Abstract
Introduction
Limited data are available on the relationship between myasthenia gravis (MG) severity and MG exacerbations and healthcare resource utilization (HCRU) following exacerbations. The objective of this study was to assess patient characteristics, exacerbation risk in relation to the MG Activities of Daily Living (MG-ADL) score, and HCRU following exacerbation.
Methods
This was a retrospective, cross-sectional, observational study of the patient-reported Myasthenia Gravis Foundation of America Global MG Patient Registry (MGFAPR).
Participants were based in the USA, aged ≥ 18 years, had a self-reported MG diagnosis and complete MG-ADL data, and were enrolled between July 1, 2013 and September 30, 2022. Patient demographics, disease characteristics, and HCRU were stratified by MG-ADL score. Negative binomial regression was used to assess the association between MG-ADL score and exacerbation. HCRU for those who had one exacerbation was calculated.
Results
In total, 3416 patients (2092 [61.2%] females) were eligible; mean (standard deviation) age at diagnosis was 49.4 (17.4) years. Compared with patients in the groups with lower MG-ADL scores (≤ 7), more patients in the higher MG-ADL groups (> 7) were female, younger at the time of MG diagnosis, Black, unemployed, uninsured, had a greater comorbidity burden, and had a shorter disease duration. A positive association between the number of exacerbations and MG-ADL score was observed at enrollment. For each additional point on the MG-ADL score, the rate of exacerbations increased by 13% (incidence rate ratio: 1.13; 95% confidence interval: 1.11–1.15; p < 0.001). At enrollment, 49.6% (n = 386/778) of patients who had one exacerbation had HCRU.
Conclusions
We found socio-demographic disparities in disease severity, a higher comorbidity burden, and an increased MG exacerbation risk with higher MG-ADL scores, with a significant impact of MG exacerbation on HCRU. These results highlight the relationship of MG-ADL score to clinical outcomes and the need for treatment optimization and personalized approaches to MG management, especially in socio-demographic groups with an increased risk of exacerbations.
Keywords: Myasthenia gravis, Retrospective, Observational, Registry, Socio-demographics, Exacerbations, HCRU, Disease severity
Plain Language Summary
We investigated the relationship between the severity of myasthenia gravis (MG), patient characteristics, and the risk of MG worsening (exacerbation). We also studied the impact of exacerbations on use of healthcare services. There were social, economic, and racial differences between people with more severe MG compared with those with less severe MG. A greater number of people with more severe MG had other diseases than those with less severe MG. Additionally, having more severe MG symptoms increased the risk of exacerbation. Healthcare services were needed in about 50% of exacerbation cases.
Key Summary Points
| Why carry out this study? |
| Exacerbations and healthcare resource utilization (HCRU) are a burden for patients with myasthenia gravis (MG); however, there are limited data available on the relationship between disease severity and MG exacerbations and on the impact of these exacerbations on HCRU. |
| We looked at patient characteristics, the relationship between exacerbation and the severity of MG as measured by the Myasthenia Gravis Activities of Daily Living (MG-ADL) score, and HCRU following exacerbation. |
| What was learned from the study? |
| A higher MG-ADL score was associated with an increased risk of exacerbation and a greater comorbidity burden; healthcare services were needed by about 50% of patients who experienced an exacerbation, reflecting a high HCRU burden for patients with increased MG-ADL scores. |
| We also highlight socioeconomic disparities in disease severity, which warrant additional research on racial and social inequities in healthcare access. |
| MG-ADL is a commonly used clinical endpoint in clinical studies and provides insights into the quantifiable risk from MG-ADL change to clinical outcomes. |
Introduction
Myasthenia gravis (MG) is a rare, chronic, autoimmune disease of the neuromuscular junction (NMJ) characterized by fluctuating chronic muscle weakness and fatigue [1–3]. Immunoglobulin G autoantibodies that inhibit signal transmission at the NMJ by binding to proteins at the postsynaptic membrane are a major mechanism involved in the MG pathology [2, 3].
Conventional therapies for MG typically include acetylcholinesterase inhibitors (AChEIs) with or without corticosteroids for initial symptomatic treatment [4]. Nonsteroidal immunosuppressive therapies, such as azathioprine, cyclosporine, or methotrexate, may be used in patients who do not respond to AChEIs or in patients who have side effects from corticosteroids [4]. However, many patients with MG continue to experience limitations in their day-to-day living, exacerbation of myasthenic symptoms, or a life-threatening myasthenic crisis despite treatment with conventional therapies [5, 6]. Further, patients with a history of myasthenic crisis or exacerbation tend to have higher levels of healthcare resource utilization (HCRU) and thus have an increased disease burden compared with patients with MG who do not have a history of myasthenic crisis or exacerbation [7, 8]. Recent approvals of novel targeted treatments, such as complement inhibitors or neonatal Fc receptor inhibitors, and their integration into the MG treatment paradigm, may help to minimize this disease burden [9–12].
The Myasthenia Gravis Activities of Daily Living (MG-ADL) scale is a validated, eight-item, usually patient-reported outcome measure that assesses the effects that MG symptoms have on patients’ daily activities. It is a standard outcome measure used in clinical trials to assess the severity of MG symptoms [13, 14]. Each of the eight items in the MG-ADL scale is rated from 0 to 3, with 0 being normal or no impairment and 3 being the most constant (for ocular items) or the most severe impairment (for non-ocular items). Scores of individual items are subsequently summed. A person’s score can range from 0 (normal) to 24 (most severe), with higher scores indicating a higher level of functional disability [14]. Despite the clinical relevance of MG-ADL score to the patient’s disease, there are limited data on the relationship between MG-ADL score and risk of MG exacerbation and on the characterization of MG-exacerbation-related HCRU.
