Near Fiber Electromyography in the Diagnosis of Myasthenia Gravis
NFEMG in MG
DOI:
https://doi.org/10.17161/rrnmf.v6i1.21795Keywords:
Near Fiber EMG, Quantitative Electromyography, Neuromuscular Junction Instability, Jitter Estimation, Single Fiber EMG, Motor Unit Instability, Myasthenia GravisAbstract
Background:
Near fiber EMG (NFEMG) focuses on activity of muscle fibers close to the electrode offers the ability to semi-automatically assess neuromuscular junction instability in a manner conceptually similar to single fiber EMG (SFEMG). The objective of this retrospective study was to compare the accuracy of NFEMG with SFEMG in diagnosing myasthenia gravis (MG).
Methods:
NFEMG was blindly applied to recordings from 50 patients SFEMG-tested at BIDMC in the prior 18 months, 12 of whom were diagnosed as having MG. Excluding the myopathic and neurogenic patients, NFEMG and SFEMG results were compared to the final clinical diagnosis using cross-validation that involved 10 randomly selected training sets and their corresponding testing sets.
Results:
In patients free of myopathy or neuropathy, NFEMG sensitivity was 100% while specificity ranged from 89% to 95% (mean of 90%). When testing on the entire cohort of patients free of other neuromuscular conditions, NFEMG sensitivity and specificity were 100% and 94%, respectively, while SFEMG sensitivity and specificity were 94% and 97%, respectively.
Conclusion:
NFEMG performs well in diagnosing MG, but prospective studies are needed. Patients with weakness for whom the differential diagnosis includes seronegative MG is substantial; an efficient way to address this is important. The ease of application and minimal training requirement suggest NFEMG could be an efficient screen prior to SFEMG referral or an alternative diagnostic test when SFEMG is unavailable, potentially addressing a significant healthcare disparity for the large population of patients with weakness that may be caused by seronegative MG.
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Copyright (c) 2025 Ross Mandeville, Adam Patterson, Justin Luk, Art Eleanore, Oscar Garnés‐Camarena, Dan Stashuk
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