ42 and the Bremso score as well. The logistic regression model based on these 5 variables was statistically significant, χ2(4) = 22.335, p < 0.0001, R2 = 0.671, with a sensitivity of 57.1%, specificity of 97.4%, a positive predictive value of 80% and a negative predictive value of 92.6%. (4) Conclusions: Our study shows that serum biomarkers (adjusted sNfL z-scores at baseline and 3 months) and CSF biomarkers (CSF NfL baseline values, CSF Aβ42), combined with a clinical score (BREMSO), can accurately predict an early cognitive decline for RRMS patients at the moment of diagnosis.

Neuroinflammation Is Associated with GFAP and sTREM2 Levels in Multiple Sclerosis

Background: Astrocytes and microglia play an important role in the inflammatory process of multiple sclerosis (MS). We investigated the associations between the cerebrospinal fluid (CSF) levels of glial fibrillary acid protein (GFAP) and soluble triggering receptors expressed on myeloid cells-2 (sTREM-2), inflammatory molecules, and clinical characteristics in a group of patients with relapsing-remitting MS (RRMS). Methods: Fifty-one RRMS patients participated in the study. Clinical evaluation and CSF collection were performed at the time of diagnosis. The CSF levels of GFAP, sTREM-2, and of a large set of inflammatory and anti-inflammatory molecules were determined. MRI structural measures (cortical thickness, T2 lesion load, cerebellar volume) were examined. Results: The CSF levels of GFAP and sTREM-2 showed significant correlations with inflammatory cytokines IL-8, G-CSF, and IL-5. Both GFAP and sTREM-2 CSF levels positively correlated with age at diagnosis. GFAP was also higher in male MS patients, and was associated with an increased risk of MS progression, as evidenced by higher BREMS at the onset. Finally, a negative association was found between GFAP CSF levels and cerebellar volume in RRMS at diagnosis. Conclusions: GFAP and sTREM-2 represent suitable biomarkers of central inflammation in MS. Our results suggest that enhanced CSF expression of GFAP may characterize patients with a higher risk of progression.

Treatment response scoring systems to assess long-term prognosis in self-injectable DMTs relapsing-remitting multiple sclerosis patients

BACKGROUND AND OBJECTIVES

Different treatment response scoring systems in treated MS patients exist. The objective was to assess the long-term predictive value of these systems in RRMS patients treated with self-injectable DMTs.

METHODS

RRMS-treated patients underwent brain MRI before the onset of therapy and 12 months thereafter, and neurological assessments every 6 months. Clinical and demographic characteristics were collected at baseline. After the first year of treatment, several scoring systems [Rio score (RS), modified Rio score (MRS), MAGNIMS score (MS), and ROAD score (RoS)] were calculated. Cox-Regression and survival analyses were performed to identify scores predicting long-term disability.

RESULTS

We included 319 RRMS patients. Survival analyses showed that patients with RS > 1 and RoS > 3 had a significant risk of reaching an EDSS of 4.0 and 6.0 The score with the best sensitivity (61%) was the RoS, while the MRS showed the best specificity (88%). The RS showed the best positive predictive value (42%) and the best accuracy (81%).

CONCLUSIONS

The combined measures integrated into different scores have an acceptable prognostic value for identifying patients with long-term disability. Thus, these data reinforce the concept of early treatment optimization to minimize the risk of long-term disability.

Development of Registry Data to Create Interactive Doctor-Patient Platforms for Personalized Patient Care, Taking the Example of the DESTINY System

“Real-world evidence (RWE)” is becoming increasingly important in order to integrate the results of randomized studies into everyday clinical practice. The data collection of RWE is usually derived from large-scale national and international registries, often driven by academic centers. We have developed a digitalized doctor–patient platform called DESTINY (DatabasE-assiSted Therapy decIsioN support sYstem) that is utilized by NeuroTransData (NTD), a network of neurologists and psychiatrists throughout Germany. This platform can be integrated into everyday practice and, as well as being used for scientific evaluations in healthcare research, can also serve as an individual, personalized treatment application. Its various modules allow for a timely identification of side-effects or interactions of treatments, can involve patients via the “My NTC Health Guide” portal, and can collect data of individual disease histories that are integrated into innovative algorithms, e.g., for the prediction of treatment response [currently available for multiple sclerosis (MS), with other indications in the pipeline]. Here, we describe the doctor–patient platform DESTINY for outpatient neurological practices and its contribution to improved treatment success as well as reduction of healthcare costs. Platforms like DESTINY may facilitate the goal of personalized healthcare.