The Myasthenia Gravis Foundation of America (MGFA) initiated the MGFA Global MG Patient Registry (MGFAPR) in 2013. This is a voluntary, online, patient-reported registry that captures the natural history of MG among patients in the United States (US) (https://www.mgregistry.org). Previous studies using the MGFAPR have investigated the impact of MG on patients’ health-related quality of life and trends in MG treatments and the burden of disease, with the aim of improving patient care and management [6, 15–19]. The objective of this retrospective, cross-sectional, observational study was to assess the patient characteristics, the exacerbation risk in relation to MG-ADL score, and the level of HCRU following exacerbation using data from the MGFAPR.
Methods
Data Source and Patient Population
On July 1, 2013, the MGFAPR opened to enrollment for adults aged ≥ 18 years with a self-reported MG diagnosis who were residing in the US, and it has been hosted on Alira Health’s Health Storylines platform since June 2022 (https://www.mgregistry.org). To contribute to the MGFAPR, patients with MG complete a comprehensive enrollment questionnaire and are then invited to complete a follow-up survey every 6 months. Patients who enrolled in the MGFAPR between July 1, 2013 and September 30, 2022 with a completed MG-ADL questionnaire were included in this study. Patients who responded “yes” to being diagnosed with MG by their doctor were included. Patients who reported a misdiagnosis for MG at any point in time post-enrollment were excluded. The authors obtained permissions to use the MG-ADL questionnaire.
This study analyzed survey data at enrollment and at the patient’s first follow-up. The MGFAPR is approved by the WCG Institutional Review Board (IRB tracking number: 20223601), and the database is Health Insurance Portability and Accountability Act compliant. This study followed the 2005 Guidelines for Good Pharmacoepidemiological Practice (GPP) and the 2015 International Society of Pharmacoepidemiology Guidelines for GPP. All patients provided informed, voluntary consent to enroll in the MGFAPR and for their data to be used in research studies.
The MGFAPR is an IRB-approved study, and this approval authorizes the analysis of de-identified data without requiring additional review or approval by the IRB. Patients provide their consent for data to be used for research purposes at the time of enrollment. Only de-identified data are analyzed in this manuscript. This study did not involve an investigation of clinical outcomes with any intervention.
Study Variables and Outcomes
The MG-ADL scale was provided as part of the MGFAPR survey for patients to self-complete. The preceding 4 weeks were given as the recall period, as predetermined by the MGFA. An MG exacerbation was defined in the MGFAPR as having longer than 7 days of new symptoms or a worsening of old symptoms, based on the patient’s interpretation, with at least 30 days since the last exacerbation. Based on this definition of MG exacerbation, patients were asked if they had had an exacerbation in the last 6 months, with a response choice of “yes,” “no,” or “unsure.” If the response was “yes,” patients were asked how many exacerbations they had, with a response choice of “1,” “2,” “3,” “4,” or “5 or more” relapses. HCRU following an exacerbation was defined as the use of intravenous immunoglobulin (IVIg) or plasma exchange (PLEX) due to an exacerbation in the last 6 months or overnight hospitalization due to “exacerbation or worsening of MG” in the last 6 months. Patients could also select multiple options as reasons for overnight hospitalization, including “rehabilitation,” “other MG-related problem,” and “non-MG-related problem,” but these events were not included in this analysis. A response of “yes” to the use of IVIg or PLEX or to hospitalization due to “exacerbation or worsening of MG” was considered as relevant HCRU and included in the analysis. Other variables to assess the socio-demographics (such as employment status and insurance type), disease characteristics (such as comorbidities), and HCRU (such as intensive care unit visits and MG treatment) were also included in the survey at enrollment and follow-up.
Data and Statistical Analyses
Deidentified data from the MGFAPR were extracted and analyzed using R statistical software (version 4.2.2). Continuous outcome variables are presented as mean, standard deviation (SD), median, interquartile range (IQR), and minimum and maximum values. Categorical variables are presented as n (%). Descriptive statistics for patient socio-demographics, disease characteristics, and level of HCRU at enrollment are reported for the overall population and stratified by MG-ADL score: 0–1, 2–4, 5–7, 8–10, 11–13, and 14+. This MG-ADL score stratification was used to maximize the level of granularity versus using a single threshold or cut-off point. All missing data were reported in descriptive analyses as “unknown or missing.” Statistical tests for comparing groups presenting different levels of MG severity (i.e., different MG-ADL score groups) were conducted using Pearson’s chi-squared test for qualitative variables and analysis of variance for quantitative variables. When the normal distribution was violated, the Kruskal–Wallis rank sum test was used. Negative binomial regression was used to assess the relationship between exacerbation and MG-ADL score. MG-ADL score was modeled in regression analyses as either a count or a categorical variable. The proportion of patients who had exactly one exacerbation and required HCRU was calculated with a 95% confidence interval (CI) using the Clopper–Pearson method.
Results
Socio-demographic and Disease Characteristics at Enrollment
In total, 3416 patients with MG were identified from the MGFAPR (Fig. 1), and 1751 completed the first follow-up survey 6 months later. Of the total population enrolled, 2092 (61.2%) were female (Table 1). The mean age at diagnosis (SD) was 49.4 (17.4) years. The highest proportion of patients (44.9%) were from the southern US. Overall, mean (SD) disease duration was 6.4 (9.5) years and was similar among the MG-ADL score groups. Of the 1189 patients with known antibody status (34.8%), 90.7% and 7.7% tested positive for acetylcholine receptor and muscle-specific tyrosine kinase autoantibodies, respectively. Over three-quarters of patients were treated with pyridostigmine, and 43.9% of patients were treated with prednisone (Table 1). Compared with the patients in the lower MG-ADL score groups (MG-ADL ≤ 7), a larger proportion of patients in the higher MG-ADL score groups (MG-ADL > 7) were female, younger at the time of MG diagnosis, Black, or unemployed (Table 1). A larger proportion of patients in the higher MG-ADL groups had depression, anxiety, chronic obstructive pulmonary disease, osteoporosis, asthma, autoimmune disorders (such as autoimmune thyroid disease, inflammatory bowel disease, systemic lupus erythematosus, and rheumatoid arthritis), and a lower income, and fewer patients in those groups had a university degree as their highest level of education, compared with patients in the lower MG-ADL groups (Table 1). In the higher MG-ADL groups, a greater proportion of patients had Medicaid insurance and fewer had commercial insurance compared with those in the lower MG-ADL groups. The largest proportion of uninsured patients was observed in the highest MG-ADL score group (7.4%; Table 1).
