Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery Network Healthcare Data

Multiple Sclerosis: New Insights into Molecular Pathogenesis and Novel Platforms for Disease Treatment

BACKGROUND

Multiple sclerosis (MS), a chronic inflammatory disorder, affects the central nervous system via myelin degradation. The cause of MS is not fully known, but during recent years, our knowledge has deepened significantly regarding the different aspects of MS, including etiology, molecular pathophysiology, diagnosis and therapeutic options. Myelin basic protein (MBP) is the main myelin protein that accounts for maintaining the stability of the myelin sheath. Recent evidence has revealed that MBP citrullination or deamination, which is catalyzed by Ca2+ dependent peptidyl arginine deiminase (PAD) enzyme leads to the reduction of positive charge, and subsequently proteolytic cleavage of MBP. The overexpression of PAD2 in the brains of MS patients plays an essential role in new epitope formation and progression of the autoimmune disorder. Some drugs have recently entered phase III clinical trials with promising efficacy and will probably obtain approval in the near future. As different therapeutic platforms develop, finding an optimal treatment for each individual patient will be more challenging.

AIMS

This review provides a comprehensive insight into MS with a focus on its pathogenesis and recent advances in diagnostic methods and its present and upcoming treatment modalities.

CONCLUSION

MS therapy alters quickly as research findings and therapeutic options surrounding MS expand. McDonald's guidelines have created different criteria for MS diagnosis. In recent years, ever-growing interest in the development of PAD inhibitors has led to the generation of many reversible and irreversible PAD inhibitors against the disease with satisfactory therapeutic outcomes.

Treatment Patterns by Race and Ethnicity in Newly Diagnosed Persons with Multiple Sclerosis

BACKGROUND

Non-Hispanic Black and Hispanic persons with MS (pwMS) are more likely to experience rapid disease progression and severe disability than non-Hispanic White pwMS; however, it is unknown how the initiation of high-efficacy disease-modifying therapies (DMTs) differs by race/ethnicity. This real-world study describes DMT treatment patterns in newly diagnosed pwMS in the United States (US) overall and by race/ethnicity.

METHODS

This retrospective analysis used the US Optum Market Clarity claims/electronic health records database (January 2015-September 2020). pwMS who were first diagnosed in 2016 or later and initiated any DMT in the two years following diagnosis were included. Continuous enrollment in the claims data for ≥ 12 months before and ≥ 24 months after diagnosis was required. Treatment patterns 2 years after diagnosis were analyzed descriptively overall and by race/ethnicity.

RESULTS

The sample included 682 newly diagnosed and treated pwMS (non-Hispanic Black, n = 99; non-Hispanic White, n = 479; Hispanic, n = 35; other/unknown race/ethnicity, n = 69). The mean time from diagnosis to DMT initiation was 4.9 months in all pwMS. Glatiramer acetate and dimethyl fumarate were the most common first-line DMTs in non-Hispanic Black (28% and 20% respectively) and Hispanic pwMS (31%, 29%); however, glatiramer acetate and ocrelizumab were the most common in non-Hispanic White pwMS (33%, 18%). Use of first-line high-efficacy DMTs was limited across all race/ethnicity subgroups (11-29%), but uptake increased in non-Hispanic Black and White pwMS over the study period.

CONCLUSION

Use of high-efficacy DMTs was low across all race/ethnicity subgroups of newly diagnosed pwMS in the US, including populations at a greater risk of experiencing rapid disease progression and severe disability.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Medication adherence to disease-modifying therapies among a cohort of Jordanian patients with relapsing-remitting multiple sclerosis: a multicentre cross-sectional study

OBJECTIVES

To evaluate medication adherence to oral and parenteral disease-modifying therapies (DMTs) and to explore factors associated with medication non-adherence in patients with multiple sclerosis (MS).

