Diagnostic approach to episodic ataxia types 1 and 2: a proposed algorithm for limited resource-settings

Episodic ataxias (EA) comprise a heterogeneous group of genetic conditions with spells of gait difficulty and imbalance, for which the main causes are EA1 (KCNA1 gene) and EA2 (CACNA1A gene). While EA1 may respond to some antiepileptics and EA2 responds to acetazolamide, no guideline exists to inform decision-making in settings where genetic testing is unavailable.

We sought to determine distinguishing clinical features between EA1 and EA2 and propose an algorithm based on our findings.

Systematized literature review to identify individuals with confirmed pathogenic variants in KCNA1 and CACNA1A, followed by statistical analysis to compose a management algorithm. Subsequently, the algorithm was tested in cases described within the last three years.

Attack duration with a cut-off of < 10 min had high sensitivity (75.3%) and specificity (94.0%) for EA1. Additional features with high specificity included symptoms during the attacks (e.g., headaches in EA2, 95.7%) and symptoms between attacks (e.g., myokymia in EA1 99.6%; nystagmus in EA2, 98.8%). Kinesigenic triggers were more frequently reported in EA1 (68.4% vs. 5.3%, p < 0.001). EA1 subjects also had more frequent attacks (Daily 37.9% vs. 15.9%, p < 0.001) and had a lower age of onset (7y, IQR [4-10] vs. 10y, IQR [5-15], p = 0.003). Testing our algorithm in a case cohort yielded a sensitivity of 87.5% in identifying EA2 cases.

EA1 and EA2 patients represent clinically different populations. We propose a management algorithm based on features with highest diagnostic accuracy, which may inform decision-making in resource-limited settings.