Age at Disease Onset Associates With Oxidative Stress, Neuroinflammation, and Impaired Synaptic Plasticity in Relapsing-Remitting Multiple Sclerosis

Age at onset is the main risk factor for disease progression in patients with relapsing-remitting multiple sclerosis (RR-MS). In this cross-sectional study, we explored whether older age is associated with specific disease features involved in the progression independent of relapse activity (PIRA). In 266 patients with RR-MS, the associations between age at onset, clinical characteristics, cerebrospinal fluid (CSF) levels of lactate, and that of several inflammatory molecules were analyzed. The long-term potentiation (LTP)-like plasticity was studied using transcranial magnetic stimulation (TMS). Older age was associated with a reduced prevalence of both clinical and radiological focal inflammatory disease activity. Older patients showed also increased CSF levels of lactate and that of the pro-inflammatory molecules monocyte chemoattractant protein 1 (MCP-1)/CCL2, macrophage inflammatory protein 1-alpha (MIP-1α)/CCL3, and interleukin (IL)-8. Finally, TMS evidenced a negative correlation between age and LTP-like plasticity. In newly diagnosed RR-MS, older age at onset is associated with reduced acute disease activity, increased oxidative stress, enhanced central inflammation, and altered synaptic plasticity. Independently of their age, patients with multiple sclerosis (MS) showing similar clinical, immunological, and neurophysiological characteristics may represent ideal candidates for early treatments effective against PIRA.

Comparable Efficacy and Safety of Teriflunomide versus Dimethyl Fumarate for the Treatment of Relapsing-Remitting Multiple Sclerosis

Background

The aim of this observational study is to investigate the efficacy and safety of two approved oral disease-modifying therapies (DMTs) in patients with remitting-relapsing multiple sclerosis (RRMS): dimethyl fumarate (DMF) vs. teriflunomide (TRF).

Methods

A total of 159 RRMS patients (82 on TRF and 77 on DMF) were included. The expanded disability status scale (EDSS), confirmed disability improvement (CDI), confirmed disability progression (CDP), and annualized relapse rate (ARR) were evaluated for the two-year period prior to enrollment in our study. The drug-associated adverse effects (AEs) were recorded. We conducted propensity matching score to compare the efficacy between TRF and DMF.

Results

After matching for the confounders, TRF- and DMF-treated groups were not different in terms of EDSS (P value = 0.54), CDI (P value = 0.80), CDP (P value = 0.39), and ARR (P value >0.05). TRF discontinuation occurred in 2 patients (2.43%) due to mediastinitis and liver dysfunction, while a patient (1.29%) discontinued DMF due to depression. Incidence rate of AEs in the TRF-treated group was 81.4%: hair thinning (hair loss) (62.9%), nail loss (20.9%), and elevated aminotransferase (14.8%) were the most common AEs; in DMF-treated patients, AEs were 88.2% with predominance of flushing (73.2%), pruritus (16.9%), and abdominal pain (16.9%).

Conclusion

Based on our findings, DMF is as efficacious and safe as TRF for the treatment of RRMS in our Iranian study population. Multicentric studies need to corroborate these findings in other populations.

Scoring the 10-year risk of ambulatory disability in multiple sclerosis: the RoAD score

BACKGROUND AND PURPOSE

Both baseline prognostic factors and short-term predictors of treatment response can influence the long-term risk of disability accumulation in patients with relapsing-remitting multiple sclerosis (RRMS). The objective was to develop and validate a scoring system combining baseline prognostic factors and 1-year variables of treatment response into a single numeric score predicting the long-term risk of disability.