Fig. 1.
Flowchart of patient selection. MG myasthenia gravis, MG-ADL Myasthenia Gravis Activities of Daily Living, MGFAPR Myasthenia Gravis Foundation of America Global Myasthenia Gravis Patient Registry
Table 1.
Socio-demographic and disease characteristics by MG-ADL score group at enrollment
| Overall population (N = 3416) | MG-ADL score 0–1 (n = 358) |
MG-ADL score 2–4 (n = 783) |
MG-ADL score 5–7 (n = 979) |
MG-ADL score 8–10 (n = 734) |
MG-ADL score 11–13 (n = 400) |
MG-ADL score 14+ (n = 162) |
p valuea | |
|---|---|---|---|---|---|---|---|---|
| Age at diagnosis, years | < 0.001 | |||||||
| Mean (SD) | 49.4 (17.4) | 50.8 (18.9) | 51.8 (18.2) | 50.2 (17.4) | 47.6 (16.6) | 46.2 (15.3) | 45.8 (15.8) | |
| Median (IQR) | 52.0 (37.0, 63.0) | 56.0 (37.0, 66.0) | 57.0 (38.0, 67.0) | 53.0 (38.0, 64.0) | 49.0 (36.0, 61.0) | 47.0 (36.0, 57.0) | 48.5 (35.0, 56.0) | |
| Sex, n (%) | < 0.001 | |||||||
| Female | 2092 (61.2) | 181 (50.6) | 398 (50.8) | 571 (58.3) | 499 (68.0) | 316 (79.0) | 127 (78.4) | |
| Male | 1324 (38.8) | 177 (49.4) | 385 (49.2) | 408 (41.7) | 235 (32.0) | 84 (21.0) | 35 (21.6) | |
| Race, n (%) | 0.013 | |||||||
| White | 2922 (85.5) | 324 (90.5) | 677 (86.5) | 842 (86.0) | 619 (84.3) | 332 (83.0) | 128 (79.0) | |
| Black | 168 (4.9) | 16 (4.5) | 37 (4.7) | 49 (5.0) | 34 (4.6) | 21 (5.3) | 11 (6.8) | |
| Asian | 40 (1.2) | 6 (1.7) | 12 (1.5) | 12 (1.2) | 8 (1.1) | 1 (0.3) | 1 (0.6) | |
| Indigenous | 9 (0.3) | 0 (0) | 1 (0.1) | 4 (0.4) | 3 (0.4) | 1 (0.3) | 0 (0) | |
| Other | 277 (8.1) | 12 (3.4) | 56 (7.2) | 72 (7.4) | 70 (9.5) | 45 (11.3) | 22 (13.6) | |
| Region, n (%) | 0.024 | |||||||
| South | 1533 (44.9) | 151 (42.2) | 314 (40.1) | 442 (45.1) | 346 (47.1) | 204 (51.0) | 76 (46.9) | |
| Midwest | 634 (18.6) | 65 (18.2) | 140 (17.9) | 186 (19.0) | 140 (19.1) | 74 (18.5) | 29 (17.9) | |
| West | 635 (18.6) | 71 (19.8) | 153 (19.5) | 181 (18.5) | 129 (17.6) | 69 (17.3) | 32 (19.8) | |
| Northeast | 604 (17.7) | 70 (19.6) | 174 (22.2) | 168 (17.2) | 118 (16.1) | 51 (12.8) | 23 (14.2) | |
| Unincorporated territory of USA | 8 (0.2) | 1 (0.3) | 1 (0.1) | 2 (0.2) | 1 (0.1) | 1 (0.3) | 2 (1.2) | |
| Unknown or missing | 2 (< 0.1) | 0 (0) | 1 (0.1) | 0 (0) | 0 (0) | 1 (0.3) | 0 (0) | |
| Income, n (%) | < 0.001 | |||||||
| Less than $15,000 | 263 (7.7) | 8 (2.2) | 28 (3.6) | 62 (6.3) | 84 (11.4) | 57 (14.3) | 24 (14.8) | |
| $15,000–$50,000 | 989 (29.0) | 82 (22.9) | 211 (26.9) | 282 (28.8) | 217 (29.6) | 141 (35.3) | 56 (34.6) | |
| $50,000–$100,000 | 1049 (30.7) | 114 (31.8) | 250 (31.9) | 313 (32.0) | 217 (29.6) | 113 (28.3) | 42 (25.9) | |
| $100,000 or over | 769 (22.5) | 110 (30.7) | 209 (26.7) | 229 (23.4) | 138 (18.8) | 62 (15.5) | 21 (13.0) | |
| I do not wish to answer | 312 (9.1) | 41 (11.5) | 75 (9.6) | 84 (8.6) | 73 (9.9) | 22 (5.5) | 17 (10.5) | |
| Unknown or missing | 34 (1.0) | 3 (0.8) | 10 (1.3) | 9 (0.9) | 5 (0.7) | 5 (1.3) | 2 (1.2) | |
| Highest level of education, n (%) | < 0.001 | |||||||
| Less than high school | 28 (0.8) | 1 (0.3) | 6 (0.8) | 8 (0.8) | 8 (1.1) | 3 (0.8) | 2 (1.2) | |
| High school/GED | 808 (23.7) | 56 (15.6) | 167 (21.3) | 239 (24.4) | 189 (25.7) | 117 (29.3) | 40 (24.7) | |
| Associate’s | 541 (15.8) | 43 (12.0) | 106 (13.5) | 154 (15.7) | 118 (16.1) | 86 (21.5) | 34 (21.0) | |
| Technical | 219 (6.4) | 18 (5.0) | 38 (4.9) | 63 (6.4) | 51 (6.9) | 33 (8.3) | 16 (9.9) | |
| Bachelor’s | 934 (27.3) | 112 (31.3) | 228 (29.1) | 272 (27.8) | 196 (26.7) | 90 (22.5) | 36 (22.2) | |
| Postgraduate | 860 (25.2) | 121 (33.8) | 229 (29.2) | 239 (24.4) | 168 (22.9) | 70 (17.5) | 33 (20.4) | |
