ICNC Abstracts, ICNC 2018

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A Diagnostic Algorithm for Early Onset Ataxia
Rick Brandsma, Corien Verschuuren, Oebo Brouwer, Hubertus Kremer, Tom de Koning, Marina de koning-Tijssen, Deborah Sival

Last modified: 2018-09-09

Abstract


Objective: To provide a clinical diagnostic algorithm for Early Onset Ataxia (EOA) that can contribute to an increased diagnostic yield.

Background: EOA comprises a large group of rare and heterogeneous disorders, manifesting before the 25th year of life. The large variety in phenotypes and genotypes makes the diagnostic workup a challenging task. The Childhood Ataxia and Cerebellar Group of the EPNS (CACG-EPNS) propose a clinical diagnostic algorithm that guides the clinician through the broad differential diagnosis of EOA.

Methods: We characterized seven crucial steps for the differential diagnosis of EOA, including: 1. clinical features, 2. assessment of additional features, 3. family history and spot diagnosis of distinct phenotypes, 4. magnetic resonance imaging, 5. biochemical testing, 6. genetic testing by Array investigation and 7. Next Generation Sequencing (NGS), including an EOA gene panel. In a small pilot, we retrospectively determined the algorithm’s diagnostic yield, using a thoroughly phenotyped historic cohort of 35 EOA patients with either “core-ataxic” (n=18), or “mixed-ataxic” (n=17) phenotypes.

Results: The diagnostic yield was 86% (core-ataxia 83% vs mixed-ataxia 88%; ns). The algorithm did not identify 2 patients with a mitochondrial disorder (core-ataxia) and 3 patients remain without a diagnosis (core and mixed-ataxia).

Conclusions: In a well-phenotyped historic cohort of EOA children, the yield of the diagnostic algorithm seemed relatively high. Future international prospective studies may hopefully elucidate the clinical gain of the algorithm.


Keywords


early onset ataxia; diagnostic algorithm; diagnostic yield

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