METHODS

A cross-sectional multicentre study was conducted among patients with MS. Patients who attended outpatient clinics of neurology departments from three major referral centres were invited to participate in the study. Medication adherence was measured using the Multiple Sclerosis Treatment Adherence Questionnaire.

KEY FINDINGS

A total of 319 patients with MS on DMT were included in the final analyses, their average age was 35 years and more than two-thirds (72.1%) of them were women. The adherent group comprised 46.7% of patients. The results of association analyses showed that factors that were associated with adherence level were female gender (P = 0.034), non-smoking/x-smoking (P = 0.007), school education (P = 0.019), unemployment (P = 0.006), history of previous DMT (P = 0.020), longer previous treatment duration (P = 0.008), and type of current DMT (P = 0.020). Among the non-adherent patients, there were significant differences between oral and parenteral DMT users in the importance of barriers to adherence (P < 0.001). Additionally, the degree of treatment satisfaction was higher in oral users than in parenteral users (P < 0.001).

CONCLUSIONS

The adherence level was quite low. Gender, smoking status, education, employment status, history of previous DMT, previous treatment duration and type of current DMT were associated with medication non-adherence in our patients with MS. These factors should be considered when evaluating medication adherence, and the modifiable factors may represent potential targets for interventions to improve pharmaceutical care planning in patients with MS.

Medication Adherence and Healthcare Utilization in Relapsing-Remitting Multiple Sclerosis Patients Using Teriflunomide

Purpose

Multiple sclerosis (MS) is a costly, immune-mediated disease of the central nervous system. Most patients have relapsing-remitting MS (RRMS) for which disease-modifying therapies (DMTs) provide an effective treatment option by reducing relapse rates. However, adherence to DMTs is suboptimal. This study examines the association between adherence to teriflunomide and clinical and healthcare utilization outcomes.

Patients and Methods

Patients with RRMS who started treatment with teriflunomide between 1/1/2018 and 12/31/2019 were analyzed using IQVIA PharMetrics^® Plus data. RRMS patients were identified via diagnosis codes and treatment types; the first prescription date for teriflunomide was the index date. Highly and poorly adherent patients were identified based on the proportion of days covered (PDC) post-index (PDC ≥0.8 and PDC ≤0.5, respectively). Patient demographics, clinical characteristics, healthcare utilization during the year pre- and post-index, and relapse rate post-index were reported descriptively. Outcomes were compared between highly and poorly adherent patients through logistic regression. Models were adjusted for demographics, comorbidities, and utilization measures during the baseline period.

Results

Among the 922 RRMS patients identified, 534 (57.9%) were highly adherent to teriflunomide, while 249 (27.0%) had PDC ≤0.5. The two groups were not statistically different in terms of demographic characteristics and comorbidities; however, poorly adherent patients were more likely to have emergency department (ED) or inpatient visits during baseline (36.9% versus 26.8%, P=0.004; 17.3% versus 10.9%, P=0.013, respectively). Unadjusted results suggested lower likelihood of both relapses and utilization during follow-up among highly adherent patients compared to poorly adherent patients. Adjusted results confirmed that high adherence was associated with decreased likelihood of post-index relapses, ED utilization, and inpatient utilization (OR [95% CI]: 0.55 [0.39-0.76], 0.49 [0.34-0.71], and 0.51 [0.27-0.97], respectively) even after controlling for baseline utilization.

Conclusion

High adherence to teriflunomide was found to be associated with fewer relapses and lower healthcare utilization among patients with RRMS.

The Epidemiology, Treatment Patterns and Economic Burden of Different Phenotypes of Multiple Sclerosis in Italy: Relapsing-Remitting Multiple Sclerosis and Secondary Progressive Multiple Sclerosis

PURPOSE

A retrospective analysis of real-world data was performed to assess the epidemiology and economic burden of multiple sclerosis (MS), relapsing-remitting MS (RRMS), and secondary-progressive MS (SPMS) in Italy.