METHODS

We analysed two independent datasets of patients with RRMS who started interferon beta or glatiramer acetate, had an Expanded Disability Status Scale (EDSS) score <4.0 at treatment start and were followed for at least 10 years. The first dataset ('training set') included patients attending three MS centres in Italy and served as a framework to create the so-called RoAD score (Risk of Ambulatory Disability). The second ('validation set') included a cohort of patients followed in Barcelona, Spain, to explore the performance of the RoAD score in predicting the risk of reaching an EDSS score ≥6.0.

RESULTS

The RoAD score (ranging from 0 to 8) derived from the training set (n = 1225), was based on demographic (age), clinical baseline prognostic factors (disease duration, EDSS) and 1-year predictors of treatment response (number of relapses, presence of gadolinium enhancement and new T2 lesions). The best cut-off score for discriminating patients at higher risk of reaching the disability milestone was ≥4. When applied to the validation set (n = 296), patients with a RoAD score ≥4 had an approximately 4-fold increased risk for reaching the disability milestone (p < 0.001).

DISCUSSION

The RoAD score is proposed as an useful tool to predict individual prognosis and optimize treatment strategy of patients with RRMS.

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

Systematic review of prediction models in relapsing remitting multiple sclerosis

The natural history of relapsing remitting multiple sclerosis (RRMS) is variable and prediction of individual prognosis challenging. The inability to reliably predict prognosis at diagnosis has important implications for informed decision making especially in relation to disease modifying therapies. We conducted a systematic review in order to collate, describe and assess the methodological quality of published prediction models in RRMS. We searched Medline, Embase and Web of Science. Two reviewers independently screened abstracts and full text for eligibility and assessed risk of bias. Studies reporting development or validation of prediction models for RRMS in adults were included. Data collection was guided by the checklist for critical appraisal and data extraction for systematic reviews (CHARMS) and applicability and methodological quality assessment by the prediction model risk of bias assessment tool (PROBAST). 30 studies were included in the review. Applicability was assessed as high risk of concern in 27 studies. Risk of bias was assessed as high for all studies. The single most frequently included predictor was baseline EDSS (n = 11). T2 Lesion volume or number and brain atrophy were each retained in seven studies. Five studies included external validation and none included impact analysis. Although a number of prediction models for RRMS have been reported, most are at high risk of bias and lack external validation and impact analysis, restricting their application to routine clinical practice.

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.

A method to compare prospective and historical cohorts to evaluate drug effects. Application to the analysis of early treatment effectiveness of intramuscular interferon-β1a in multiple sclerosis patients

BACKGROUND

Disease modifying therapy have changed the natural evolution of multiple sclerosis (MS), with efficacy demonstrated in randomized clinical trials. Standard-of-care effectiveness is needed to complement clinical trial data and highlight outcomes in real-world practice, but comparing prospective patients with historical cohorts likely introduces biases. To address these potential biases, assigning a patient with a score that expresses his/her disease prognosis before starting a therapy may make it possible to evaluate the unbiased ability of the therapy to modify disease natural history. Thus, we aimed at analyzing the effectiveness of intramuscular interferon-β1a (im IFN-β1a) matching by BREMSO score (Bayesian Risk Estimate for Multiple Sclerosis at Onset) a prospective real-world cohort of treated patients with a historical cohort of untreated patients.

MATERIAL AND METHODS

We observed 108 newly diagnosed, treatment naïve MS patients over 12 months of treatment with im IFN-β1a. BREMSO score was used to assign a value to each patient, giving the real-world treated patients comparable with the Historical untreated patients, on the basis of the same risk to have unfavorable evolution.

RESULTS

A significantly higher percentage of relapse-free patients is observed in IFN-β1a treated cohort vs. Historical untreated cohort (79.6% vs. 59.3%, p < 0.01). Clinical relapses risk is reduced by 2.2 times in treated patients (p = 0.01).

