Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment

Risk factors and long-term outcomes of oropharyngeal dysphagia in persons with multiple sclerosis: a systematic review protocol

BACKGROUND

Oropharyngeal dysphagia (OPD) can be functionally debilitating in persons with multiple sclerosis (pwMS). OPD induces alterations in safety and efficiency of food and/or liquid ingestion and may incur negative sequalae such as aspiration pneumonia or malnutrition/dehydration. Early detection and timely management of OPD in pwMS could prevent such complications and reduce mortality rates. Identifying risk factors of OPD relative to its onset or repeat manifestation will enable the development of care pathways that target early assessment and sustained management. The aims of this systematic review are to compile, evaluate, and summarize the existing literature reporting potential risk factors and associated long-term outcomes (e.g., aspiration pneumonia, malnutrition, dehydration, and/or death) of OPD in pwMS.

METHODS

We will undertake a systematic review to identify studies that describe patterns and complications of OPD in pwMS. Variables of interest include predictors of OPD along with long-term outcomes. We will search MEDLINE, Embase, CINAHL, AMED, the Cochrane Library, Web of Science, and Scopus. We will consider studies for inclusion if they involve at least 30 adult participants with MS and report risk factors for OPD and/or its long-term outcomes. Studies will be excluded if they refer to esophageal or oropharyngeal dysphagia induced by causes other than multiple sclerosis. Study selection and data extraction will be performed by two independent assessors for abstract and full article review. We will present study characteristics in tables and document research findings for dysphagia-related risk factors or its complications via a narrative format or meta-analysis if warranted (e.g., mean difference and/or risk ratio measurements). All included studies will undergo risk-of-bias assessment conducted independently by two authors with consensus on quality ratings.

DISCUSSION

There is a lacune for systematic reviews involving risk factors and long-term outcomes of dysphagia in pwMS to date. Our systematic review will provide the means to develop accurate and efficient management protocols for careful monitoring and evaluation of dysphagia in pwMS. The results of this systematic review will be published in a peer-reviewed journal.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022340625.

Comparative Effectiveness of Natalizumab, Fingolimod, and Injectable Therapies in Pediatric-Onset Multiple Sclerosis: A Registry-Based Study

BACKGROUND AND OBJECTIVES

Patients with pediatric-onset multiple sclerosis (POMS) typically experience higher levels of inflammation with more frequent relapses, and though patients with POMS usually recover from relapses better than adults, patients with POMS reach irreversible disability at a younger age than adult-onset patients. There have been few randomized, placebo-controlled clinical trials of multiple sclerosis (MS) disease-modifying therapies (DMTs) in patients with POMS, and most available data are based on observational studies of off-label use of DMTs approved for adults. We assessed the effectiveness of natalizumab compared with fingolimod using injectable platform therapies as a reference in pediatric patients in the global MSBase registry.

METHODS

This retrospective study included patients with POMS who initiated treatment with an injectable DMT, natalizumab, or fingolimod between January 1, 2006, and May 3, 2021. Patients were matched using inverse probability treatment weighting. The primary outcome was time to first relapse from index therapy initiation. Secondary study outcomes included annualized relapse rate; proportions of relapse-free patients at 1, 2, and 5 years; time to treatment discontinuation; and times to 24-week confirmed disability worsening and confirmed disability improvement.

RESULTS

A total of 1,218 patients with POMS were included in this analysis. Patients treated with fingolimod had a significantly lower risk of relapse than patients treated with injectable DMTs (hazard ratio [HR], 0.49; 95% confidence interval [CI], 0.29-0.83; p = 0.008). After adjustment for prior DMT experience in the unmatched sample, patients treated with natalizumab had a significantly lower risk of relapse than patients treated either with injectable DMTs (HR, 0.15; 95% CI 0.07-0.31; p < 0.001) or fingolimod (HR, 0.37; 95% CI 0.14-1.00; p = 0.049). The adjusted secondary study outcomes were generally consistent with the primary outcome or with previous observations. The findings in the inverse probability treatment weighting-adjusted patient populations were confirmed in multiple sensitivity analyses.

DISCUSSION

Our analyses of relapse risk suggest that natalizumab is more effective than fingolimod in the control of relapses in this population with high rates of new inflammatory activity, consistent with previous studies of natalizumab and fingolimod in adult-onset patients and POMS. In addition, both fingolimod and natalizumab were more effective than first-line injectable therapies.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that patients with POMS treated with natalizumab had a lower risk of relapse than those with fingolimod.

Introducing a core dataset for real-world data in multiple sclerosis registries and cohorts: Recommendations from a global task force

BACKGROUND

As of September 2022, there was no globally recommended set of core data elements for use in multiple sclerosis (MS) healthcare and research. As a result, data harmonisation across observational data sources and scientific collaboration is limited.

OBJECTIVES

To define and agree upon a core dataset for real-world data (RWD) in MS from observational registries and cohorts.

METHODS

A three-phase process approach was conducted combining a landscaping exercise with dedicated discussions within a global multi-stakeholder task force consisting of 20 experts in the field of MS and its RWD to define the Core Dataset.

RESULTS

A core dataset for MS consisting of 44 variables in eight categories was translated into a data dictionary that has been published and disseminated for emerging and existing registries and cohorts to use. Categories include variables on demographics and comorbidities (patient-specific data), disease history, disease status, relapses, magnetic resonance imaging (MRI) and treatment data (disease-specific data).

CONCLUSION

The MS Data Alliance Core Dataset guides emerging registries in their dataset definitions and speeds up and supports harmonisation across registries and initiatives. The straight-forward, time-efficient process using a dedicated global multi-stakeholder task force has proven to be effective to define a concise core dataset.

Harmonizing government responses to the COVID-19 pandemic

Public health and safety measures (PHSM) made in response to the COVID-19 pandemic have been singular, rapid, and profuse compared to the content, speed, and volume of normal policy-making. Not only can they have a profound effect on the spread of the disease, but they may also have multitudinous secondary effects, in both the social and natural worlds. Unfortunately, despite the best efforts by numerous research groups, existing data on COVID-19 PHSM only partially captures their full geographical scale and policy scope for any significant duration of time. This paper introduces our effort to harmonize data from the eight largest such efforts for policies made before September 21, 2021 into the taxonomy developed by the CoronaNet Research Project in order to respond to the need for comprehensive, high quality COVID-19 data. In doing so, we present a comprehensive comparative analysis of existing data from different COVID-19 PHSM datasets, introduce our novel methodology for harmonizing COVID-19 PHSM data, and provide a clear-eyed assessment of the pros and cons of our efforts.

Claims-based algorithm to estimate the Expanded Disability Status Scale for multiple sclerosis in a German health insurance fund: a validation study using patient medical records

Background

The Expanded Disability Status Scale (EDSS) quantifies disability and measures disease progression in multiple sclerosis (MS), however is not available in administrative claims databases.

Objectives

To develop a claims-based algorithm for deriving EDSS and validate it against a clinical dataset capturing true EDSS values from medical records.

Methods

We built a unique linked dataset combining claims data from the German AOK PLUS sickness fund and medical records from the Multiple Sclerosis Management System 3D (MSDS3D). Data were deterministically linked based on insurance numbers. We used 69 MS-related diagnostic indicators recorded with ICD-10-GM codes within 3 months before and after recorded true EDSS measures to estimate a claims-based EDSS proxy (pEDSS). Predictive performance of the pEDSS was assessed as an eight-fold (EDSS 1.0-7.0, ≥8.0), three-fold (EDSS 1.0-3.0, 4.0-5.0, ≥6.0), and binary classifier (EDSS <6.0, ≥6.0). For each classifier, predictive performance measures were determined, and overall performance was summarized using a macro F1-score. Finally, we implemented the algorithm to determine pEDSS among an overall cohort of patients with MS in AOK PLUS, who were alive and insured 12 months prior to and after index diagnosis.