PATIENTS AND METHODS

An observational study on administrative databases from a sample of Italian entities was carried-out. Between 01/2010-12/2017, patients with ≥1 MS diagnosis code (ICD-9-CM:340 and/or exemption code:046) and/or ≥1 disease-modifying therapies (DMTs) prescription, were included. Among MS-cohort, SPMS patients were identified by ≥2 hospitalizations or by ≥2 drug prescriptions related to MS progression. MS patients not fulfilling SPMS criteria were included as RRMS. Mean annual healthcare costs were reported during follow-up and stratified by DMT treatment/untreatment.

RESULTS

Overall, 9543 MS patients were included; 8397 with RRMS and 1146 with SPMS. Estimated prevalence of MS was 141.6/100,000 inhabitants (RRMS 124.4/100,000 and SPMS 17.2/100,000). Mean annual cost for untreated and treated patient was respectively: €3638 and €11796 (MS-cohort), €3183 and €11486 (RRMS-cohort), €6317 and €15511 (SPMS-cohort). The first-line DMT treatment duration averaged 27.4 ± 22.8 months; the mean cost was 19004€ for the whole period. The second-line DMT treatment lasted on average 31.1 ± 24.5 months; the mean cost was 47293€ for the whole period.

CONCLUSION

This study provided insights into the MS epidemiology in Italy and its economic burden. Healthcare costs associated with MS management were mainly driven by DMTs expenditure. A trend of higher healthcare-resource consumption was observed among SPMS-cohort.

Temporal trends of multiple sclerosis disease activity: Electronic health records indicators

BACKGROUND

Long-term data on multiple sclerosis (MS) inflammatory disease activity are limited. We examined electronic health records (EHR) indicators of disease activity in people with MS.

METHODS

We analyzed prospectively collected research registry data and linked EHR data in a clinic-based cohort from 2000 to 2016. We used the trend of the yearly incident relapse rate from the registry data as benchmark. We then calculated the temporal trends of potentially relevant EHR measures, including mean count of the MS diagnostic code, mentions of MS-related concepts, MS-related health utilizations and selected prescriptions.

RESULTS

1,555 MS patients had both registry and EHR data. Between 2000 and 2016, the registry data showed a declining trend in the yearly incident relapse rate, parallel to an increasing trend of DMT usage. Among the EHR measures, covariate-adjusted frequency of diagnostic code of MS, procedure codes of MS-related imaging studies and emergency room visits, and electronic prescription for steroids declined over time, mirroring the temporal trend of the benchmark yearly incident relapse rate.

CONCLUSION

This study highlights EHR indicators of MS relapse that could enable large-scale examination of long-term disease activities or inform individual patient monitoring in clinical settings where EHR data are available.

Creating a Real-World Data, United States Healthcare Claims-Based Adaptation of Kurtzke Functional Systems Scores for Assessing Multiple Sclerosis Severity and Progression

Introduction

This article describes the development of a unique mapping of the Kurtzke Functional Systems Scores (KFSS) from International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) codes among multiple sclerosis (MS) patients within a US Integrated Delivery Network (IDN). Valid identification of increasing disability may allow deeper insight into MS progression and possible treatments.

Methods

This cohort study identified MS patients in the IDN, Intermountain Healthcare. Experienced clinicians and informaticists mapped electronic health record ICD-9-CM codes to KFSS components generating a modified Kurtzke Expanded Disability Status Scale (EDSS). Modified EDSS scores were used to assess disability progression by calculating means, medians, ranges, and changes in KFSS and modified EDSS scores.

Results

Overall, 608/2960 (20.5%) patients were identified as having MS progression and presented a wide range of scores on the EDSS 10-point scale. The median (range) first and second EDSS scores were 0 (0–6) and 5 (1–8), respectively. The median (range) change from first to second score was 5 (1–7.5). The median first KFSS score for all systems was 0, and the mean differed among components. The highest mean first KFSS score (1.06) was measured for sensory function and lowest (0.12) for cerebellar functions. Of the 544 patients with their first EDSS scores in the ≤ 2.5 group, 75.2% and 15.1% had their second EDSS scores in group 3–5.5 and ≥ 6, respectively. Of the 62 patients with their first EDSS score in the 3–5.5 group, 58.1% had their second EDSS scores in group ≥ 6.