CONCLUSIONS

We propose a promising method to manage observational data in a relatively unbiased way, in order to analyze real-world treatment effectiveness.

Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis

Personalized treatment is ideal for multiple sclerosis (MS) owing to the heterogeneity of clinical features, but current knowledge gaps, including validation of biomarkers and treatment algorithms, limit practical implementation. The contemporary approach to personalized MS therapy depends on evidence-based prognostication, an initial treatment choice and evaluation of early treatment responses to identify the need to switch therapy. Prognostication is directed by baseline clinical, environmental and demographic factors, MRI measures and biomarkers that correlate with long-term disability measures. The initial treatment choice should be a shared decision between the patient and physician. In addition to prognosis, this choice must account for patient-related factors, including comorbidities, pregnancy planning, preferences of the patients and their comfort with risk, and drug-related factors, including safety, cost and implications for treatment sequencing. Treatment response has traditionally been assessed on the basis of relapse rate, MRI lesions and disability progression. Larger longitudinal data sets have enabled development of composite outcome measures and more stringent standards for disease control. Biomarkers, including neurofilament light chain, have potential as early surrogate markers of prognosis and treatment response but require further validation. Overall, attainment of personalized treatment for MS is complex but will be refined as new data become available.

Amyloid PET as a marker of normal-appearing white matter early damage in multiple sclerosis: correlation with CSF β-amyloid levels and brain volumes

PURPOSE

The disease course of multiple sclerosis (MS) is unpredictable, and reliable prognostic biomarkers are needed. Positron emission tomography (PET) with β-amyloid tracers is a promising tool for evaluating white matter (WM) damage and repair. Our aim was to investigate amyloid uptake in damaged (DWM) and normal-appearing WM (NAWM) of MS patients, and to evaluate possible correlations between cerebrospinal fluid (CSF) β-amyloid_1-42 (Aβ) levels, amyloid tracer uptake, and brain volumes.

METHODS

Twelve MS patients were recruited and divided according to their disease activity into active and non-active groups. All participants underwent neurological examination, neuropsychological testing, lumbar puncture, brain magnetic resonance (MRI) imaging, and ¹⁸F-florbetapir PET. Aβ levels were determined in CSF samples from all patients. MRI and PET images were co-registered, and mean standardized uptake values (SUV) were calculated for each patient in the NAWM and in the DWM. To calculate brain volumes, brain segmentation was performed using statistical parametric mapping software. Nonparametric statistical analyses for between-group comparisons and regression analyses were conducted.

RESULTS

We found a lower SUV in DWM compared to NAWM (p < 0.001) in all patients. Decreased NAWM-SUV was observed in the active compared to non-active group (p < 0.05). Considering only active patients, NAWM volume correlated with NAWM-SUV (p = 0.01). Interestingly, CSF Aβ concentration was a predictor of both NAWM-SUV (r = 0.79; p = 0.01) and NAWM volume (r = 0.81, p = 0.01).

CONCLUSIONS

The correlation between CSF Aβ levels and NAWM-SUV suggests that the predictive role of β-amyloid may be linked to early myelin damage and may reflect disease activity and clinical progression.

The Bayesian risk estimate at onset (BREMSO) correlates with cognitive and physical disability in patients with early multiple sclerosis

BACKGROUND

Prevention of long-term disability is the goal of therapeutic intervention in Relapsing Remitting MS (RRMS). The Bayesian Risk Estimate for MS at Onset (BREMSO) gives an individual risk score predicting disease evolution into Secondary Progressive MS (SPMS). We investigated whether BREMSO correlates with physical disability, cognitive dysfunction, and regional brain atrophy early in MS.

METHODS

One hundred RRMS patients with at least two years of follow-up were enrolled. BREMSO score as well as Symbol Digit Modalities Test (SDMT) and Multiple Sclerosis Severity Score (MSSS), Timed 25-Foot Walk Test (T25-FW) and 9-Hole Peg Test (9-HPT), were assessed. Intracranial volume (ICV), subcortical gray matter structures and corpus callosum (CC) were automatically segmented on MRI images and their volumes measured.