Results

We recruited 100 people with MS insured by AOK PLUS who had ≥1 EDSS measure in MSDS3D between 01/10/2015 and 30/06/2019 (620 measurements overall). Patients had a mean rescaled EDSS of 3.2 and pEDSS of 3.0. The pEDSS deviated from the true EDSS by 1.2 points, resulting in a mean squared error of prediction of 2.6. For the eight-fold classifier, the macro F1-score of 0.25 indicated low overall predictive performance. Broader severity groupings were better performing, with the three-fold and binary classifiers for severe disability achieving a F1-score of 0.68 and 0.84, respectively. In the overall AOK PLUS cohort (3,756 patients, 71.9% female, mean 51.9 years), older patients, patients with progressive forms of MS and those with higher comorbidity burden showed higher pEDSS.

Conclusion

Generally, EDSS was underestimated by the algorithm as mild-to-moderate symptoms were poorly captured in claims across all functional systems. While the proxy-based approach using claims data may not allow for granular description of MS disability, broader severity groupings show good predictive performance.

Impact of adherence to disease-modifying drugs in multiple sclerosis: A study on Italian real-world data

BACKGROUND

Multiple Sclerosis (MS) is a chronic inflammatory disease of the central nervous system requiring complex diagnostic and therapeutic management. Treatment with Disease Modifying Drugs (DMDs) is aimed at reducing relapse rate and disease disability. Few real-world, population-based data are available on the impact of adherence on relapse rate. The objective of this study was to assess the impact of adherence to DMDs on relapses in a real-world Italian setting.

METHODS

Population-based cohort study. People with MS (PwMS) older than 18 years and residing in the Emilia-Romagna region, Northern Italy, were identified through administrative databases using a validated algorithm. A Cox regression model with a time-varying exposure was performed to assess the association between level of adherence to DMDs and relapses over a 5-year period.

RESULTS

A total of 2,528 PwMS receiving a first prescription of DMDs between 2015 and 2019 were included (average age of 42, two-thirds female). Highly adherent PwMS had a 25 % lower hazard of experiencing moderate or severe relapses than non-adherent PwMS (Hazard Ratio 0.75, 95 % CI 0.58 to 0.98), after adjusting for age and sex. Several sensitivity analyses supported the main result.

CONCLUSION

The results of our study support the hypothesis that a high level of DMD adherence in MS is associated with a lower risk of moderate or severe relapse. Therefore, choosing the DMD with which to start drug treatment and recommending adherence to treatment appear to be crucial aspects involving both physicians and patients.

Data monitoring roadmap. The experience of the Italian Multiple Sclerosis and Related Disorders Register

INTRODUCTION

Over the years, disease registers have been increasingly considered a source of reliable and valuable population studies. However, the validity and reliability of data from registers may be limited by missing data, selection bias or data quality not adequately evaluated or checked. This study reports the analysis of the consistency and completeness of the data in the Italian Multiple Sclerosis and Related Disorders Register.

METHODS

The Register collects, through a standardized Web-based Application, unique patients. Data are exported bimonthly and evaluated to assess the updating and completeness, and to check the quality and consistency. Eight clinical indicators are evaluated.

RESULTS

The Register counts 77,628 patients registered by 126 centres. The number of centres has increased over time, as their capacity to collect patients. The percentages of updated patients (with at least one visit in the last 24 months) have increased from 33% (enrolment period 2000-2015) to 60% (enrolment period 2016-2022). In the cohort of patients registered after 2016, there were ≥ 75% updated patients in 30% of the small centres (33), in 9% of the medium centres (11), and in all the large centres (2). Clinical indicators show significant improvement for the active patients, expanded disability status scale every 6 months or once every 12 months, visits every 6 months, first visit within 1 year and MRI every 12 months.

CONCLUSIONS

Data from disease registers provide guidance for evidence-based health policies and research, so methods and strategies ensuring their quality and reliability are crucial and have several potential applications.

Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol

(1) Objective: We explore the predictive power of a novel stream of patient data, combining wearable devices and patient reported outcomes (PROs), using an AI-first approach to classify the health status of Parkinson's disease (PD), multiple sclerosis (MS) and stroke patients (collectively named PMSS). (2) Background: Recent studies acknowledge the burden of neurological disorders on patients and on the healthcare systems managing them. To address this, effort is invested in the digital transformation of health provisioning for PMSS patients. (3) Methods: We introduce the data collection journey within the ALAMEDA project, which continuously collects PRO data for a year through mobile applications and supplements them with data from minimally intrusive wearable devices (accelerometer bracelet, IMU sensor belt, ground force measuring insoles, and sleep mattress) worn for 1-2 weeks at each milestone. We present the data collection schedule and its feasibility, the mapping of medical predictor variables to wearable device capabilities and mobile application functionality. (4) Results: A novel combination of wearable devices and smartphone applications required for the desired analysis of motor, sleep, emotional and quality-of-life outcomes is introduced. AI-first analysis methods are presented that aim to uncover the prediction capability of diverse longitudinal and cross-sectional setups (in terms of standard medical test targets). Mobile application development and usage schedule facilitates the retention of patient engagement and compliance with the study protocol.

Real-world evidence of ocrelizumab-treated relapsing multiple sclerosis cohort shows changes in progression independent of relapse activity mirroring phase 3 trials

Ocrelizumab is a B cell-depleting drug widely used in relapsing-remitting multiple sclerosis (RRMS) and primary-progressive MS. In RRMS, it is becoming increasingly apparent that accumulation of disability not only manifests as relapse-associated worsening (RAW) but also as progression independent of relapse activity (PIRA) throughout the disease course. This study's objective was to investigate the role of PIRA in RRMS patients treated with ocrelizumab. We performed a single-center, retrospective, cross-sectional study of clinical data acquired at a German tertiary multiple sclerosis referral center from 2018 to 2022. All patients with RRMS treated with ocrelizumab for at least six months and complete datasets were analyzed. Confirmed disability accumulation (CDA) was defined as a ≥ 12-week confirmed increase from the previous expanded disability status scale (EDSS) score of ≥ 1.0 if the previous EDSS was ≤  5.5 or a ≥ 0.5-point increase if the previous EDSS was > 5.5. PIRA was defined as CDA without relapse since the last EDSS measurement and at least for the preceding 12 weeks. RAW was defined as CDA in an interval of EDSS measurements with ≥ 1 relapses. Cox proportional hazard models were used to analyze the probability of developing PIRA depending on various factors, including disease duration, previous disease-modifying treatments (DMTs), and optical coherence tomography-assessed retinal degeneration parameters. 97 patients were included in the analysis. Mean follow-up time was 29 months (range 6 to 51 months). 23.5% developed CDA under ocrelizumab therapy (n = 23). Of those, the majority developed PIRA (87.0% of CDA, n = 20) rather than RAW (13.0% of CDA, n = 3). An exploratory investigation using Cox proportional hazards ratios revealed two possible factors associated with an increased probability of developing PIRA: a shorter disease duration prior to ocrelizumab (p = 0.02) and a lower number of previous DMTs prior to ocrelizumab (p = 0.04). Our data show that in ocrelizumab-treated RRMS patients, the main driver of disability accumulation is PIRA rather than RAW. Furthermore, these real-world data show remarkable consistency with data from phase 3 randomized controlled trials of ocrelizumab in RRMS, which may increase confidence in translating results from tightly controlled RCTs into the real-world clinical setting.

Improving the efficiency of clinical trials in multiple sclerosis

BACKGROUND

Phase 3 clinical trials for disease-modifying therapies in relapsing-remitting multiple sclerosis (RRMS) have utilized a limited number of conventional designs with a high degree of success. However, these designs limit the types of questions that can be addressed, and the time and cost required. Moreover, trials involving people with progressive multiple sclerosis (MS) have been less successful.

OBJECTIVE

The objective of this paper is to discuss complex innovative trial designs, intermediate and composite outcomes and to improve the efficiency of trial design in MS and broaden questions that can be addressed, particularly as applied to progressive MS.

METHODS

We held an international workshop with experts in clinical trial design.

RESULTS

Recommendations include increasing the use of complex innovative designs, developing biomarkers to enrich progressive MS trial populations, prioritize intermediate outcomes for further development that target therapeutic mechanisms of action other than peripherally mediated inflammation, investigate acceptability to people with MS of data linkage for studying long-term outcomes of clinical trials, use Bayesian designs to potentially reduce sample sizes required for pediatric trials, and provide sustained funding for platform trials and registries that can support pragmatic trials.

