Risk Factors for Generalization in Patients with Ocular Myasthenia Gravis: A Multicenter Retrospective Cohort Study

The Influence of Loneliness and Anxiety Levels on MG-Specific Quality of Life in Patients with Myasthenia Gravis during the COVID-19 Pandemic

BACKGROUND

Myasthenia gravis (MG) is one of the most common autoimmune diseases and can occur at any age. The study aimed to evaluate the influence of loneliness and anxiety levels on MG-specific quality of life in patients with myasthenia gravis during the COVID-19.

METHODS

This was a cross sectional study and the population consisted of patients with MG throughout Turkey between August 1 and October 31, 2021. The study was completed with 149 patients who met the inclusion criteria and agreed to participate in the research.

RESULTS

The mean MG-specific quality of life score was 44.15 ± 13.77, the mean UCLA loneliness scale was 56.13 ± 11.55, and the mean Beck Anxiety Inventory score was 24.63 ± 13.78. There was a moderately positive linear relationship between the participants' MG-specific quality of life (r = 0.589; p < 0.001) and the Beck Anxiety Inventory, and there was a weak negative linear relationship with the UCLA loneliness scale (r = 0.323; p < 0.001). The variables affecting the MG-specific quality of life were the following: anxiety, loneliness, employment status, income level, symptom levels, and having another chronic disease; a multiple regression model was created with these variables, and it was observed that anxiety and loneliness affected participants' MG-specific quality of life. The variables in the model explained 52.9% of the MG-specific quality of life, and the model was statistically significant (p < 0.001).

CONCLUSION

The study determined that the participants had intense loneliness, moderate anxiety, and a moderate MG-specific quality of life. The participants' MG-specific quality of life has decreased as their anxiety has increased, and the participants' MG-specific quality of life has increased as their loneliness has increased.

Pediatric Ocular Myasthenia Gravis: Single-Center Experience

BACKGROUND

Currently, there is no universally accepted standard treatment for ocular myasthenia gravis (OMG) in children. We aimed to investigate the possible proper regimens and timing of treatment for pediatric OMG cases based on the clinical manifestations: OMG with ptosis only and OMG with other features.

METHODS

One hundred and forty two OMG cases attended at the Department of Pediatrics, Xiangya Hospital, Central South University, from 2010 to 2019 were included, and information from medical records was reviewed and recorded. Comparisons of clinical characteristics between patients with OMG with ptosis only and patients with OMG with other features as well as between patients treated with glucocorticoid (GC) within or after six months from disease onset were performed.

RESULTS

OMG with other features constituted about 54.9% of the cases, and 66.2% of the patients achieved optimal outcome. Patients with OMG with ptosis only responded to pyridostigmine alone more than patients with OMG with other features who required several therapies (P < 0.001). Patients with OMG with ptosis only had a larger proportion of optimal outcome than the patients with OMG with other features (P = 0.002), and the difference remained significant even when the individual outcome groups were compared (P < 0.001). Patients who received GC within six months had a greater proportion of optimal outcome than those who received it after six months (P < 0.001).

CONCLUSIONS

Although OMG with other features is a more common subtype of OMG, it is also more severe than OMG with ptosis only. An earlier addition of GC leads to optimal outcome.

Safety of COVID-19 vaccine in patients with myasthenia gravis: a self-controlled case series study

Background

The safety of COVID-19 vaccines has been clarified in clinical trials; however, some immunocompromised patients, such as myasthenia gravis (MG) patients, are still hesitant to receive vaccines. Whether COVID-19 vaccination increases the risk of disease worsening in these patients remains unknown. This study aims to evaluate the risk of disease exacerbation in COVID-19-vaccinated MG patients.

Methods

The data in this study were collected from the MG database at Tangdu Hospital, the Fourth Military Medical University, and the Tertiary Referral Diagnostic Center at Huashan Hospital, Fudan University, from 1 April 2022 to 31 October 2022. A self-controlled case series method was applied, and the incidence rate ratios were calculated in the prespecified risk period using conditional Poisson regression.

