Patient-perceived progression in multiple system atrophy: natural history of quality of life.

BACKGROUND: Health-related quality of life (Hr-QoL) scales provide crucial information on neurodegenerative disease progression, help improve patient care and constitute a meaningful endpoint for therapeutic research. However, Hr-QoL progression is usually poorly documented, as for multiple system atrophy (MSA), a rare and rapidly progressing alpha-synucleinopathy. This work aimed to describe Hr-QoL progression during the natural course of MSA, explore disparities between patients and identify informative items using a four-step statistical strategy. METHODS: We leveraged the data of the French MSA cohort comprising annual assessments with the MSA-QoL questionnaire for more than 500 patients over up to 11 years. A four-step strategy (1) determined the subdimensions of Hr-QoL, (2) modelled the subdimension trajectories over time, (3) mapped item impairments with disease stages and (4) identified most informative items. RESULTS: Four dimensions were identified. In addition to the original motor, non-motor and emotional domains, an oropharyngeal component was highlighted. While the motor and oropharyngeal domains deteriorated rapidly, the non-motor and emotional aspects were already impaired at cohort entry and deteriorated slowly over the disease course. Impairments were associated with sex, diagnosis subtype and delay since symptom onset. Except for the emotional domain, each dimension was driven by key identified items. CONCLUSION: The multidimensional Hr-QoL deteriorates progressively over the course of MSA and brings essential knowledge for improving patient care. As exemplified with MSA, the thorough description of Hr-QoL over time using the four-step strategy can provide perspectives on neurodegenerative diseases' management to ultimately deliver better support focused on the patient's perspective.

Recent Advances in Clinical Trials in Multiple System Atrophy.

PURPOSE OF REVIEW: This review summarizes previous and ongoing neuroprotection trials in multiple system atrophy (MSA), a rare and fatal neurodegenerative disease characterized by parkinsonism, cerebellar, and autonomic dysfunction. It also describes the preclinical therapeutic pipeline and provides some considerations relevant to successfully conducting clinical trials in MSA, i.e., diagnosis, endpoints, and trial design. RECENT FINDINGS: Over 30 compounds have been tested in clinical trials in MSA. While this illustrates a strong treatment pipeline, only two have reached their primary endpoint. Ongoing clinical trials primarily focus on targeting α-synuclein, the neuropathological hallmark of MSA being α-synuclein-bearing glial cytoplasmic inclusions. The mostly negative trial outcomes highlight the importance of better understanding underlying disease mechanisms and improving preclinical models. Together with efforts to refine clinical measurement tools, innovative statistical methods, and developments in biomarker research, this will enhance the design of future neuroprotection trials in MSA and the likelihood of positive outcomes.

Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints.

Neurodegenerative diseases are characterized by numerous markers of progression and clinical endpoints. For instance, multiple system atrophy (MSA), a rare neurodegenerative synucleinopathy, is characterized by various combinations of progressive autonomic failure and motor dysfunction, and a very poor prognosis. Describing the progression of such complex and multi-dimensional diseases is particularly difficult. One has to simultaneously account for the assessment of multivariate markers over time, the occurrence of clinical endpoints, and a highly suspected heterogeneity between patients. Yet, such description is crucial for understanding the natural history of the disease, staging patients diagnosed with the disease, unravelling subphenotypes, and predicting the prognosis. Through the example of MSA progression, we show how a latent class approach modeling multiple repeated markers and clinical endpoints can help describe complex disease progression and identify subphenotypes for exploring new pathological hypotheses. The proposed joint latent class model includes class-specific multivariate mixed models to handle multivariate repeated biomarkers possibly summarized into latent dimensions and class-and-cause-specific proportional hazard models to handle time-to-event data. Maximum likelihood estimation procedure, validated through simulations is available in the lcmm R package. In the French MSA cohort comprising data of 598 patients during up to 13 years, five subphenotypes of MSA were identified that differ by the sequence and shape of biomarkers degradation, and the associated risk of death. In posterior analyses, the five subphenotypes were used to explore the association between clinical progression and external imaging and fluid biomarkers, while properly accounting for the uncertainty in the subphenotypes membership.

Joint models for the longitudinal analysis of measurement scales in the presence of informative dropout.