CONCLUSION

Novel trial designs and further development of intermediate outcomes may improve clinical trial efficiency in MS and address novel therapeutic questions.

Prognosis Markers and Patient Health Behavior of COVID-19 on Treatment in Turkey

Objective: During the COVID-19 outbreak, the medical sector services to the public were reportedly not appropriate. Due to the novelty of the disease, the patients were not aware of the right treatment and what health facilities were required. Method: The current research is designed to determine the relationship between prognosis markers and patient health behavior in treatment of COVID-19 patients in Turkey. The sample was identified through cluster sampling method. A smart PLS statistical tool was utilized for structural equation model findings. Result: The findings show that patient treatment performance can be improved with adequate treatment strategies, patient health behavior, prognosis markers and performance status. This research is significant by its nature because it adopted a novel research model which established new relationships between the variables of the study. Practically, this research deliberated the importance of prognosis markers, patient health behavior, adequate treatment strategies, and performance status on patient treatment performance. Conclusion: This research faced several limitations that are reported in the end with the future directions for scholars to contribute further to the knowledge of patients' treatment performance.

Data Resource Profile: The Multiple Sclerosis Documentation System 3D and AOK PLUS Linked Database (MSDS-AOK PLUS)

Real-world evidence in multiple sclerosis (MS) is limited by the availability of data elements in individual real-world datasets. We introduce a novel, growing database which links administrative claims and medical records from an MS patient management system, allowing for the complete capture of patient profiles. Using the AOK PLUS sickness fund and the Multiple Sclerosis Documentation System MSDS^3D from the Center of Clinical Neuroscience (ZKN) in Germany, a linked MS-specific database was developed (MSDS-AOK PLUS). Patients treated at ZKN and insured by AOK PLUS were recruited and asked for informed consent. For linkage, insurance IDs were mapped to registry IDs. After the deletion of insurance IDs, an anonymized dataset was provided to a university-affiliate, IPAM e.V., for further research applications. The dataset combines a complete record of patient diagnoses, treatment, healthcare resource use, and costs (AOK PLUS), with detailed clinical parameters including functional performance and patient-reported outcomes (MSDS^3D). The dataset currently captures 500 patients; however, is actively expanding. To demonstrate its potential, we present a use case describing characteristics, treatment, resource use, and costs of a patient subsample. By linking administrative claims to clinical information in medical charts, the novel MSDS-AOK PLUS database can increase the quality and scope of real-world studies in MS.

One-year prediction of cognitive decline following cognitive-stimulation from real-world data

Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.

Comparative effectiveness of cladribine tablets versus other oral disease-modifying treatments for multiple sclerosis: Results from MSBase registry

BACKGROUND

Effectiveness of cladribine tablets, an oral disease-modifying treatment (DMT) for multiple sclerosis (MS), was established in clinical trials and confirmed with real-world experience.

OBJECTIVES

Use real-world data to compare treatment patterns and clinical outcomes in people with MS (pwMS) treated with cladribine tablets versus other oral DMTs.

METHODS

Retrospective treatment comparisons were based on data from the international MSBase registry. Eligible pwMS started treatment with cladribine, fingolimod, dimethyl fumarate, or teriflunomide tablets from 2018 to mid-2021 and were censored at treatment discontinuation/switch, death, loss to follow-up, pregnancy, or study period end. Treatment persistence was evaluated as time to discontinuation/switch; relapse outcomes included time to first relapse and annualized relapse rate (ARR).

RESULTS

Cohorts included 633 pwMS receiving cladribine tablets, 1195 receiving fingolimod, 912 receiving dimethyl fumarate, and 735 receiving teriflunomide. Individuals treated with fingolimod, dimethyl fumarate, or teriflunomide switched treatment significantly more quickly than matched cladribine tablet cohorts (adjusted hazard ratio (95% confidence interval): 4.00 (2.54-6.32), 7.04 (4.16-11.93), and 6.52 (3.79-11.22), respectively). Cladribine tablet cohorts had significantly longer time-to-treatment discontinuation, time to first relapse, and lower ARR, compared with other oral DMT cohorts.

CONCLUSION

Cladribine tablets were associated with a significantly greater real-world treatment persistence and more favorable relapse outcomes than all oral DMT comparators.

Utilization of real-world data in assessing treatment effectiveness for diffuse large B-cell lymphoma

Direct comparisons of the effectiveness of the numerous novel therapies in the diffuse large B-cell lymphoma (DLBCL) treatment landscape in a range of head-to-head randomized phase 3 trials would be time-consuming and costly. Comparative effectiveness studies using real-world data (RWD) represent a complementary approach. Recently, several studies of relapsed/refractory (R/R) DLBCL have used RWD to create observational cohorts to compare patient outcomes with cohorts derived from single-arm phase 2 trials. Using propensity score methods to balance clinically and prognostically relevant baseline covariates, closely matched patient-level cohorts can be generated. By incorporating appropriate measures to assess covariate balance and address potential bias in comparative effectiveness study designs, robust comparative analyses can be performed. Results from such studies have been used to supplement regulatory approval of therapies assessed in single-arm trials. While RWD studies have a greater susceptibility to bias compared to randomized controlled trials, well-designed and appropriately analyzed studies can provide complementary real-world evidence on treatment effectiveness.

Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis

Background

Consumer-grade fitness trackers offer exciting opportunities to study persons with chronic diseases in greater detail and in their daily-life environment. However, attempts to bring fitness tracker measurement campaigns from tightly controlled clinical environments to home settings are often challenged by deteriorating study compliance or by organizational and resource limitations.

Objectives

By revisiting the study design and patient-reported experiences of a partly remote study with fitness trackers (BarKA-MS study), we aimed to qualitatively explore the relationship between overall study compliance and scalability. On that account, we aimed to derive lessons learned on strengths, weaknesses, and technical challenges for the conduct of future studies.

Methods

The two-phased BarKA-MS study employed Fitbit Inspire HR and electronic surveys to monitor physical activity in 45 people with multiple sclerosis in a rehabilitation setting and in their natural surroundings at home for up to 8 weeks. We examined and quantified the recruitment and compliance in terms of questionnaire completion and device wear time. Furthermore, we qualitatively evaluated experiences with devices according to participants' survey-collected reports. Finally, we reviewed the BarKA-MS study conduct characteristics for its scalability according to the Intervention Scalability Assessment Tool checklist.

Results

Weekly electronic surveys completion reached 96%. On average, the Fitbit data revealed 99% and 97% valid wear days at the rehabilitation clinic and in the home setting, respectively. Positive experiences with the device were predominant: only 17% of the feedbacks had a negative connotation, mostly pertaining to perceived measurement inaccuracies. Twenty-five major topics and study characteristics relating to compliance were identified. They broadly fell into the three categories: "effectiveness of support measures", "recruitment and compliance barriers", and "technical challenges". The scalability assessment revealed that the highly individualized support measures, which contributed greatly to the high study compliance, may face substantial scalability challenges due to the strong human involvement and limited potential for standardization.

Conclusion

The personal interactions and highly individualized participant support positively influenced study compliance and retention. But the major human involvement in these support actions will pose scalability challenges due to resource limitations. Study conductors should anticipate this potential compliance-scalability trade-off already in the design phase.

New-Onset Diabetes Is a Potential Marker for the Malignant Transformation of Pancreatic Cysts: A Real-World Population Cohort Study

OBJECTIVES

New-onset diabetes mellitus has been shown to be associated with pancreatic cancer (PC) in the general population. Our objective was to leverage real-world data to assess the association of new-onset diabetes (NODM) with malignant transformation in a large longitudinal cohort of pancreatic cyst patients.

METHODS

A retrospective longitudinal cohort study was conducted using IBM's MarketScan claims databases from 2009 to 2017. From 200 million database subjects, we selected patients with newly diagnosed cysts without prior pancreatic pathology.

RESULTS

Of the 137,970 patients with a pancreatic cyst, 14,279 had a new diagnosis. Median follow-up was 41.6 months. Patients with NODM progressed to PC at nearly 3 times the rate of patients without a diabetes history (hazard ratio, 2.80; 95% confidence interval, 2.05-3.83) and at a significantly higher rate than patients with preexisting diabetes (hazard ratio, 1.59; 95% confidence interval, 1.14-2.21). The mean interval between NODM and cancer diagnosis was 7.5 months.