| Unknown or missing | 26 (0.8) | 7 (2.0) | 9 (1.1) | 4 (0.4) | 4 (0.5) | 1 (0.3) | 1 (0.6) | |
| Employment status, n (%) | < 0.001 | |||||||
| Full-time | 1150 (33.7) | 130 (36.3) | 286 (36.5) | 387 (39.5) | 220 (30.0) | 98 (24.5) | 29 (17.9) | |
| Part-time | 337 (9.9) | 55 (15.4) | 71 (9.1) | 97 (9.9) | 75 (10.2) | 25 (6.3) | 14 (8.6) | |
| Unemployed | 1911 (55.9) | 167 (46.6) | 422 (53.9) | 491 (50.2) | 436 (59.4) | 277 (69.3) | 118 (72.8) | |
| Unknown or missing | 18 (0.5) | 6 (1.7) | 4 (0.5) | 4 (0.4) | 3 (0.4) | 0 (0) | 1 (0.6) | |
| Insurance type, n (%) | < 0.001 | |||||||
| Commercial | 1568 (45.9) | 171 (47.8) | 366 (46.7) | 467 (47.7) | 332 (45.2) | 172 (43.0) | 60 (37.0) | |
| Medicare | 1229 (36.0) | 145 (40.5) | 324 (41.4) | 351 (35.9) | 242 (33.0) | 118 (29.5) | 49 (30.2) | |
| Medicaid | 145 (4.2) | 6 (1.7) | 16 (2.0) | 30 (3.1) | 44 (6.0) | 35 (8.8) | 14 (8.6) | |
| Tri-care/DoD/CF | 109 (3.2) | 8 (2.2) | 17 (2.2) | 29 (3.0) | 30 (4.1) | 20 (5.0) | 5 (3.1) | |
| Medicare and Medicaid | 96 (2.8) | 6 (1.7) | 12 (1.5) | 29 (3.0) | 17 (2.3) | 22 (5.5) | 10 (6.2) | |
| No insurance | 81 (2.4) | 3 (0.8) | 8 (1.0) | 23 (2.3) | 24 (3.3) | 11 (2.8) | 12 (7.4) | |
| Unknown or missing | 188 (5.5) | 19 (5.3) | 40 (5.1) | 50 (5.1) | 45 (6.1) | 22 (5.5) | 12 (7.4) | |
| Treatment of MG, n (%) | ||||||||
| Pyridostigmine | 2594 (75.9) | 196 (54.7) | 566 (72.3) | 751 (76.7) | 603 (82.2) | 338 (84.5) | 140 (86.4) | < 0.001 |
| Prednisone | 1500 (43.9) | 143 (39.9) | 332 (42.4) | 446 (45.6) | 328 (44.7) | 171 (42.8) | 80 (49.4) | 0.5 |
| Azathioprine | 496 (14.5) | 55 (15.4) | 116 (14.8) | 141 (14.4) | 103 (14.0) | 54 (13.5) | 27 (16.7) | 0.95 |
| Mycophenolate | 754 (22.1) | 99 (27.7) | 162 (20.7) | 203 (20.7) | 159 (21.7) | 92 (23.0) | 39 (24.1) | 0.12 |
| Myfortic | 9 (0.3) | 2 (0.6) | 3 (0.4) | 0 (0) | 0 (0) | 1 (0.3) | 3 (1.9) | 0.007 |
| Cyclosporine | 29 (0.8) | 4 (1.1) | 3 (0.4) | 7 (0.7) | 8 (1.1) | 4 (1.0) | 3 (1.9) | 0.6 |
| Tacrolimus | 20 (0.6) | 2 (0.6) | 2 (0.3) | 6 (0.6) | 6 (0.8) | 3 (0.8) | 1 (0.6) | 0.94 |
| Rituximab | 68 (2.0) | 4 (1.1) | 10 (1.3) | 15 (1.5) | 19 (2.6) | 15 (3.8) | 5 (3.1) | 0.027 |
| Eculizumab | 27 (0.8) | 1 (0.3) | 7 (0.9) | 7 (0.7) | 5 (0.7) | 4 (1.0) | 3 (1.9) | 0.024 |
| Cyclophosphamide | 4 (0.1) | 0 (0) | 1 (0.1) | 1 (0.1) | 2 (0.3) | 0 (0) | 0 (0) | 0.8 |
| IVIg or SCIg | 569 (16.7) | 27 (7.5) | 83 (10.6) | 164 (16.8) | 148 (20.2) | 96 (24.0) | 51 (31.5) | < 0.001 |
| PLEX | 135 (4.0) | 2 (0.6) | 16 (2.0) | 33 (3.4) | 39 (5.3) | 30 (7.5) | 15 (9.3) | < 0.001 |
| Disease duration, years | < 0.001 | |||||||
| Mean (SD) | 6.4 (9.5) | 8.9 (11.5) | 6.6 (9.6) | 5.5 (8.3) | 6.5 (10.2) | 6.0 (8.7) | 5.8 (8.6) | |
| Median (IQR) | 2.3 (0.9, 7.6) | 4.5 (1.5, 10.7) | 2.5 (0.9, 7.8) | 2.0 (0.8, 6.5) | 2.0 (0.8, 7.9) | 2.0 (0.8, 7.3) | 2.1 (0.8, 6.7) | |
| Comorbidities, n (%) | ||||||||
| High blood pressure | 1446 (42.3) | 147 (41.1) | 316 (40.4) | 418 (42.7) | 328 (44.7) | 170 (42.5) | 67 (41.4) | 0.8 |
| High cholesterol | 1304 (38.2) | 132 (36.9) | 299 (38.2) | 375 (38.3) | 290 (39.5) | 153 (38.3) | 55 (34.0) | 0.97 |
| Depression | 1116 (32.7) | 54 (15.1) | 161 (20.6) | 308 (31.5) | 298 (40.6) | 216 (54.0) | 79 (48.8) | < 0.001 |
| Anxiety | 1096 (32.1) | 52 (14.5) | 177 (22.6) | 300 (30.6) | 287 (39.1) | 200 (50.0) | 80 (49.4) | < 0.001 |
| Asthma | 660 (19.3) | 40 (11.2) | 89 (11.4) | 180 (18.4) | 175 (23.8) | 120 (30.0) | 56 (34.6) | < 0.001 |
| Diabetes mellitus types 1 and 2 | 634 (18.6) | 61 (17.0) | 132 (16.9) | 186 (19.0) | 140 (19.1) | 79 (19.8) | 36 (22.2) | 0.6 |