In health cohort studies, repeated measures of markers are often used to describe the natural history of a disease. Joint models allow to study their evolution by taking into account the possible informative dropout usually due to clinical events. However, joint modeling developments mostly focused on continuous Gaussian markers while, in an increasing number of studies, the actual quantity of interest is non-directly measurable; it constitutes a latent variable evaluated by a set of observed indicators from questionnaires or measurement scales. Classical examples include anxiety, fatigue, cognition. In this work, we explain how joint models can be extended to the framework of a latent quantity measured over time by indicators of different nature (e.g. continuous, binary, ordinal). The longitudinal submodel describes the evolution over time of the quantity of interest defined as a latent process in a structural mixed model, and links the latent process to each observation of the indicators through appropriate measurement models. Simultaneously, the risk of multi-cause event is modelled via a proportional cause-specific hazard model that includes a function of the mixed model elements as linear predictor to take into account the association between the latent process and the risk of event. Estimation, carried out in the maximum likelihood framework and implemented in the R-package JLPM, has been validated by simulations. The methodology is illustrated in the French cohort on Multiple-System Atrophy (MSA), a rare and fatal neurodegenerative disease, with the study of dysphagia progression over time stopped by the occurrence of death.

An Item Response Theory analysis of the Unified Multiple System Atrophy Rating Scale.

INTRODUCTION: The Unified Multiple System Atrophy Rating Scale (UMSARS) has four subscales that have been specifically designed for the clinical assessment of MSA patients. UMSARS I (activities of daily living) and II (motor examination) subscales are regularly used as primary endpoints in treatment trials. The main objective of this study was to identify UMSARS I and II subscale items that best describe progression over time. METHODS: All MSA patients seen at the French Reference Centre for MSA from 2007 to 2020 were included in a prospective cohort with an annual follow-up assessment including UMSARS. The repeated measures of the 26 UMSARS I and II items were analyzed using a longitudinal Item Response Theory model to identify the most informative items for each of the five UMSARS IV disease stages. Sample size estimates were further calculated for the most informative items as a group. RESULTS: A total of 557 MSA patients were included with a mean follow-up of 2.3 years. The majority of items progressed with disease duration or across the different UMSARS IV disability stages, with the exception of those related to dysautonomia. Roughly 70% of the scale information was carried by only 11/26 items, many reflecting the patient perspective. These yielded similar sample size estimates than UMSARS I + II items. CONCLUSION: This study provides important information about the progression of UMSARS I and II subscale items. Improvements seem particularly necessary regarding those assessing dysautonomia. A shortened scale may be useful as outcome for future clinical trials.

Impact of Coronavirus Disease 2019 in a French Cohort of Myasthenia Gravis.

OBJECTIVE: To describe the clinical characteristics and outcomes of coronavirus disease 2019 (COVID-19) among patients with myasthenia gravis (MG) and identify factors associated with COVID-19 severity in patients with MG. METHODS: The CO-MY-COVID registry was a multicenter, retrospective, observational cohort study conducted in neuromuscular referral centers and general hospitals of the FILNEMUS (Filière Neuromusculaire) network (between March 1, 2020, and June 8, 2020), including patients with MG with a confirmed or highly suspected diagnosis of COVID-19. COVID-19 was diagnosed based on a PCR test from a nasopharyngeal swab or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology, thoracic CT scan, or typical symptoms. The main outcome was COVID-19 severity based on location of treatment/management (home, hospitalized in a medical unit, or in an intensive care unit). We collected information on demographic variables, general history, and risk factors for severe COVID-19. Multivariate ordinal regression models were used to identify factors associated with severe COVID-19 outcomes. RESULTS: Among 3,558 patients with MG registered in the French database for rare disorders, 34 (0.96%) had COVID-19. The mean age at COVID-19 onset was 55.0 ± 19.9 years (mean MG duration: 8.5 ± 8.5 years). By the end of the study period, 28 patients recovered from COVID-19, 1 remained affected, and 5 died. Only high Myasthenia Gravis Foundation of America (MGFA) class (≥IV) before COVID-19 was associated with severe COVID-19 (p = 0.004); factors that were not associated included sex, MG duration, and medium MGFA classes (≤IIIb). The type of MG treatment had no independent effect on COVID-19 severity. CONCLUSIONS: This registry-based cohort study shows that COVID-19 had a limited effect on most patients, and immunosuppressive medications and corticosteroids used for MG management are not risk factors for poorer outcomes. However, the risk of severe COVID-19 is elevated in patients with high MGFA classes (odds ratio, 102.6 [4.4-2,371.9]). These results are important for establishing evidence-based guidelines for the management of patients with MG during the COVID-19 pandemic.