CONCLUSIONS

Cyst patients who developed NODM progressed to PC at 3 times the rate of nondiabetics and at a greater rate than preexisting diabetics. The diagnosis of NODM preceded cancer detection by several months. These results support the inclusion of diabetes mellitus screening in cyst surveillance algorithms.

Do patients’ and referral centers’ characteristics influence multiple sclerosis phenotypes? Results from the Italian multiple sclerosis and related disorders register

Background

Multiple sclerosis (MS) is characterized by phenotypical heterogeneity, partly resulting from demographic and environmental risk factors. Socio-economic factors and the characteristics of local MS facilities might also play a part.

Methods

This study included patients with a confirmed MS diagnosis enrolled in the Italian MS and Related Disorders Register in 2000–2021. Patients at first visit were classified as having a clinically isolated syndrome (CIS), relapsing–remitting (RR), primary progressive (PP), progressive-relapsing (PR), or secondary progressive MS (SP). Demographic and clinical characteristics were analyzed, with centers’ characteristics, geographic macro-areas, and Deprivation Index. We computed the odds ratios (OR) for CIS, PP/PR, and SP phenotypes, compared to the RR, using multivariate, multinomial, mixed effects logistic regression models.

Results

In all 35,243 patients from 106 centers were included. The OR of presenting more advanced MS phenotypes than the RR phenotype at first visit significantly diminished in relation to calendar period. Females were at a significantly lower risk of a PP/PR or SP phenotype. Older age was associated with CIS, PP/PR, and SP. The risk of a longer interval between disease onset and first visit was lower for the CIS phenotype, but higher for PP/PR and SP. The probability of SP at first visit was greater in the South of Italy.

Discussion

Differences in the phenotype of MS patients first seen in Italian centers can be only partly explained by differences in the centers’ characteristics. The demographic and socio-economic characteristics of MS patients seem to be the main determinants of the phenotypes at first referral.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10072-022-06169-7.

Use of follow-on disease-modifying treatments for multiple sclerosis: Consensus recommendations

BACKGROUND

As patents for multiple sclerosis (MS) therapies expire, follow-on disease-modifying treatments (FO-DMTs) become available at reduced cost. Concerns exist that cheaper FO-DMTs are used simply to reduce healthcare costs. However, the well-being of people with MS should take priority.

OBJECTIVES

To identify best practices for FO-DMT development and use by agreeing on principles and consensus statements through appraisal of published evidence.

METHODS

Following a systematic review, we formulated five overarching principles and 13 consensus statements. Principles and statements were voted on by a multidisciplinary panel from 17 European countries, Argentina, Canada and the United States.

RESULTS

All principles and statements were endorsed by >80% of panellists. In brief, FO-DMTs approved within highly regulated areas can be considered effective and safe as their reference products; FO-DMTs can be evaluated case by case and do not always require Phase III trials; long-term pharmacovigilance and transparency are needed; there is lack of evidence for multiple- and cross-switching among FO-DMTs; and education is needed to address remaining concerns.

CONCLUSION

Published data support the use of FO-DMTs in MS. The consensus may aid shared decision-making. While our consensus focused on Europe, the results may contribute to enhanced quality standards for FO-DMTs use elsewhere.

Real-world evidence for cladribine tablets in multiple sclerosis: further insights into efficacy and safety

Cladribine (CLAD) is a purine nucleoside analog approved in tablet form to treat highly active multiple sclerosis (MS). CLAD tablets are the first oral therapy with an infrequent dosing schedule, administered in two annual treatment courses, each divided into two treatment cycles comprising 4–5 days of treatment. The efficacy and safety of CLAD tablets have been verified in randomized controlled clinical trials. Clinical observational studies are performed in more representative populations and over more extended periods, and thus provide valuable complementary insights. Here, we summarize the available evidence for CLAD tablets from post-marketing trials, including two observational, four long-term extensions, and two comparative studies. The patients in the post-marketing setting differed from the cohort recruited in the pivotal phase III trials regarding demographics and MS-related disability. The limited number of studies with small cohorts corroborate the disease-modifying capacity of oral CLAD and report on a durable benefit after active treatment periods. Skin-related adverse events were common in the studies focusing on safety aspects. In addition, single cases of CLAD-associated autoimmune events have been reported. Lastly, CLAD tablets appear safe regarding COVID-19 concerns, and patients mount a robust humoral immune response to SARS-CoV‑2 vaccination. We conclude that the current real-world evidence for CLAD tablets as immune reconstitution therapy for treatment of MS is based on a small number of studies and a population distinct from the cohorts randomized in the pivotal phase III trials. Further research should advance the understanding of long-term disease control after active treatment periods and the mitigation of adverse events.

Challenges and Opportunities of Real-World Data: Statistical Analysis Plan for the Optimise:MS Multicenter Prospective Cohort Pharmacovigilance Study

Introduction

Optimise:MS is an observational pharmacovigilance study aimed at characterizing the safety profile of disease-modifying therapies (DMTs) for multiple sclerosis (MS) in a real world population. The study will categorize and quantify the occurrence of serious adverse events (SAEs) in a cohort of MS patients recruited from clinical sites around the UK. The study was motivated particularly by a need to establish the safety profile of newer DMTs, but will also gather data on outcomes among treatment-eligible but untreated patients and those receiving established DMTs (interferons and glatiramer acetate). It will also explore the impact of treatment switching.

Methods

Causal pathway confounding between treatment selection and outcomes, together with the variety and complexity of treatment and disease patterns observed among MS patients in the real world, present statistical challenges to be addressed in the analysis plan. We developed an approach for analysis of the Optimise:MS data that will include disproportionality-based signal detection methods adapted to the longitudinal structure of the data and a longitudinal time-series analysis of a cohort of participants receiving second-generation DMT for the first time. The time-series analyses will use a number of exposure definitions in order to identify temporal patterns, carryover effects and interactions with prior treatments. Time-dependent confounding will be allowed for via inverse-probability-of-treatment weighting (IPTW). Additional analyses will examine rates and outcomes of pregnancies and explore interactions of these with treatment type and duration.

Results

To date 14 hospitals have joined the study and over 2,000 participants have been recruited. A statistical analysis plan has been developed and is described here.

Conclusion

Optimise:MS is expected to be a rich source of data on the outcomes of DMTs in real-world conditions over several years of follow-up in an inclusive sample of UK MS patients. Analysis is complicated by the influence of confounding factors including complex treatment histories and a highly variable disease course, but the statistical analysis plan includes measures to mitigate the biases such factors can introduce. It will enable us to address key questions that are beyond the reach of randomized controlled trials.

Recommendations for the use of propensity score methods in multiple sclerosis research

BACKGROUND

With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies.

OBJECTIVES

To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research.

METHODS

We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice.

RESULTS

Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses.

CONCLUSION

This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.

Validation of a machine learning approach to estimate expanded disability status scale scores for multiple sclerosis

Background

Disability assessment using the Expanded Disability Status Scale (EDSS) is important to inform treatment decisions and monitor the progression of multiple sclerosis. Yet, EDSS scores are documented infrequently in electronic medical records.

Objective

To validate a machine learning model to estimate EDSS scores for multiple sclerosis patients using clinical notes from neurologists.

Methods

A machine learning model was developed to estimate EDSS scores on specific encounter dates using clinical notes from neurologist visits. The OM1 MS Registry data were used to create a training cohort of 2632 encounters and a separate validation cohort of 857 encounters, all with clinician-recorded EDSS scores. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV), calculated using a binarized version of the outcome. The Spearman R and Pearson R values were calculated. The model was then applied to encounters without clinician-recorded EDSS scores in the MS Registry.

Results

The model had a PPV of 0.85, NPV of 0.85, and AUC of 0.91. The model had a Spearman R value of 0.75 and Pearson R value of 0.74 when evaluating performance using the continuous estimated EDSS and clinician-recorded EDSS scores. Application of the model to eligible encounters resulted in the generation of eEDSS scores for an additional 190,282 encounters from 13,249 patients.

