Objective: We aimed to evaluate the clinical characteristics of patients with myoclonus and to identify the possible etiologies in different types of myoclonus based on electrophysiological findings.

Background: In a case with myoclonus, the diagnosis is challenging because differential diagnosis list is long. Myoclonus is classified according to the origin, time course, triggering factors or other clinical features. One classification scheme involves clinical features and groups myoclonus into essential, physiologic and epileptic subtypes. However, the terms, essential and epileptic, are confusing. In our opinion, considering to find the definite diagnosis, the most useful classification is the one which groups myoclonus according to the site of origin, i.e cortical, subcortical (basal ganglia or brain stem), spinal cord and peripheral nerves.

Method: We performed a retrospective analysis of all electrophysiological recordings between 2005 and 2018, in which myoclonus was confirmed. We analyzed clinical data including age at examination, gender, age at onset, and main neurological or systemic findings, underlying etiology and electrophysiological features including polymyogram analysis, long-loop reflexes, electroencephalography and somatosensory evoked potentials.

Results: We identified 146 patients. The most commonly encountered etiologies in this group were genetic (n=62) and genetic-metabolic disorders (n=5) followed by immune disorders (n=14), infectious diseases (n=11) and medication-related myoclonus (n=16). There were patients with cortical (n=42), cortico-subcortical (n=87), subcortical-brainstem (n=19) and spinal (n=6) types of myoclonus. Age at onset alone did not provide a correct diagnosis. Determination of electrophysiogical type of myoclonus provided correct diagnosis in 11 (7.5%) patients whereas the correct diagnosis was identified by major clinical finding in 45 (30.8%) patients (p=0.000).

Conclusion: Electrophysiological subtype of myoclonus and accompanying systemic and neurological findings help in differential diagnosis and to determine underlying etiology in patients with myoclonus. Age at onset may narrow the list. In any case with myoclonus, electrophysiology with major clinical findings provides an easy way of classification and an important contribution to identify underlying etiology.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/a-retrospective-analysis-of-clinical-and-electrophysiological-characteristics-of-patients-with-myoclonus/