Results

Inactivated COVID-19 vaccines did not increase the risk of disease exacerbation in MG patients with stable disease status. A few patients experienced transient disease worsening, but the symptoms were mild. It is noted that more attention should be paid to thymoma-related MG, especially within 1 week after COVID-19 vaccination.

Conclusion

COVID-19 vaccination has no long-term impact on MG relapse.

Short-term outcome prediction for myasthenia gravis: an explainable machine learning model

Background

Myasthenia gravis (MG) is an autoimmune disease characterized by muscle weakness and fatigability. The fluctuating nature of the disease course impedes the clinical management.

Objective

The purpose of the study was to establish and validate a machine learning (ML)-based model for predicting the short-term clinical outcome in MG patients with different antibody types.

Methods

We studied 890 MG patients who had regular follow-ups at 11 tertiary centers in China from 1 January 2015 to 31 July 2021 (653 patients for derivation and 237 for validation). The short-term outcome was the modified post-intervention status (PIS) at a 6-month visit. A two-step variable screening was used to determine the factors for model construction and 14 ML algorithms were used for model optimisation.

Results

The derivation cohort included 653 patients from Huashan hospital [age 44.24 (17.22) years, female 57.6%, generalized MG 73.5%], and the validation cohort included 237 patients from 10 independent centers [age 44.24 (17.22) years, female 55.0%, generalized MG 81.2%]. The ML model identified patients who were improved with an area under the receiver operating characteristic curve (AUC) of 0.91 [0.89-0.93], 'Unchanged' 0.89 [0.87-0.91], and 'Worse' 0.89 [0.85-0.92] in the derivation cohort, whereas identified patients who were improved with an AUC of 0.84 [0.79-0.89], 'Unchanged' 0.74 [0.67-0.82], and 'Worse' 0.79 [0.70-0.88] in the validation cohort. Both datasets presented a good calibration ability by fitting the expectation slopes. The model is finally explained by 25 simple predictors and transferred to a feasible web tool for an initial assessment.

Conclusion

The explainable, ML-based predictive model can aid in forecasting the short-term outcome for MG with good accuracy in clinical practice.

Sex differences in neuromuscular disorders

The prevalence, onset, pathophysiology, and clinical course of many neuromuscular disorders (NMDs) may significantly differ between males and females. Some NMDs are more frequently observed in females, and characterized to show a higher grade of severity during or after the pregnancy. Meanwhile, others tend to have an earlier onset in males and exhibit a more variable progression. Prevalently, sex differences in NMDs have a familiar character given from genetic inheritance. However, they may also influence clinical presentation and disease severity of acquired NMD forms, and are represented by both hormonal and genetic factors. Consequently, to shed light on the distinctive role of biological factors in the different clinical phenotypes, we summarize in this review the sex related differences and their distinctive biological roles emerging from the current literature in both acquired and inherited NMDs.

Knowledge mapping of global trends for myasthenia gravis development: A bibliometrics analysis

Background

Myasthenia gravis (MG) is an autoimmune disease with acquired neuromuscular junction transmission disorders. In the last two decades, various pathogenesis, application of immunosuppressive agents, and targeted immunotherapy have been significant events. However, extracting the most critical information from complex events is very difficult to guide clinical work. Therefore, we used bibliometrics to summarize and look forward.

Methods

Science Citation Index Expanded (SCI-E) from the Web of Science Core Collection (WoSCC) database was identified as a source of material for obtaining MG-related articles. Scimago Graphica, CiteSpace, VOSviewer, and bibliometrix were utilized for bibliometric analysis. Knowledge network graphs were constructed and visualized; countries, institutions, authors, journals, references, and keywords were evaluated. In addition, GraphPad Prism and Microsoft Excel 365 were applied for statistical analysis.

Results

As of October 25, 2022, 9,970 original MG-related articles were used for the bibliometric analysis; the cumulative number of citations to these articles was 236,987, with an H-index of 201. The United States ranked first in terms of the number of publications (2,877) and H-index (134). Oxford has the highest H-index (67), and Udice French Research University has the highest number of publications (319). The author with the highest average number of citations (66.19), publications (151), and H-index (53) was Vincent A. 28 articles have remained in an explosive period of citations. The final screening yielded predictive keywords related to clinical trials and COVID-19.