Conclusion

EDSS scores can be estimated with very good performance using a machine learning model applied to clinical notes, thus increasing the utility of real-world data sources for research purposes.

The need for a strategic therapeutic approach: multiple sclerosis in check

Multiple sclerosis (MS) is the most common chronic autoimmune neurological disease. Its therapeutic management has drastically evolved in the recent years with the development of specific disease-modifying therapies (DMTs). Together with the established injectables, oral and intravenous alternatives are now available for MS patients with significant benefits to modulate the disease course. Certain drugs present with a higher efficacy than the others, profiles and frequencies of adverse events differentiate as well. Thus due to the several and different treatment alternatives, the therapeutic approach adopted by neurologists requires a tactical focus for a targeted, timed, and meaningful treatment decision. An integration of rational and emotional control with proper communication skills is necessary for shared decision-making with patients. In this perspective paper, we reinforce the necessary concept of strategic MS treatment approach using all available therapies based on scientific evidence and current experience. We apply a didactic analogy to the strategic game chess. The opening with oriented attack (i.e. already in early disease stages as clinical isolated syndrome), a correct choice of chess pieces to move (i.e. among the several DMTs), a re-assessment reaction to different scenarios (e.g. sustained disease activity, adverse events, and family planning) and the advantage of real-world data are discussed to try the best approach to ultimately successfully approach the best personalized MS treatment.

Characterizing Long-term Disability Progression and Employment in NARCOMS Registry Participants with Multiple Sclerosis Taking Dimethyl Fumarate

Background

Delayed-release dimethyl fumarate (DMF) is effective in relapsing-remitting multiple sclerosis (RRMS), but long-term effects of DMF on disability and disease progression in clinical settings are unknown. We evaluated disability and employment outcomes in persons with RRMS treated with DMF for up to 5 years.

Methods

This longitudinal study included US North American Research Committee on Multiple Sclerosis (NARCOMS) Registry participants with RRMS reporting DMF initiation in fall 2013 through spring 2018 with 1 year or more of follow-up. Time to 6-month confirmed disability progression (≥1-point increase in Patient-Determined Disease Steps [PDDS] score) and change in employment status were evaluated using Kaplan-Meier analysis. Participants were censored at last follow-up or at DMF discontinuation, whichever came first.

Results

During the study, 725 US participants with RRMS had at least 1 year of DMF follow-up data, of whom most were female and White. At year 5, 69.9% (95% CI, 65.4%-73.9%) of these participants were free from 6-month confirmed disability progression, and 84.7% (95% CI, 78.6%-89.2%) were free from conversion to secondary progressive MS. Of 116 participants with data at baseline and year 5, most had stable or improved PDDS and Performance Scales scores over 5 years. Of 322 participants 62 years and younger and employed at the index survey, 66.0% (95% CI, 57.6%-73.1%) were free from a negative change in employment type over 5 years.

Conclusions

Most US NARCOMS Registry participants treated up to 5 years with DMF remained free from 6-month confirmed disability progression and conversion to secondary progressive MS and had stable disability and employment status. These results support the long-term stability of disability and work-related outcomes with disease-modifying therapy.

The Multiple Sclerosis Data Alliance Catalogue: Enabling Web-Based Discovery of Metadata from Real-World Multiple Sclerosis Data Sources

Background

One of the major objectives of the Multiple Sclerosis Data Alliance (MSDA) is to enable better discovery of multiple sclerosis (MS) real-world data (RWD).

Methods

We implemented the MSDA Catalogue, which is available worldwide. The current version of the MSDA Catalogue collects descriptive information on governance, purpose, inclusion criteria, procedures for data quality control, and how and which data are collected, including the use of e-health technologies and data on collection of COVID-19 variables. The current cataloguing procedure is performed in several manual steps, securing an effective catalogue.

Results

Herein we summarize the status of the MSDA Catalogue as of January 6, 2021. To date, 38 data sources across five continents are included in the MSDA Catalogue. These data sources differ in purpose, maturity, and variables collected, but this landscaping effort shows that there is substantial alignment on some domains. The MSDA Catalogue shows that personal data and basic disease data are the most collected categories of variables, whereas data on fatigue measurements and cognition scales are the least collected in MS registries/cohorts.

Conclusions

The Web-based MSDA Catalogue provides strategic overview and allows authorized end users to browse metadata profiles of data cohorts and data sources. There are many existing and arising RWD sources in MS. Detailed cataloguing of MS RWD is a first and useful step toward reducing the time needed to discover MS RWD sets and promoting collaboration.

Monitoring of safety and effectiveness of cladribine in multiple sclerosis patients over 50 years

Clinical trial data regarding efficacy and safety of cladribine in MS are limited to young individuals, and the overall risk-benefit profile does not necessarily applies to elderly patients. We investigated effectiveness and safety outcomes in MS patients initiating cladribine at ≥50 years (n=35) and <50 years (n=62), over a median follow-up of 12.4 months. There were no differences in time to evidence of disease activity (HR=0.73, 95%CI=0.18-2.91, p=0.657), post-treatment lymphocyte counts (β=0.24, p=0.825) or occurrence of adverse events (OR=0.84, 95%CI=0.24-2.93, p=0.791) between age groups. Female sex and greater disability were associated with higher risk of adverse events (especially infections). These limited data do not suggest safety concerns regarding use of cladribine in elderly MS.

Clinical and Research Applications of the Electronic Medical Record in Multiple Sclerosis: A Narrative Review of Current Uses and Future Applications

BACKGROUND

The electronic medical record (EMR) has revolutionized health care workflow and delivery. It has evolved from a clinical adjunct to a multifaceted tool, with uses relevant to patient care and research.

METHODS

A MEDLINE literature review was conducted to identify data regarding the use of EMR for multiple sclerosis (MS) clinical care and research.

RESULTS

Of 282 relevant articles identified, 29 were included. A variety of EMR integrated platforms with features specific to MS have been designed, with options for documenting disease course, disability status, and treatment. Research efforts have focused on early diagnosis identification, relapse prediction, and surrogates for disability status.

CONCLUSIONS

The available platforms and associated research support the utility of harnessing EMR for MS care. The adoption of a core set of discrete EMR elements should be considered to support future research efforts and the ability to harmonize data across institutions.

OPTIMISE: MS study protocol: a pragmatic, prospective observational study to address the need for, and challenges with, real world pharmacovigilance in multiple sclerosis

INTRODUCTION

The power of 'real world' data to improve our understanding of the clinical aspects of multiple sclerosis (MS) is starting to be realised. Disease modifying therapy (DMT) use across the UK is driven by national prescribing guidelines. As such, the UK provides an ideal country in which to gather MS outcomes data. A rigorously conducted observational study with a focus on pharmacovigilance has the potential to provide important data to inform clinicians and patients while testing the reliability of estimates from pivotal trials when applied to patients in the UK.

METHODS AND ANALYSIS

The primary aim of this study is to characterise the incidence and compare the risk of serious adverse events in people with MS treated with DMTs. The OPTIMISE:MS database enables electronic data capture and secure data transfer. Selected clinical data, clinical histories and patient-reported outcomes are collected in a harmonised fashion across sites at the time of routine clinical visits. The first patient was recruited to the study on 24 May 2019. As of January 2021, 1615 individuals have baseline data recorded; follow-up data are being captured and will be reported in due course.

ETHICS AND DISSEMINATION

This study has ethical permission (London City and East; Ref 19/LO/0064). Potential concerns around data storage and sharing are mitigated by the separation of identifiable data from all other clinical data, and limiting access to any identifiable data. The results of this study will be disseminated via publication. Participants provide consent for anonymised data to be shared for further research use, further enhancing the value of the study.

Innovation in Informatics to Improve Clinical Care and Drug Accessibility for Rare Diseases in China

Background: In China, there are severe unmet medical needs of people living with rare diseases. Relatedly, there is a dearth of data to inform rare diseases policy. This is historically partially due to the lack of informatics infrastructure, including standards and terminology, data sharing mechanisms and network; and concerns over patient privacy protection.

Objective: This study aims to introduce the progress of China's rare disease informatics platform and knowledgebase, and to discuss critical enablers of rare disease informatics innovation, including: data standardization; knowledgebase construction; national policy support; and multi-stakeholder participation.

