Simon Gamraoui1,2, Guillaume Mathey3,4, Marc Debouverie5,3,4, Catherine Malaplate6, René Anxionnat7, Francis Guillemin5,4, Jonathan Epstein5,4. 1. Inserm CIC-EC 1433, Nancy University Hospital, Université de Lorraine, 54 000, Nancy, France. simon.gamraoui@gmail.com. 2. CHRU de Nancy-Hôpitaux de Brabois, Allée du Morvan 54511, Vandœuvre-les-Nancy Cedex, 54511, Nancy, France. simon.gamraoui@gmail.com. 3. Department of Neurology, Nancy University Hospital, 54 000, Nancy, France. 4. Université de Lorraine, EA 4360 Apemac, 54 000, Nancy, France. 5. Inserm CIC-EC 1433, Nancy University Hospital, Université de Lorraine, 54 000, Nancy, France. 6. Department of Biochemistry, Molecular Biology and Nutrition, Nancy University Hospital, 54 000, Nancy, France. 7. Department of Neuroradiology, Nancy University Hospital, 54 000, Nancy, France.
Abstract
BACKGROUND: The 2017 revision of the McDonald criteria highlights the usefulness of cerebrospinal fluid (CSF) immunoglobulin G (IgG) analysis to diagnose multiple sclerosis (MS). The objective of this study was to assess the diagnostic performances of CSF IgG analysis in the absence of a gold standard. METHODS: All patients who underwent CSF IgG analysis for events suggestive of MS in Nancy University Hospital (France) from 2008 to 2011 were retrospectively included. A latent class analysis with Bayesian approach was used to infer MS prevalence (latent variable) as well as the diagnostic properties of the 2005 and 2010 McDonald criteria and CSF IgG analysis (observed variables). RESULTS: Data from 673 patients were analysed. For CSF IgG analysis, the Bayesian latent class analysis estimated sensitivity of 0.93 (95% CrI 0.89-0.96) and specificity of 0.81 (95% CrI 0.77-0.85). The true prevalence estimate was 36% (95% CrI 0.33-0.40). Sensitivity and specificity estimates for patients with events suggestive of remitting-onset MS were similar to those for the whole sample-0.92 (95% CrI 0.85-0.96) and 0.80 (95% CrI 0.76-0.84), respectively-but higher for patients with signs of progressive-onset MS-0.95 (95% CrI 0.84-0.99) and 0.88 (95% CrI 0.78-0.94), respectively. CONCLUSIONS: In the absence of a gold standard, latent class analysis indicates good diagnostic properties of CSF IgG analysis for MS. This test could thus be useful, especially for patients who tested negative for the 2005 and 2010 McDonald criteria. These findings deserve to be confirmed prospectively.
BACKGROUND: The 2017 revision of the McDonald criteria highlights the usefulness of cerebrospinal fluid (CSF) immunoglobulin G (IgG) analysis to diagnose multiple sclerosis (MS). The objective of this study was to assess the diagnostic performances of CSF IgG analysis in the absence of a gold standard. METHODS: All patients who underwent CSF IgG analysis for events suggestive of MS in Nancy University Hospital (France) from 2008 to 2011 were retrospectively included. A latent class analysis with Bayesian approach was used to infer MS prevalence (latent variable) as well as the diagnostic properties of the 2005 and 2010 McDonald criteria and CSF IgG analysis (observed variables). RESULTS: Data from 673 patients were analysed. For CSF IgG analysis, the Bayesian latent class analysis estimated sensitivity of 0.93 (95% CrI 0.89-0.96) and specificity of 0.81 (95% CrI 0.77-0.85). The true prevalence estimate was 36% (95% CrI 0.33-0.40). Sensitivity and specificity estimates for patients with events suggestive of remitting-onset MS were similar to those for the whole sample-0.92 (95% CrI 0.85-0.96) and 0.80 (95% CrI 0.76-0.84), respectively-but higher for patients with signs of progressive-onset MS-0.95 (95% CrI 0.84-0.99) and 0.88 (95% CrI 0.78-0.94), respectively. CONCLUSIONS: In the absence of a gold standard, latent class analysis indicates good diagnostic properties of CSF IgG analysis for MS. This test could thus be useful, especially for patients who tested negative for the 2005 and 2010 McDonald criteria. These findings deserve to be confirmed prospectively.
Entities:
Keywords:
Bayesian analysis; Cerebrospinal fluid; Diagnostic test assessment; Latent class model; Multiple sclerosis
Authors: G A SCHUMACHER; G BEEBE; R F KIBLER; L T KURLAND; J F KURTZKE; F MCDOWELL; B NAGLER; W A SIBLEY; W W TOURTELLOTTE; T L WILLMON Journal: Ann N Y Acad Sci Date: 1965-03-31 Impact factor: 5.691
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