| Autoimmune thyroid disease | 461 (13.5) | 32 (8.9) | 73 (9.3) | 134 (13.7) | 117 (15.9) | 74 (18.5) | 31 (19.1) | < 0.001 |
| Osteoporosis | 400 (11.7) | 31 (8.7) | 68 (8.7) | 107 (10.9) | 97 (13.2) | 69 (17.3) | 28 (17.3) | < 0.001 |
| Inflammatory bowel disease | 271 (7.9) | 11 (3.1) | 36 (4.6) | 69 (7.0) | 60 (8.2) | 67 (16.8) | 28 (17.3) | < 0.001 |
| Rheumatoid arthritis | 206 (6.0) | 13 (3.6) | 40 (5.1) | 43 (4.4) | 50 (6.8) | 39 (9.8) | 21 (13.0) | < 0.001 |
| COPD | 194 (5.7) | 12 (3.4) | 21 (2.7) | 54 (5.5) | 43 (5.9) | 42 (10.5) | 22 (13.6) | < 0.001 |
| Psoriasis | 179 (5.2) | 10 (2.8) | 33 (4.2) | 58 (5.9) | 36 (4.9) | 29 (7.3) | 13 (8.0) | 0.11 |
| Lupus (SLE) | 73 (2.1) | 0 (0) | 9 (1.1) | 13 (1.3) | 19 (2.6) | 19 (4.8) | 13 (8.0) | < 0.001 |
| Multiple sclerosis | 23 (0.7) | 0 (0) | 3 (0.4) | 7 (0.7) | 5 (0.7) | 7 (1.8) | 1 (0.6) | 0.11 |
| Number of rescue treatmentsb | < 0.001 | |||||||
| 0 | 2286 (66.9) | 279 (77.9) | 567 (72.4) | 651 (66.5) | 470 (64.0) | 240 (60.0) | 79 (48.8) | |
| 1 | 677 (19.8) | 29 (8.1) | 99 (12.6) | 187 (19.1) | 182 (24.8) | 120 (30.0) | 60 (37.0) | |
| 2 | 14 (0.4) | 0 (0) | 0 (0) | 5 (0.5) | 3 (0.4) | 3 (0.8) | 3 (1.9) | |
| Unknown or missing | 439 (12.9) | 50 (14.0) | 117 (14.9) | 136 (13.9) | 79 (10.8) | 37 (9.3) | 20 (12.3) | |
| Number of exacerbations (one or more in the past 6 months)c | < 0.001 | |||||||
| Mean (SD) | 2.0 (1.3) | 1.3 (0.8) | 1.7 (1.2) | 1.7 (1.1) | 2.2 (1.3) | 2.4 (1.5) | 2.7 (1.6) | |
| Median (IQR) | 1.0 (1.0, 3.0) | 1.0 (1.0, 1.0) | 1.0 (1.0, 2.0) | 1.0 (1.0, 2.0) | 2.0 (1.0, 3.0) | 2.0 (1.0, 3.0) | 3.0 (1.0, 4.5) | |
CF Canadian Forces, COPD chronic obstructive pulmonary disease, DoD Department of Defense, GED General Educational Development, IQR interquartile range, IVIg intravenous immunoglobulin, MG-ADL Myasthenia Gravis Activities of Daily Living, N number of patients analyzed, PLEX plasma exchange, SCIg subcutaneous immunoglobulin, SD standard deviation, SLE systemic lupus erythematosus
aSignificant p values are indicated in bold
bRepresents whether patients answered the question of whether they were currently taking IVIg/SCIg or PLEX with “none,” “1,” or “2” of these
cN = 1540 patients analyzed; includes patients who had one or more exacerbations in the past 6 months
Relationship Between MG-ADL Score and MG Exacerbation Risk
At enrollment, the median MG-ADL score was 6.0 (mean [SD] 6.5 [3.9]), and 45.2% (n = 1543) of patients had experienced an exacerbation within the past 6 months. The proportion of patients who had an MG exacerbation in the 6 months prior to enrollment by MG-ADL score group is presented in Fig. 2. Overall, the proportion of patients who had an MG exacerbation was greater in the higher MG-ADL score groups (MG-ADL > 7; 62.6% of 1296 patients across the higher MG-ADL score groups) compared with patients in the lower MG-ADL score groups (MG-ADL ≤ 7; 34.5% of 2120 patients across the lower MG-ADL score groups), and 78.4% (n = 127) of patients in the MG-ADL 14+ score group had an exacerbation in the past 6 months (Fig. 2). There was also increased overnight hospitalization and treatment use following an exacerbation in the higher MG-ADL score groups compared with patients in the lower MG-ADL score groups (Fig. 2). In addition, a greater proportion of patients in the higher MG-ADL score groups had ever visited an ICU compared with patients in the lower MG-ADL score groups. The overall mean (SD) number of exacerbations within the past 6 months in those patients who had experienced at least one exacerbation was 2.0 (1.3), and the mean number of exacerbations was higher in patients in the higher MG-ADL score groups (Table 1).