Methods: A systemic national strategy, delivered through multi-stakeholder engagement, has been implemented to create and accelerate the informatics infrastructure to support rare diseases management. This includes a disease registry system, together with more than 80 hospitals, to perform comprehensive research information collection, including clinical, genomic and bio-sample data. And a case reporting system, with a network of 324 hospitals, covering all mainland Chinese provinces, to further support reporting of rare diseases data. International standards were incorporated, and privacy issues were addressed through HIPAA compliant rules.

Results: The National Rare Diseases Registry System of China (NRDRS) now covers 166 rare diseases and more than 63,000 registered patients. The National Rare Diseases Case Reporting System of China (NRDCRS) was primarily founded on the National Network of Rare Diseases (NNRD) of 324 hospitals and focused on real-time rare diseases case reporting; more than 400,000 cases have been reported. Based on the data available in the two systems, the National Center for Health Technology Assessment (HTA) of Orphan Medicinal Products (OMP) has been established and the expert consensus on HTA of OMP was produced. The largest knowledgebase for rare disease in Chinese has also been developed.

Conclusion: A national strategy and the coordinating mechanism is the key to success in the improvement of Chinese rare disease clinical care and drug accessibility. Application of innovative informatics solutions can help accelerate the process, improve quality and increase efficiency.

NARCOMS and Other Registries in Multiple Sclerosis: Issues and Insights

Observational studies and registries can play a critical role in elucidating the natural and treated history of multiple sclerosis (MS) and identifying factors associated with outcomes such as disability and health-related quality of life. The North American Research Committee on Multiple Sclerosis (NARCOMS) Registry is one of multiple registries worldwide that focuses on people with MS, but one of the very few patient-driven MS registries. On the 25th anniversary of the first data collection for the NARCOMS Registry, we discuss the importance of disease registries in the MS field, describe key concepts related to registry design and management, and highlight findings from MS registries relevant to clinical care or health policy.

Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): Age is a key contributor to presentation

BACKGROUND

The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set.

OBJECTIVE

The objective of this study is to describe the Novartis-Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes.

METHODS

We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients' baseline age, using phase III study data (≈8000 patients).

RESULTS

NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%-75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment.

CONCLUSION

NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity.

Prevalence, Incidence, and Risk of Progression of Asymptomatic Pancreatic Cysts in Large Sample Real-world Data

OBJECTIVES

Using large-sample, real-world administrative claims data, we evaluated the prevalence of putatively asymptomatic pancreatic cysts, the historical growth in their incident diagnosis, and their risk of malignant progression.

METHODS

Data were sourced from IBM MarketScan administrative claims databases of more than 200 million patients. Period prevalence was assessed using 700,000 individuals without conditions that predispose to pancreatic cyst. The standardized cumulative incidence was compared with the cross-sectional abdominal imaging rate from 2010-2017. The risk of progression to pancreatic cancer for 14,279 newly diagnosed patients with a cyst was estimated using Kaplan-Meier analysis.

RESULTS

Standardized prevalence increased exponentially with age and was 1.84% (95% confidence interval, 1.80%-1.87%) for patients older than 45. Standardized incidence nearly doubled from 2010-2017 (6.3 to 11.4 per 10,000), whereas the imaging rate changed from only 8.0% to 9.4%. The cumulative risk of pancreatic cancer at 7 years was 3.0% (95% confidence interval, 2.4%-3.5%), increasing linearly (R2 = 0.991) with an annual progression risk of 0.47%.

CONCLUSIONS

Using large-sample data, we show a significant burden of asymptomatic pancreatic cysts, with an annual risk of progression to cancer of 0.47% for 7 years. Rapid growth in cyst diagnosis over the last decade far outpaced increases in the imaging rate.

Associations between multiple sclerosis and incidence of heart diseases: Systematic review and meta-analysis of observational studies

BACKGROUND

Observational studies have described associations between multiple sclerosis (MS) and heart diseases, but the results were mixed.

METHODS

Medline, Embase, and Cochrane CENTRAL were searched up to 5 October 2020 according to a protocol (PROSPERO registration number CRD42020184493). We included longitudinal non-randomized studies of exposure comparing the incidence of acquired heart diseases between people with multiple sclerosis (pwMS) and people without multiple sclerosis. We used ROBINS-E and the GRADE approach to assess risk of bias and the certainty of evidence, respectively. Data were pooled using random-effect models.

RESULTS

Of 5,159 studies, nine studies met the inclusion criteria. MS was associated with an increased risk for myocardial infarction (HR 1.6, 95% CI 1.2 to 2.0, I2 86%, n = 1,209,079) and heart failure (HR 1.7, 95% CI 1.3 to 2.2, I2 49%, n = 489,814). The associations were more pronounced among women and younger people in subgroup analyses. We found no difference for ischemic heart disease (HR 1.0, 95% CI 0.8 to 1.4, I2 86%, n = 679,378) and bradycardia (HR 1.5, 95% CI 0.4 to 5.0, I2 50%, n = 187,810). The risk of atrial fibrillation was lower in pwMS (HR 0.7, 95% CI 0.6 to 0.8, I2 0%, n = 354,070), but the risk of bias was high, and the certainty of evidence was rated as very low. One study found more cases of infectious endocarditis among pwMS (HR 1.2, 95% CI 1.0 to 1.4, n = 83,712).

CONCLUSIONS

Myocardial infarction and heart failure should be considered in people with multiple sclerosis during follow-up examinations.

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

BACKGROUND AND OBJECTIVES

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem.

METHODS

We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used.

RESULTS

We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features.

CONCLUSIONS

Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS.

Early High Efficacy Treatment in Multiple Sclerosis Is the Best Predictor of Future Disease Activity Over 1 and 2 Years in a Norwegian Population-Based Registry

Background: Moderate and high efficacy disease modifying therapies (DMTs) have a profound effect on disease activity. The current treatment guidelines only recommend high efficacy DMTs for patients with highly active MS. The objective was to examine the impact of initial treatment choice in achieving no evidence of disease activity (NEDA) at year 1 and 2.

Methods: Using a real-world population-based registry with limited selection bias from the southeast of Norway, we determined how many patients achieved NEDA on moderate and high efficacy DMTs.

Results: 68.0% of patients who started a high efficacy DMT as the first drug achieved NEDA at year 1 and 52.4% at year 2 as compared to 36.0 and 19.4% of patients who started a moderate efficacy DMT as a first drug. The odds ratio (OR) of achieving NEDA on high efficacy drugs compared to moderate efficacy drugs as a first drug at year 1 was 3.9 (95% CI 2.4–6.1, p < 0.001). The OR for high efficacy DMT as the second drug was 2.5 (95% CI 1.7–3.9, p < 0.001), and was not significant for the third drug. Patients with a medium or high risk of disease activity were significantly more likely to achieve NEDA on a high efficacy therapy as a first drug compared to moderate efficacy therapy as a first drug.

Conclusions: Achieving NEDA at year 1 and 2 is significantly more likely in patients on high-efficacy disease modifying therapies than on moderate efficacy therapies, and the first choice of treatment is the most important. The immunomodulatory treatment guidelines should be updated to ensure early, high efficacy therapy for the majority of patients diagnosed with MS.

Real-world propensity score comparison of treatment effectiveness of peginterferon beta-1a vs. subcutaneous interferon beta-1a, glatiramer acetate, and teriflunomide in patients with relapsing-remitting multiple sclerosis

BACKGROUND

Multiple disease-modifying therapies (DMTs) have been approved by the U.S. Food & Drug Administration for the treatment of relapsing-remitting multiple sclerosis (RRMS). In separately conducted clinical trials, peginterferon beta-1a, subcutaneous interferon beta-1a (SC IFN beta-1a), glatiramer acetate (GA), and teriflunomide have demonstrated efficacy for reducing relapses. No head-to-head phase III clinical trials have directly compared the treatment efficacy of peginterferon beta-1a with these other DMTs.

OBJECTIVES

A propensity score-based comparison was conducted of the treatment effectiveness of peginterferon beta-1a vs. SC IFN beta-1a, GA, and teriflunomide among patients with RRMS identified from a large U.S. administrative healthcare claims database.