Conclusion

This innovative mapping technique is a promising method for future comparative effectiveness and safety research of Disease-Modifying Therapy in Real-World Data repositories. Future research to validate and expand on this method in another healthcare database is encouraged.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-021-01858-9.

Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records

Background

The epidemiologic impact of hereditary angioedema (HAE) is difficult to quantify, due to misclassification in retrospective studies resulting from non-specific diagnostic coding. The aim of this study was to identify cohorts of patients with HAE-1/2 by evaluating structured and unstructured data in a US ambulatory electronic medical record (EMR) database.

Methods

A retrospective feasibility study was performed using the GE Centricity EMR Database (2006–2017). Patients with ≥ 1 diagnosis code for HAE-1/2 (International Classification of Diseases, Ninth Revision, Clinical Modification 277.6 or International Classification of Diseases, Tenth Revision, Clinical Modification D84.1) and/or ≥ 1 physician note regarding HAE-1/2 and ≥ 6 months’ data before and after the earliest code or note (index date) were included. Two mutually exclusive cohorts were created: probable HAE (≥ 2 codes or ≥ 2 notes on separate days) and suspected HAE (only 1 code or note). The impact of manually reviewing physician notes on cohort formation was assessed, and demographic and clinical characteristics of the 2 final cohorts were described.

Results

Initially, 1691 patients were identified: 190 and 1501 in the probable and suspected HAE cohorts, respectively. After physician note review, the confirmed HAE cohort comprised 254 patients and the suspected HAE cohort decreased to 1299 patients; 138 patients were determined not to have HAE and were excluded. The overall false-positive rate for the initial algorithms was 8.2%. Across final cohorts, the median age was 50 years and > 60% of patients were female. HAE-specific prescriptions were identified for 31% and 2% of the confirmed and suspected HAE cohorts, respectively.

Conclusions

Unstructured EMR data can provide valuable information for identifying patients with HAE-1/2. Further research is needed to develop algorithms for more representative HAE cohorts in retrospective studies.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

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.

Identifying people with multiple sclerosis in the Canadian Primary Care Sentinel Surveillance Network

BACKGROUND

We validated a case definition for multiple sclerosis using a clinical cohort linked with the Manitoba Primary Care Research Network of the Canadian Primary Care Sentinel Surveillance Network, and applied this definition to describe multiple sclerosis epidemiology using the Canadian Primary Care Sentinel Surveillance Network repository.

METHODS

We developed candidate case definitions for multiple sclerosis in the Manitoba Primary Care Research Network using diagnoses and medications. We compared these case definitions to multiple sclerosis diagnoses identified by applying a validated definition to population-based administrative data (reference standard 1) and multiple sclerosis diagnoses recorded by the provincial Multiple Sclerosis Clinic (reference standard 2) using sensitivity, specificity, positive predictive value and negative predictive value. We applied the preferred case definition to the national Canadian Primary Care Sentinel Surveillance Network dataset.

RESULTS

The Manitoba Primary Care Research Network included 160,904 patients. The preferred case definition required ≥2 billing records for multiple sclerosis within 2 years or multiple sclerosis listed as a health condition or ≥1 multiple sclerosis-specific prescription. This definition had a low sensitivity versus administrative (44.25%) and clinic datasets (53.41%) but high specificity versus administrative data (99.95%). Specificity was lower versus clinic data (71.43%), but the positive predictive value was high.

CONCLUSION

We developed a case definition for multiple sclerosis that can be applied to the Canadian Primary Care Sentinel Surveillance Network dataset for studies examining primary care of persons with multiple sclerosis.