Fig. 2.
Heat map of MG treatment, HCRU, and MG exacerbations at enrollment by MG-ADL score group. % denotes the percentage of patients in the respective MG-ADL score group. ap < 0.001 for differences between MG-ADL score groups. bApplicable for prednisone, NSISTs, and pyridostigmine only. cp = 0.017 for differences between MG-ADL score groups. HCRU healthcare resource utilization, ICU intensive care unit, IVIg intravenous immunoglobulin, MG myasthenia gravis, MG-ADL Myasthenia Gravis Activities of Daily Living, NSIST non-steroidal immunosuppressive therapy, PLEX plasma exchange
In the binomial regression model with MG-ADL score as a count variable, a statistically significant, positive association was observed between exacerbation within the 6 months prior to enrollment and MG-ADL score at enrollment (adjusted incidence rate ratio [IRR]: 1.13; 95% CI 1.11–1.15; p < 0.001) (Table 2), equating to a 13% increase in the number of exacerbations for each additional point on the MG-ADL score. A similar result was observed when MG-ADL score was used as a categorical variable. Compared with MG-ADL score group 0–1, the IRR for exacerbation increased with higher MG-ADL scores: 2.00 (MG-ADL 2–4 score group), 2.92 (5–7), 4.34 (8–10), 5.59 (11–13), and 7.22 (14+) (p < 0.001; Table 2).
Table 2.
Risk of exacerbation by socio-demographics and disease characteristics at enrollment
| Unadjusted | Adjusted | |||||
|---|---|---|---|---|---|---|
| IRR | 95% CI | p valueb | IRR | 95% CI | p valueb | |
| MG-ADL as a categorical variable (vs 0–1 group) | ||||||
| 2–4 | 2.04 | 1.57–2.67 | < 0.001 | 2.00 | 1.54–2.62 | < 0.001 |
| 5–7 | 3.20 | 2.50–4.15 | < 0.001 | 2.92 | 2.27–3.79 | < 0.001 |
| 8–10 | 4.98 | 3.88–6.46 | < 0.001 | 4.34 | 3.35–5.67 | < 0.001 |
| 11–13 | 6.85 | 5.29–8.96 | < 0.001 | 5.59 | 4.27–7.40 | < 0.001 |
| 14+ | 9.09 | 6.82–12.20 | < 0.001 | 7.22 | 5.35–9.82 | < 0.001 |
| MG-ADL as a count variable | ||||||
| MG-ADL score | 1.15 | 1.14–1.17 | < 0.001 | 1.13 | 1.11–1.15 | < 0.001 |
| Body weight | 1.00 | 1.00–1.00 | 0.4 | 1.00 | 1.00–1.00 | 0.9 |
| Age at diagnosis | 0.99 | 0.99–0.99 | < 0.001 | 1.00 | 0.99–1.00 | 0.075 |
| Sex (male vs female) | 0.72 | 0.65–0.81 | < 0.001 | 1.04 | 0.92–1.18 | 0.5 |
| Race (vs White) | ||||||
| Black | 1.07 | 0.84–1.36 | 0.6 | 0.96 | 0.76–1.21 | 0.7 |
| Asian | 0.86 | 0.51–1.44 | 0.6 | 1.09 | 0.68–1.74 | 0.7 |
| Indigenous | 0.45 | 0.10–1.65 | 0.3 | 0.40 | 0.09–1.26 | 0.2 |
| Other | 1.26 | 1.05–1.52 | 0.015 | 1.09 | 0.92–1.29 | 0.3 |
| Income (vs less than $15,000) | ||||||
| $15,000–$50,000 | 0.72 | 0.59–0.88 | 0.002 | 1.00 | 0.81–1.23 | > 0.9 |
| $50,000–$100,000 | 0.63 | 0.51–0.77 | < 0.001 | 0.93 | 0.74–1.16 | 0.5 |
| $100,000 or over | 0.60 | 0.48–0.74 | < 0.001 | 0.96 | 0.77–1.22 | 0.8 |
| I do not wish to answer | 0.63 | 0.49–0.81 | < 0.001 | 0.94 | 0.72–1.21 | 0.6 |
| Prednisone (yes vs no) | 1.27 | 1.14–1.41 | < 0.001 | 1.28 | 1.15–1.41 | < 0.001 |
| Pyridostigmine (yes vs no) | 1.27 | 1.11–1.44 | < 0.001 | 1.00 | 0.88–1.14 | > 0.9 |
| Thymectomy (yes vs no) | 1.05 | 0.93–1.18 | 0.4 | 0.93 | 0.81–1.07 | 0.3 |
| Thymic tumor (yes vs no) | 1.12 | 0.94–1.34 | 0.2 | 1.11 | 0.92–1.33 | 0.3 |
| High blood pressure (yes vs no) | 0.95 | 0.86–1.06 | 0.4 | 1.01 | 0.90–1.13 | 0.9 |
| High cholesterol (yes vs no) | 0.88 | 0.79–0.98 | 0.017 | 0.91 | 0.82–1.02 | 0.11 |
| Diabetes (yes vs no) | 0.85 | 0.74–0.98 | 0.024 | 0.81 | 0.70–0.93 | 0.002 |
| Other autoimmune disease (yes vs no) | 1.30 | 1.16–1.45 | < 0.001 | 1.06 | 0.95–1.19 | 0.3 |
| Respiratory disease (yes vs no) | 1.42 | 1.26–1.60 | < 0.001 | 1.09 | 0.97–1.22 | 0.2 |
| Osteoporosis (yes vs no) | 1.26 | 1.08–1.47 | 0.004 | 1.06 | 0.91–1.22 | 0.5 |
| Depression (yes vs no) | 1.46 | 1.31–1.63 | < 0.001 | 1.05 | 0.93–1.18 | 0.5 |
| Anxiety (yes vs no) | 1.44 | 1.29–1.61 | < 0.001 | 1.08 | 0.95–1.21 | 0.2 |
The output of the covariates included in the regression model was not presented when MG-ADL was used as a categorical variable
Unadjusted and adjusted negative binomial regression; variables with more than 5% missing data (antibody status, NSISTs, biologics, IVIg or SCIg or PLEX, time to diagnosis, and health insurance) were included in the model but were not presented or interpreted
CI confidence interval, IRR incidence rate ratio, IVIg intravenous immunoglobulin, MG-ADL Myasthenia Gravis Activities of Daily Living, NSIST non-steroidal immunosuppressive therapy, PLEX plasma exchange, SCIg subcutaneous immunoglobulin