METHODS

Adult patients (18-65 years of age) who had ≥1 claim for an MS diagnosis between November 2013 and June 2017 and ≥1 claim for peginterferon beta-1a, SC IFN beta-1a, GA, or teriflunomide between November 1, 2014, and March 31, 2017 were identified from the IBM® MarketScan® Commercial database. The index date was the first claim of a patient's DMT initiated. Only patients who had ≥12 months of insurance enrollment pre-index (baseline period) and ≥90 days post-index (variable length follow-up period) were included. Patients were grouped into cohorts according to the index DMT. Patient demographics and clinical characteristics were evaluated. Propensity score matching (PSM) was separately conducted for pairwise comparisons of treatment effectiveness between peginterferon beta-1a and the other DMT cohorts. During the post-index follow-up period, annualized relapse rate (ARR; relapse defined as hospitalization or outpatient visit with subsequent treatment), annualized number and length of inpatient stays, and the number of claims for durable medical equipment were evaluated.

RESULTS

With PSM, there were 325 patients (mean age: 46.0 years) in the peginterferon beta-1a cohort compared to 967 (mean age: 46.9 years) in the SC IFN beta-1a cohort; likewise there were 564 patients (mean age: 47.4 years) in the peginterferon beta-1a and 1688 (mean age: 47.6 years) in the GA cohort; and finally there were 584 patients (mean age: 49.1 years) in the peginterferon beta-1a cohort and 1742 (mean age: 49.0 years) in the teriflunomide cohort. During the post-index follow-up period, the ARR did not significantly differ between the peginterferon beta-1a and SC IFN beta-1a cohorts; the ARR was lower among patients treated with peginterferon beta-1a than among those treated with GA (Least squares mean [LSM] estimate: 0.25 vs. 0.31; LSM ratio: 0.809; P=0.027) or teriflunomide (LSM estimate: 0.26 vs. 0.37; LSM ratio: 0.704; P<0.001). The annualized mean number and length of inpatient stays and the mean number of claims for durable medical equipment during the post-index follow-up did not differ between the matched peginterferon beta-1a and GA cohorts nor the peginterferon beta-1a and teriflunomide cohorts.

CONCLUSION

In this real-world comparative analysis of patients with similar patient characteristics, treatment with peginterferon beta-1a was associated with lower ARRs than treatment with either GA or teriflunomide; ARRs did not differ among patients treated with SC IFN beta-1a. Also, all other measured secondary outcomes did not differ between study cohorts. These real-world data may help support decision-making in the treatment of patients with RRMS.

The Value of Real-World Data in Understanding Prostate Cancer Risk and Improving Clinical Care: Examples from Swedish Registries

Simple Summary

Real-world data (RWD), i.e., data reflecting normal clinical practice collected outside the constraints of randomised controlled trials, provide important insights into our understanding of prostate cancer and its management. Clinical cancer registries are an important source of RWD. Depending on their scope and the potential linkage to other data sources, registry-based data can be utilised to address a variety of questions including risk factors, healthcare utilisation, treatment effectiveness, adverse effects, disparities in healthcare access, quality of care and healthcare economics. This review describes the various registry-based RWD sources for prostate cancer research in Sweden (namely the National Prostate Cancer Register, the Prostate Cancer data Base Sweden (PCBaSe) and the Patient-overview Prostate Cancer) and documents their utility for better understanding prostate cancer aetiology and improving clinical care.

Abstract

Real-world data (RWD), that is, data from sources other than controlled clinical trials, play an increasingly important role in medical research. The development of quality clinical registers, increasing access to administrative data sources, growing computing power and data linkage capacities have contributed to greater availability of RWD. Evidence derived from RWD increases our understanding of prostate cancer (PCa) aetiology, natural history and effective management. While randomised controlled trials offer the best level of evidence for establishing the efficacy of medical interventions and making causal inferences, studies using RWD offer complementary evidence about the effectiveness, long-term outcomes and safety of interventions in real-world settings. RWD provide the only means of addressing questions about risk factors and exposures that cannot be “controlled”, or when assessing rare outcomes. This review provides examples of the value of RWD for generating evidence about PCa, focusing on studies using data from a quality clinical register, namely the National Prostate Cancer Register (NPCR) Sweden, with longitudinal data on advanced PCa in Patient-overview Prostate Cancer (PPC) and data linkages to other sources in Prostate Cancer data Base Sweden (PCBaSe).

E-health and multiple sclerosis

PURPOSE OF REVIEW

To outline recent applications of e-health data and digital tools for improving the care and management of healthcare for people with multiple sclerosis.

RECENT FINDINGS

The digitization of most clinical data, along with developments in communication technologies, miniaturization of sensors and computational advances are enabling aggregation and clinically meaningful analyses of real-world data from patient registries, digital patient-reported outcomes and electronic health records (EHR). These data are allowing more confident descriptions of prognoses for multiple sclerosis patients and the long-term relative benefits and safety of disease-modifying treatments (DMT). Registries allow detailed, multiple sclerosis-specific data to be shared between clinicians more easily, provide data needed to improve the impact of DMT and, with EHR, characterize clinically relevant interactions between multiple sclerosis and other diseases. Wearable sensors provide continuous, long-term measures of performance dynamics in relevant ecological settings. In conjunction with telemedicine and online apps, they promise a major expansion of the scope for patients to manage aspects of their own care. Advances in disease understanding, decision support and self-management using these Big Data are being accelerated by machine learning and artificial intelligence.

SUMMARY

Both health professionals and patients can employ e-health approaches and tools for development of a more patient-centred learning health system.

Standardization and digitization of clinical data in multiple sclerosis

Standardization is necessary to ensure the reliability of clinical data and to enable longitudinal and cross-sectional comparisons of data obtained in different centres and countries. In patients with multiple sclerosis (MS), standardized clinical data are needed for monitoring of disability and for collecting real-world evidence for use in research. This Perspective describes attempts to improve the standardization and digitization of clinical data in MS, including digital electronic health recording systems and applications that attempt to offer a comprehensive assessment of patients' neurological deficits and their effects on daily life. Despite the challenges raised by regulatory, ethical and data-privacy considerations, the standardization and digitization of clinical data in MS is expected to generate new insights into the pathophysiology of the disease and to contribute to personalized patient care.

Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research

The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS.

The use and quality of reporting of propensity score methods in multiple sclerosis literature: A review

Background:

Propensity score (PS) analyses are increasingly used in multiple sclerosis (MS) research, largely owing to the greater availability of large observational cohorts and registry databases.

Objective:

To evaluate the use and quality of reporting of PS methods in the recent MS literature.

Methods:

We searched the PubMed database for articles published between January 2013 and July 2019. We restricted the search to comparative effectiveness studies of two disease-modifying therapies.

Results:

Thirty-nine studies were included in the review, with most studies (62%) published within the past 3 years. All studies reported the list of covariates used for the PS model, but only 21% of studies mentioned how those covariates were selected. Most studies used PS matching (72%), followed by PS adjustment (18%), weighting (15%), and stratification (3%), with some overlap. Most studies using matching or weighting reported checking post-PS covariate imbalance (91%), although about 45% of these studies relied on p values from various statistical tests. Only 25% of studies using matching reported calculating robust standard errors for the PS analyses.

Conclusions:

The quality of reporting of PS methods in the MS literature is sub-optimal in general, and in some cases, inappropriate methods are used.

The course of multiple sclerosis rewritten: a Norwegian population-based study on disease demographics and progression

OBJECTIVES

Over the past few decades, there has been an improvement in the rate of disability progression in multiple sclerosis (MS) patients, and most studies relate this evolvement to the introduction of disease-modifying therapies. However, several other factors have changed over this period, including access to MRI and newer diagnostic criteria. The aim of this study is to investigate changes in the natural course of MS over time in a near-complete and geographically well-defined population from the south-east of Norway.

METHODS

We examined disease progression and demographics over two decades and assessed the effect of disease-modifying therapies using linear mixed-effect models.

