Effect of Gender, Disease Duration and Treatment on Muscle Strength in Myasthenia Gravis

[Myasthenia gravis-Gender aspects and family planning]

BACKGROUND

There is evidence that gender-specific differences can influence the diagnostics, treatment and long-term disease course of myasthenia gravis (MG). In women the diagnosis is often made during childbearing age.

OBJECTIVE

Gender-specific differences in MG and relevant aspects in routine clinical practice are presented. In addition, current studies on family planning, pregnancy and childbirth in MG are highlighted and treatment recommendations are derived.

MATERIAL AND METHODS

Narrative literature review.

RESULTS

In addition to sociodemographic data, gender-specific differences encompass clinical as well as paraclinical factors, such as disease severity and antibody status. With few exceptions pregnancy is possible with good maternal and neonatal outcome. During pregnancy and peripartum, children of MG patients should be closely monitored for early detection and treatment of potential syndromes caused by diaplacental transfer of maternal antibodies.

CONCLUSION

Gender-specific factors can influence the course of MG. Adequate medical counselling and multidisciplinary collaboration are essential for MG patients who wish to have children.

Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Neuromuscular fatigue in autoimmune myasthenia gravis: A cross-sectional study

OBJECTIVES

To investigate the presence of increased neuromuscular fatigue (NMF) in individuals with myasthenia gravis (IwMG), compared to healthy controls. A secondary aim was to assess associations between NMF, strength and perceived health-related quality of life (HRQoL) and symptom severity in IwMG.

METHODS

In this cross-sectional study, we assessed NMF using classical myoelectrical indicators (root mean square: RMS, mean power frequency: MPF) obtained from surface electromyography (sEMG) during a sustained submaximal isometric contraction of the right Biceps Brachii and the right Vastus Lateralis and by evaluating the post-effort decline in peak torque following a fatiguing task consisting of a 40-second sustained isometric contraction. Relationships with MG-specific clinical scores (Myasthenia Muscle Score for symptom severity, MGQOL-15-F for HRQoL) were investigated.

RESULTS

Forty-one females with MG were compared to 18 control females of similar age. IwMG demonstrated reduced strength in both muscle groups, compared to control subjects. In both populations and both limbs, NMF was demonstrated by an increase in RMS and a decrease in MPF. However, IwMG did not demonstrate greater NMF based on these myoelectrical indicators nor based on post-effort peak torque decline.

DISCUSSION

Despite a decrease in baseline strength, IwMG did not display greater NMF in this specific experimental paradigm. This cohort consisted of individuals with mild-to-moderately severe MG which was well-controlled and stable. Further studies are warranted to identify simple and reliable methods to measure NMF in MG and to understand the relationship between NMF and perceived fatigue in activities of daily living for IwMG.

Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach

Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.

The Influence of Gender, Age, and Body Mass Index on Arch Height and Arch Stiffness

Arch structure and arch function play an important role in maintaining balance, bearing body weight, and absorbing ground reaction forces. However, arch structure and arch function are known to vary extensively and may be affected by factors such as gender, age, and obesity. Therefore, the purpose of this study was to examine the influence of gender, age, and body mass index (BMI) on arch height and arch stiffness. A total of 173 participants (aged 57.60 ± 11.19 years, mean BMI 25.12 ± 3.93 kg/m²) participated in this cross-sectional study. A 3-dimensional laser scanner was used to measure foot structure information in each subject, from which arch height and arch stiffness were calculated. The results showed that women had low-arched feet compared with men (p = .001), and no arch stiffness difference was found. Older individuals tended to have a stiffer arch than middle-aged and younger individuals (p < .05), and no arch height difference was found. BMI had an impact on arch height (p < .05) but not arch stiffness. Finally, a weak positive relationship existed between arch height and arch stiffness (r = 0.32, p < .01). The findings suggest that gender, age, and obesity have a certain impact on arch structure and arch stiffness. Figuring out the relationship between these factors and arch structure may be helpful in understanding the bases of foot deformity and foot dysfunction.

Upper extremity strength: Normative reference data among uninjured employees

BACKGROUND

Extremity strength testing is used to determine the ability of industrial employees to perform a physically demanding occupation safely, evaluate post-injury impairment, and monitor return to function after injury. There is an unmet clinical need for a robust and granular source of normative reference values to serve as a resource for baseline data on upper extremity isometric strength.

OBJECTIVE

Develop normative reference data for upper extremity strength among industrial employees and investigate associations between strength and physical job demands.

METHODS

Upper extremity strength data from 107,102 industrial employees were collected post-hire. In this study, isometric tests for pinch; hand grip; wrist pronation and supination; and flexion of the wrist, elbow, and shoulder strength were retrospectively analyzed in relationship to sex, age, and job level. Associations between strength scores and five levels of work, defined the United States Department of Labor's Dictionary of Occupational Titles by level of physical difficulty (1-5), were determined.

RESULTS

Higher strength scores were positively associated with more physically demanding job levels (P < 0.001), and there was a progressive increase in strength scores with increased physical job demands for both sexes (P = 0.0002). All strength scores differed significantly by decade of age (P < 0.001). All scores except for pinch strength demonstrated a moderate or high positive correlation with job level (r≥0.50).

CONCLUSIONS

The normative reference upper extremity strength data collected in this study for industrial employees may be useful for evaluating rehabilitation and recovery following injury or illness. In order to utilize normalized strength data as a post-injury reference, it is important to consider job level in addition to age and sex, as these variables are highly correlated with baseline upper extremity strength.

Expiratory muscle strength as a predictor of functional exercise capacity in generalized myasthenia gravis

Objectives:

To investigate the correlations between the 6-minute walk test and disease severity, pulmonary functions, and respiratory muscle performance in patients with generalized myasthenia gravis (MG) and to determine whether MG disease severity, pulmonary functions, and respiratory muscle performance contribute to 6-minute walk test distance in generalized MG.

Methods:

This cross-sectional trial was conducted at Hacettepe University in Ankara, Turkey. The study was carried out from February to August 2017. Twenty-eight class II-III MG patients participated in

the study. Patients’ disease severity was determined with the Myasthenia gravis composite scale. All participants underwent the 6-minute walk test, pulmonary function tests, and respiratory muscle strength and endurance assessment.

Results:

Approximately 40% of patients’ expiratory muscle strength were under the lower limit of normal. Multiple linear regression analysis revealed that the percentage of predicted expiratory muscle strength that patients reached were significant and independent predictor of percentage of 6-minute walk test distance that patients reached according to reference values (R2=0.493, F [1-27]=25.275, p<0.001).

Conclusion:

Expiratory muscle strength is a significant determinant of functional exercise performance in generalized MG with mild or moderate weakness affecting muscles other than the ocular muscles.