bSignificant p values are indicated in bold
A statistically significant association was also found between MG exacerbation and use of prednisone (IRR 1.28; 95% CI 1.15–1.41; p < 0.001) compared with no use of prednisone. Conversely, having diabetes, compared with no diabetes, was associated with a statistically significantly lower risk of MG exacerbation (IRR 0.81; 95% CI 0.70–0.93; p = 0.002).
HCRU Following an MG Exacerbation
At enrollment, 778 (22.8%) patients had reported one exacerbation in the previous 6 months, and out of these, 49.6% of patients required treatment with IVIg, treatment with PLEX, or overnight hospitalization following their exacerbation (Fig. 3). Of the 1751 patients who completed the first follow-up survey at 6 months, 331 (18.9%) patients reported an exacerbation, of whom 54.1% required treatment or overnight hospitalization following their exacerbation.
Fig. 3.
HCRU following exacerbation among patients with one exacerbation. HCRU healthcare resource utilization, IVIg intravenous immunoglobulin, PLEX plasma exchange
Discussion
This study provides a unique insight into the socio-demographics of patients with MG, the disease characteristics, the exacerbation risk, and HCRU following an exacerbation, all stratified by MG-ADL score in a real-world MG population. To our knowledge, this is the first study in a real-world setting to confirm that greater MG severity is associated with increased exacerbation risk and levels of HCRU. We also observed significant differences in socio-demographics across MG-ADL score groups.
Almost half of our cohort had experienced an exacerbation in the 6 months prior to enrollment, which had a substantial impact on HCRU. A previous real-world study using IBM® MarketScan® data showed that exacerbation was significantly associated with high costs among patients with MG receiving second-line therapy [8]. Mean disease durations were similar between MG-ADL score groups, which suggests that some patients can have severe disease early on in their MG journey. Based on these data, prescribers may want to consider targeted treatments sooner rather than later for these patients, as such treatments can reduce MG severity by improving symptoms rapidly and, in turn, may prevent exacerbations and reduce HCRU.
We also observed a relationship between socio-demographic characteristics and MG disease severity at MGFAPR enrollment. Compared with patients in the lower MG-ADL score groups, a larger proportion of patients in the higher MG-ADL score groups were Black, unemployed, and uninsured, and fewer had a university degree as their highest level of education. Our results corroborate those of a large US database study that found significant racial differences in complications and hospitalizations related to MG, whereby Black patients were significantly more likely to experience adverse inpatient outcomes than patients of other races [20]. Furthermore, the debilitating nature of MG symptoms can affect a patient’s ability to work full-time or participate in more physical types of work and activities [21]. According to previous studies, up to 50% of patients with MG may have experienced unemployment [21, 22], which will inevitably have had a negative impact on their access to health insurance, particularly in the US, and their ability to pay for costs associated with treatment. In our study, the full-time employment rate was lower in patients in the higher MG-ADL score groups. The unemployment rate in the US for the general population was about 4% in December 2023 [23], while the unemployment rate in our study was around 50% in the lower MG-ADL score groups and increased in the higher MG-ADL score groups, suggesting the potential impact MG can have on employment status and income. These findings are supported by research into social determinants of health, defined as non-medical, non-biological, and non-genetic factors of the environment or individuals’ lives (such as age, housing environment, and employment) that can lead to suboptimal treatment and poor access to healthcare among individuals with MG [24, 25]. Thus, insurance and healthcare providers may need to take a personalized approach to patient management and identify patients at risk of exacerbations who require additional support.