Conclusion

We conducted a bibliometric analysis of 9,970 original MG-related articles published between 1966 and 2022. Ultimately, we found that future MG research hotspots include two major parts: (1) studies directly related to MG disease itself: clinical trials of various targeted biological agents; the relationship between biomarkers and therapeutic decisions, pathogenesis and outcome events, ultimately serving individualized management or precision therapy; (2) studies related to MG and COVID-19: different variants of COVID-19 (e.g., Omicron) on MG adverse outcome events; assessment of the safety of different COVID-19 vaccines for different subtypes of MG.

Development and Validation of a Nomogram for Predicting Generalization in Patients With Ocular Myasthenia Gravis

Background

This study aims to develop and validate a nomogram for predicting 1- and 2-year generalization probabilities in patients with ocular myasthenia gravis (OMG).

Methods

In total, 501 eligible patients with OMG treated at seven tertiary hospitals in China between January 2015 and May 2019 were included. The primary outcome measure was disease generalization. A nomogram for predicting 1- and 2-year generalization probabilities was constructed using a stepwise Cox regression model. Nomogram performance was quantified using C-indexes and calibration curves. Two-year cumulative generalization rates were analyzed using the Kaplan−Meier method for distinct nomogram-stratified risk groups. The clinical usefulness of the nomogram was evaluated using decision curve analysis (DCA).

Result

The eligible patients were randomly divided into a development cohort (n=351, 70%) and a validation cohort (n=150, 30%). The final model included five variables: sex, onset age, repetitive nerve stimulation findings, acetylcholine receptor antibody test results, and thymic status. The model demonstrated good discrimination (C-indexes of 0.733 and 0.788 in the development and validation cohorts, respectively) and calibration, with good agreement between actual and nomogram-estimated generalization probabilities. Kaplan−Meier curves revealed higher 2-year cumulative generalization rates in the high-risk group than that in the low-risk group. DCA demonstrated a higher net benefit of nomogram-assisted decisions compared to treatment of all patients or none.

Conclusion

The nomogram model can predict 1- and 2-year generalization probabilities in patients with OMG and stratified these patients into distinct generalization risk groups. The nomogram has potential to aid neurologists in selecting suitable patients for initiating immunotherapy and for enrolment in clinical trials of risk-modifying treatments.

Association of immunosuppression treatment with generalization among patients with ocular myasthenia gravis: a propensity score analysis

BACKGROUND

To analyze disease generalization in patients with ocular myasthenia gravis (OMG) treated with immunosuppression compared with patients without immunosuppression treatment.

METHODS

In this retrospective cohort study, we analyzed data from patients with OMG at seven medical centers in China from January 1, 2015 to May 1, 2019 and compared disease generalization in patients (treated with immunosuppression vs. not treated) within 2 years of disease onset using raw and inverse probability of treatment weighting (IPTW) analyses.

RESULTS

In the study population of 813 patients with OMG, 425 (52.3%) with immunosuppression had a mean (SD) onset age of 50.0 (15.1) years, and 188 (44.2%) were women. The remaining 388 (47.7%) patients were not immunosuppressed (mean age, 48.4 [15.0] years; 185 [47.7%] women). Disease generalization developed in 122 (31.4%) and 37 (8.7%) patients in the non-immunosuppression and immunosuppression groups, respectively. Relative to non-immunosuppression, immunosuppression was associated with a lower risk of generalization in a multivariable-adjusted Cox model (hazard ratio [HR], 0.27; 95% confidence interval [CI], 0.18-0.40; P<0.001) and IPTW-weighted Cox model (HR, 0.28; 95% CI, 0.19-0.42; P<0.001). In sensitivity analyses, longer duration of immunosuppression was associated with a lower risk of generalization (HR, 0.90 for every one-month increase; 95% CI, 0.87-0.92; P<0.001; IPTW-adjusted). Combination therapy with steroids and non-steroidal immunosuppressants showed superior efficacy in reducing the risk of generalization (HR, 0.14; 95% CI, 0.07-0.26; P<0.001).

CONCLUSIONS

Immunosuppression significantly reduced the 2-year risk of generalization in patients with OMG.