Format

Send to

Choose Destination
Ann Neurol. 2017 Dec;82(6):892-899. doi: 10.1002/ana.25084. Epub 2017 Nov 21.

A recessive ataxia diagnosis algorithm for the next generation sequencing era.

Author information

1
Department of Neurology, Hautepierre Hospital, University Hospitals of Strasbourg, Strasbourg, France.
2
Institute of Genetics and Molecular and Cellular Biology, INSERM-U964/CNRS-UMR7104, University of Strasbourg, Illkirch, France.
3
Strasbourg Federation of Translational Medicine, University of Strasbourg, Strasbourg, France.
4
Syntax for Science, Palma, Mallorca, Spain.
5
Department of Genetics, Pitié-Salpêtrière University Hospital, Paris, France.
6
Neurometabolic GRC, Pierre and Marie Curie University, Paris, France.
7
Neurometabolic Research Group, Pierre and Marie Curie University, Paris, France.
8
Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
9
German Center for Neurodegenerative Diseases, Tübingen, Germany.
10
Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behavior, Nijmegen, the Netherlands.
11
Department of Neurology, Free University of Brussels, Erasme Hospital, Brussels, Belgium.
12
Rare Disease Genetics Laboratory, University Institute of Clinical Research, University of Montpellier, Montpellier University Hospital Center, Montpellier, France.
13
Actelion Pharmaceuticals, Allschwil, Switzerland.

Abstract

OBJECTIVE:

Differential diagnosis of autosomal recessive cerebellar ataxias can be challenging. A ranking algorithm named RADIAL that predicts the molecular diagnosis based on the clinical phenotype of a patient has been developed to guide genetic testing and to align genetic findings with the clinical context.

METHODS:

An algorithm that follows clinical practice, including patient history, clinical, magnetic resonance imaging, electromyography, and biomarker features, was developed following a review of the literature on 67 autosomal recessive cerebellar ataxias and personal clinical experience. Frequency and specificity of each feature were defined for each autosomal recessive cerebellar ataxia, and corresponding prediction scores were assigned. Clinical and paraclinical features of patients are entered into the algorithm, and a patient's total score for each autosomal recessive cerebellar ataxia is calculated, producing a ranking of possible diagnoses. Sensitivity and specificity of the algorithm were assessed by blinded analysis of a multinational cohort of 834 patients with molecularly confirmed autosomal recessive cerebellar ataxia. The performance of the algorithm was assessed versus a blinded panel of autosomal recessive cerebellar ataxia experts.

RESULTS:

The correct diagnosis was ranked within the top 3 highest-scoring diagnoses at a sensitivity and specificity of >90% for 84% and 91% of the evaluated genes, respectively. Mean sensitivity and specificity of the top 3 highest-scoring diagnoses were 92% and 95%, respectively. The algorithm outperformed the panel of ataxia experts (p = 0.001).

INTERPRETATION:

Our algorithm is highly sensitive and specific, accurately predicting the underlying molecular diagnoses of autosomal recessive cerebellar ataxias, thereby guiding targeted sequencing or facilitating interpretation of next-generation sequencing data. Ann Neurol 2017;82:892-899.

PMID:
29059497
DOI:
10.1002/ana.25084
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center