RESULTS

BREMSO score correlated negatively with SDMT at visit1 (β = -0.33, p = 0.019), visit2 (β = -0.34, p = 0.017) and visit3 (β = -0.34, p = 0.014), and positively with MSSS at visit1 (r = 0.38, p = 0.006), visit2 (r = 0.47, p < 0.0001) and visit3 (r = 0.42, p = 0.002), but not with T25-FW and 9-HPT. BREMSO negatively correlated with CC volume at baseline (p < 0.03). No correlations were found with ICV and subcortical gray matter.

CONCLUSIONS

BREMSO score at onset correlated with physical disability (MSSS), cognitive function (SDMT) and CC volume measurements in patients with early MS.

CSF β-amyloid predicts prognosis in patients with multiple sclerosis

BACKGROUND

The importance of predicting disease progression in multiple sclerosis (MS) has increasingly been recognized, and hence reliable biomarkers are needed.

OBJECTIVES

To investigate the prognostic role of cerebrospinal fluid (CSF) amyloid beta_1-42 (Aβ) levels by the determination of a cut-off value to classify patients in slow and fast progressors. To evaluate possible association with white matter (WM) and grey matter (GM) damage at early disease stages.

METHODS

Sixty patients were recruited and followed up for 3-5 years. Patients underwent clinical assessment, brain magnetic resonance imaging (MRI; at baseline and after 1 year), and CSF analysis to determine Aβ levels. T1-weighted volumes were calculated. T2-weighted scans were used to quantify WM lesion loads.

RESULTS

Lower CSF Aβ levels were observed in patients with a worse follow-up Expanded Disability Status Scale (EDSS; r = -0.65, p < 0.001). The multiple regression analysis confirmed CSF Aβ concentration as a predictor of patients' EDSS increase (r = -0.59, p < 0.0001). Generating a receiver operating characteristic curve, a cut-off value of 813 pg/mL was determined as the threshold able to identify patients with worse prognosis (95% confidence interval (CI): 0.690-0.933, p = 0.0001). No differences in CSF tau and neurofilament light chain (NfL) levels were observed (p > 0.05).

CONCLUSION

Low CSF Aβ levels may represent a predictive biomarker of disease progression in MS.

Variants of MicroRNA Genes: Gender-Specific Associations with Multiple Sclerosis Risk and Severity

Multiple sclerosis (MS) is an autoimmune neuro-inflammatory disease arising from complex interactions of genetic, epigenetic, and environmental factors. Variations in genes of some microRNAs--key post-transcriptional regulators of many genes--can influence microRNAs expression/function and contribute to MS via expression changes of protein-coding target mRNA genes. We performed an association study of polymorphous variants of MIR146A rs2910164, MIR196A2 rs11614913, MIR499A rs3746444 MIR223 rs1044165 and their combinations with MS risk and severity. 561 unrelated patients with bout-onset MS and 441 healthy volunteers were enrolled in the study. We observed associations of MS risk with allele MIR223*T and combination (MIR223*T + MIR146A*G/G) carriage in the entire groups and in women at Bonferroni-corrected significance level (pcorr < 0.05). Besides, MIR146A*G/G association with MS was observed in women with nominal significance (pf = 0.025). No MS associations were found in men. A more severe MS course (MSSS value > 3.5) was associated with the carriage of MIR499A*C/T and, less reliably, of MIR499A*C (pcorr = 0.006 and pcorr = 0.024, respectively) and with the carriage of combinations (MIR499A*C/T + MIR196A2*C) and (MIR499A*C + MIR196A2*C) (pcorr = 0.00078 and pcorr = 0.0059, respectively). These associations also showed gender specificity, as they were not significant in men and substantially reinforced in women. The strongest association with MS severity was observed in women for combination (MIR499A*C/T + MIR196A2*C): pcorr = 4.43 × 10(-6) and OR = 3.23 (CI: 1.99-5.26).