In our study, a larger proportion of patients in higher MG-ADL score groups than in lower MG-ADL groups had comorbidities, including autoimmune diseases. In a recent study of 178 patients with MG, incidence of respiratory diseases had a positive association with higher MGFA disease classes [26]. This is in line with our study, whereby we observed a higher prevalence of asthma and chronic obstructive pulmonary disease in the higher MG-ADL groups. In another study, a positive correlation of mild cognitive impairment and depression with MG severity was found, which also correlates with the findings of our study [27]. Further, a study from Japan concluded that glucocorticoid-induced osteoporosis worsens quality of life in patients with MG, which may explain the larger proportion of patients with osteoporosis in the higher MG-ADL groups observed in this study [28]. It is known that long-term use of immunosuppressive therapies, including steroids, is associated with an increased risk of comorbidities such as diabetes [29]. Patients who are treated with immunosuppressive therapies may experience a lower rate of exacerbation due to continued treatment but still also might develop these comorbidities. Conversely, previous studies have shown that high doses of prednisone may be associated with a risk of MG exacerbation [30]. Though not definitive due to a lack of dosing and treatment duration data in this study, this may explain the observed correlation between higher prednisone use and an increased risk of MG exacerbation. Interestingly, diabetes, which may be an adverse event from steroid use, was also associated with a reduced risk of MG exacerbation. This may suggest some association of over- or undertreatment due to development of comorbidities and risk of exacerbation, and further research may be warranted. Together, these findings emphasize the need for considering the impact of existing comorbidities, and the potential risk of emerging comorbidities, in MG management plans.
MG symptoms and quality of life are consistently reported as worse among female patients than male patients [15, 31, 32]. In our study, there was a greater proportion of female patients in the higher MG-ADL score groups, supporting the literature data. Although there are some hypotheses as to the gender differences in MG, ranging from chromosomal inactivation [33] to hormonal fluctuations around menstruation and pregnancy [34, 35], the underlying mechanism explaining the greater severity in female compared with male patients is unknown. However, there is a general consensus that lifestyle factors and comorbidities should be considered when managing MG, especially for female patients [31, 32].
While the MGFAPR is the largest patient-reported database of patients with MG, and the overall results for the exploratory analyses at first follow-up were consistent and comparable to those at enrollment, increasing the internal validity, there are several limitations to this study: (1) selection bias owing to voluntary enrollment in the MGFAPR, which may have caused a larger proportion of females to be enrolled in the study in addition to the expected increase owing to the female predominance in MG; (2) the self-reported MG diagnosis that was not validated by a physician; and (3) the use of a 4-week recall period for the MG-ADL questionnaire, which differs from the usual 7-day recall period used in most MG clinical trials. Additionally, the cross-sectional study design does not allow a causal association to be established, and further study is needed to better understand the temporal or causal association between MG-ADL score, exacerbation, and downstream implications for HCRU.
Conclusion
The observed relationship between MG-ADL score and risk of exacerbations highlights the importance and utility of MG-ADL in assessing outcomes in routine clinical practice and informing treatment decisions. Furthermore, this study has identified that a large proportion of patients with MG in the US have high MG-ADL scores, resulting in higher myasthenic exacerbation, leading to increased HCRU. Optimization of the treatment regimen—including the consideration of faster-acting therapeutics and personalized approaches to management, especially for patients with comorbidities, and the socio-demographic groups at risk of increased disease severity—is necessary to reduce the overall disease burden and improve outcomes of care for patients with MG.
Acknowledgments
The authors thank the patients and their caregivers for their contributions to this study.
Medical Writing and Editorial Assistance
Medical writing support was provided by Rachel Price, PhD, and Nishtha Chandra, PhD, of Ogilvy Health UK, and funded by UCB, in accordance with Good Publication Practice 2022 guidelines (https://www.ismpp.org/gpp-2022). The authors thank Wendi Huff, a former employee of MGFA, for her work on the MGFAPR. The authors also thank David Harrison, DPhil, of UCB for publication and editorial support.
Author Contributions
Angela Ting, Minjee Park, Oshin Sangha, Mohita Kumar, Jean-François Ricci, and Edward Lee made substantial contributions to the study conception, study design, and data analysis. Minjee Park, Oshin Sangha, Mohita Kumar, Jean-François Ricci, and Richard J. Novak acquired and analyzed the data. All authors reviewed and revised the manuscript critically for intellectual content.
Funding
This study and the Rapid Service Fee were funded by UCB.
Data Availability
Data from non-interventional studies are outside of UCB’s data-sharing policy and unavailable for sharing.
Declarations
Conflict of Interest
Angela Ting is a former employee and shareholder of UCB. Mohita Kumar and Edward Lee are employees and shareholders of UCB. Minjee Park and Jean-François Ricci are employees of Alira Health. Oshin Sangha is a former employee of Alira Health and a member of the MGFA Registry Advisory Council. Richard J. Nowak reports research support from Alexion Pharmaceuticals, argenx, Annexon Biosciences, Genentech, Grifols, Immunovant, Momenta (now Johnson & Johnson), the MGFA, the National Institutes of Health, UCB, and Horizon Therapeutics (now Amgen). Richard J. Nowak has also served as a consultant and advisor for Alexion Pharmaceuticals, argenx, Cabaletta Bio, COUR Pharmaceuticals, CSL Behring, Grifols, Immunovant, Momenta (now Johnson & Johnson), UCB, and Horizon Therapeutics (now Amgen).
Ethical Approval
MGFAPR is a patient-driven registry funded and supervised by the MGFA and managed by Alira Health since June 2022. The MGFAPR is approved by the WCG Institutional Review Board (IRB tracking number: 20223601), and the database is Health Insurance Portability and Accountability Act compliant. This study followed the 2005 Guidelines for Good Pharmacoepidemiological Practice (GPP) and the 2015 International Society of Pharmacoepidemiology Guidelines for GPP. All patients provided informed, voluntary consent to enroll in the MGFAPR and for their data to be used in research studies.
Footnotes
Prior Presentation: The results from this manuscript have been previously presented at the Myasthenia Gravis Foundation of America Scientific Session at AANEM, Phoenix, AZ, USA, on November 1, 2023.
Angela Ting no longer works at UCB and Oshin Sangha no longer works at Alira Health.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data from non-interventional studies are outside of UCB’s data-sharing policy and unavailable for sharing.