RESULTS

In a cohort of 2097 patients, we found a significant improvement in disability as measured by the Expanded Disability Status Scale (EDSS) stratified by age, and the improvement remained significant after adjusting for time on disease-modifying medications, gender and progressive MS at onset. The time from disease onset to EDSS 6 in the total cohort was 29.8 years (95% CI 28.5-31.1) and was significantly longer in patients diagnosed after 2006 compared to patients diagnosed before. There are significant differences between patient demographics, as well as time to EDSS 6, in the near-complete, geographically well-defined population compared to an additional cohort from the capital Oslo and its suburbs.

CONCLUSION

The natural course of MS is improving, but the improvement seen in disease progression has multifaceted explanations. Our study underlines the importance of completeness of data, relevant timeframes and demographics when comparing different MS populations. Studies on incomplete populations should be interpreted with caution.

Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS)

Background: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is the first example of a learning health system in multiple sclerosis (MS). This paper describes the initial implementation of MS PATHS and initial patient characteristics.

Methods: MS PATHS is an ongoing initiative conducted in 10 healthcare institutions in three countries, each contributing standardized information acquired during routine care. Institutional participation required the following: active MS patient census of ≥500, at least one Siemens 3T magnetic resonance imaging scanner, and willingness to standardize patient assessments, share standardized data for research, and offer universal enrolment to capture a representative sample. The eligible participants have diagnosis of MS, including clinically isolated syndrome, and consent for sharing pseudonymized data for research. MS PATHS incorporates a self-administered patient assessment tool, the Multiple Sclerosis Performance Test, to collect a structured history, patient-reported outcomes, and quantitative testing of cognition, vision, dexterity, and walking speed. Brain magnetic resonance imaging is acquired using standardized acquisition sequences on Siemens 3T scanners. Quantitative measures of brain volume and lesion load are obtained. Using a separate consent, the patients contribute DNA, RNA, and serum for future research. The clinicians retain complete autonomy in using MS PATHS data in patient care. A shared governance model ensures transparent data and sample access for research.

Results: As of August 5, 2019, MS PATHS enrolment included participants (n = 16,568) with broad ranges of disease subtypes, duration, and severity. Overall, 14,643 (88.4%) participants contributed data at one or more time points. The average patient contributed 15.6 person-months of follow-up (95% CI: 15.5–15.8); overall, 166,158 person-months of follow-up have been accumulated. Those with relapsing–remitting MS demonstrated more demographic heterogeneity than the participants in six randomized phase 3 MS treatment trials. Across sites, a significant variation was observed in the follow-up frequency and the patterns of disease-modifying therapy use.

Conclusions: Through digital health technology, it is feasible to collect standardized, quantitative, and interpretable data from each patient in busy MS practices, facilitating the merger of research and patient care. This approach holds promise for data-driven clinical decisions and accelerated systematic learning.

Data Collection in Multiple Sclerosis: The MSDS Approach

Multiple sclerosis (MS) is a frequent chronic inflammatory disease of the central nervous system that affects patients over decades. As the monitoring and treatment of MS become more personalized and complex, the individual assessment and collection of different parameters ranging from clinical assessments via laboratory and imaging data to patient-reported data become increasingly important for innovative patient management in MS. These aspects predestine electronic data processing for use in MS documentation. Such technologies enable the rapid exchange of health information between patients, practitioners, and caregivers, regardless of time and location. In this perspective paper, we present our digital strategy from Dresden, where we are developing the Multiple Sclerosis Documentation System (MSDS) into an eHealth platform that can be used for multiple purposes. Various use cases are presented that implement this software platform and offer an important perspective for the innovative digital patient management in the future. A holistic patient management of the MS, electronically supported by clinical pathways, will have an important impact on other areas of patient care, such as neurorehabilitation.

Long-term effectiveness in patients previously treated with cladribine tablets: a real-world analysis of the Italian multiple sclerosis registry (CLARINET-MS)

Background

The CLARINET-MS study assessed the long-term effectiveness of cladribine tablets by following patients with multiple sclerosis (MS) in Italy, using data from the Italian MS Registry.

Methods

Real-world data (RWD) from Italian MS patients who participated in cladribine tablets randomised clinical trials (RCTs; CLARITY, CLARITY Extension, ONWARD or ORACLE-MS) across 17 MS centres were obtained from the Italian MS Registry. RWD were collected during a set observation period, spanning from the last dose of cladribine tablets during the RCT (defined as baseline) to the last visit date in the registry, treatment switch to other disease-modifying drugs, date of last Expanded Disability Status Scale recording or date of the last relapse (whichever occurred last). Time-to-event analysis was completed using the Kaplan-Meier (KM) method. Median duration and associated 95% confidence intervals (CI) were estimated from the model.

Results

Time span under observation in the Italian MS Registry was 1-137 (median 80.3) months. In the total Italian patient population (n = 80), the KM estimates for the probability of being relapse-free at 12, 36 and 60 months after the last dose of cladribine tablets were 84.8%, 66.2% and 57.2%, respectively. The corresponding probability of being progression-free at 60 months after the last dose was 63.7%. The KM estimate for the probability of not initiating another disease-modifying treatment at 60 months after the last dose of cladribine tablets was 28.1%, and the median time-to-treatment change was 32.1 (95% CI 15.5-39.5) months.

Conclusion

CLARINET-MS provides an indirect measure of the long-term effectiveness of cladribine tablets. Over half of MS patients analysed did not relapse or experience disability progression during 60 months of follow-up from the last dose, suggesting that cladribine tablets remain effective in years 3 and 4 after short courses at the beginning of years 1 and 2.

Multiple Sclerosis in the Campania Region (South Italy): Algorithm Validation and 2015-2017 Prevalence

We aim to validate a case-finding algorithm to detect individuals with multiple sclerosis (MS) using routinely collected healthcare data, and to assess the prevalence of MS in the Campania Region (South Italy). To identify individuals with MS living in the Campania Region, we employed an algorithm using different routinely collected healthcare administrative databases (hospital discharges, drug prescriptions, outpatient consultations with payment exemptions), from 1 January 2015 to 31 December 2017. The algorithm was validated towards the clinical registry from the largest regional MS centre (n = 1460). We used the direct method to standardise the prevalence rate and the capture-recapture method to estimate the proportion of undetected cases. The case-finding algorithm including individuals with at least one MS record during the study period captured 5362 MS patients (females = 64.4%; age = 44.6 ± 12.9 years), with 99.0% sensitivity (95% CI = 98.3%, 99.4%). Standardised prevalence rate per 100,000 people was 89.8 (95% CI = 87.4, 92.2) (111.8 for females [95% CI = 108.1, 115.6] and 66.2 for males [95% CI = 63.2, 69.2]). The number of expected MS cases was 2.7% higher than cases we detected. We developed a case-finding algorithm for MS using routinely collected healthcare data from the Campania Region, which was validated towards a clinical dataset, with high sensitivity and low proportion of undetected cases. Our prevalence estimates are in line with those reported by international studies conducted using similar methods. In the future, this cohort could be used for studies with high granularity of clinical, environmental, healthcare resource utilisation, and pharmacoeconomic variables.

Data harmonization for collaborative research among MS registries: A case study in employment

OBJECTIVE

To assess the feasibility of collaboration and retrospective data harmonization among three multiple sclerosis (MS) registries by investigating employment status.

METHODS

We used the Maelstrom guidelines to facilitate retrospective harmonization of data from three MS registries, including the NARCOMS (North American Research Committee on MS) Registry, German MS Register (GMSR), and United Kingdom MS (UK-MS) Register. A protocol was developed based on the guidelines, and summary-level data were used to combine results. Employment status and a limited set of factors associated with employment (age, sex, education, and disability level) were harmonized. A meta-analytic approach was used to pool estimates using a weighted average of logistic regression estimates and their variances in a random effects model.

RESULTS

Employment status, age, sex, education, and disability were mapped. The overall employment rate was 57% (11,143 employed out of 19,562 persons with MS) with the GMSR having the highest proportion of participants employed (66.2%), followed by the UK-MS (55.2%) and NARCOMS (43.0%) registries. As disability level increased, the odds of not being employed increased.

CONCLUSION

Harmonization across registries was feasible. The Maelstrom guidelines provide a valuable roadmap for conducting high-quality harmonization projects. The pooling of data sources has the potential to be an important mechanism for conducting research in MS.