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High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis

Journal of Neurology volume 266, pages 902–909 (2019)Cite this article

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.

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References

  1. Schumacher GA, Beebe G, Kibler RF, Kurland LT, Kurtzke JF, Mcdowell F et al. Problems of experimental trials of therapy in multiple sclerosis: report by the panel on the evaluation of experimental trials of therapy in multiple sclerosis. Ann N Y Acad Sci. 1965;122:552–568

    Article  CAS  PubMed  Google Scholar 

  2. Poser CM, Paty DW, Scheinberg L, McDonald WI, Davis FA, Ebers GC et al (1983) New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 13(3):227–231

    Article  CAS  PubMed  Google Scholar 

  3. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD et al (2001) Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Ann Neurol 50(1):121–127

    Article  CAS  Google Scholar 

  4. Polman CH, Reingold SC, Edan G, Filippi M, Hartung H-P, Kappos L et al (2005 Dec) Diagnostic criteria for multiple sclerosis: 2005 revisions to the ‘McDonald Criteria’. Ann Neurol 58(6):840–846

    Article  PubMed  Google Scholar 

  5. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302

    Article  PubMed  PubMed Central  Google Scholar 

  6. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17(2):162–173

    Article  PubMed  Google Scholar 

  7. Tintore M, Rovira À, Río J, Otero-Romero S, Arrambide G, Tur C et al (2015) Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain J Neurol 138(Pt 7):1863–1874

    Article  Google Scholar 

  8. Kuhle J, Disanto G, Dobson R, Adiutori R, Bianchi L, Topping J et al (2015) Conversion from clinically isolated syndrome to multiple sclerosis: a large multicentre study. Mult Scler Houndmills Basingstoke Engl 21(8):1013–1024

    Article  CAS  Google Scholar 

  9. Arrambide G, Tintore M, Espejo C, Auger C, Castillo M, Río J et al (2018) The value of oligoclonal bands in the multiple sclerosis diagnostic criteria. Brain J Neurol 141(4):1075–1084

    Article  Google Scholar 

  10. Petzold A. Intrathecal oligoclonal IgG synthesis in multiple sclerosis. J Neuroimmunol. 2013;262(1–2):1–10

    Article  CAS  PubMed  Google Scholar 

  11. Debouverie M, Pittion-Vouyovitch S, Louis S, Guillemin F, LORSEP Group (2008) Natural history of multiple sclerosis in a population-based cohort. Eur J Neurol 15(9):916–921

    Article  CAS  PubMed  Google Scholar 

  12. El Adssi H, Debouverie M, Guillemin F, LORSEP Group (2012) Estimating the prevalence and incidence of multiple sclerosis in the Lorraine region, France, by the capture-recapture method. Mult Scler Houndmills Basingstoke Engl 18(9):1244–1250

    Article  Google Scholar 

  13. Housley WJ, Pitt D, Hafler DA (2015) Biomarkers in multiple sclerosis. Clin Immunol Orlando Fla 161(1):51–58

    Article  CAS  Google Scholar 

  14. Green BF. A general solution for the latent class model of latent structure analysis. Psychometrika. 1951;16(2):151–166

    Article  PubMed  Google Scholar 

  15. Rindskopf D, Rindskopf W (1986) The value of latent class analysis in medical diagnosis. Stat Med 5(1):21–27

    Article  CAS  PubMed  Google Scholar 

  16. Hui SL, Zhou XH (1998) Evaluation of diagnostic tests without gold standards. Stat Methods Med Res 7(4):354–370

    Article  CAS  PubMed  Google Scholar 

  17. Garrett ES, Eaton WW, Zeger S (2002) Methods for evaluating the performance of diagnostic tests in the absence of a gold standard: a latent class model approach. Stat Med 21(9):1289–1307

    Article  PubMed  Google Scholar 

  18. Joseph L, Gyorkos TW, Coupal L (1995) Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. Am J Epidemiol 141(3):263–272

    Article  CAS  PubMed  Google Scholar 

  19. Dendukuri N, Joseph L (2001) Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics 57(1):158–167

    Article  CAS  PubMed  Google Scholar 

  20. Ling DI, Pai M, Schiller I, Dendukuri N (2014) A Bayesian framework for estimating the incremental value of a diagnostic test in the absence of a gold standard. BMC Med Res Methodol 14:67

    Article  PubMed  PubMed Central  Google Scholar 

  21. Dendukuri N, Bélisle P, Joseph L (2010) Bayesian sample size for diagnostic test studies in the absence of a gold standard: comparing identifiable with non-identifiable models. Stat Med 29(26):2688–2697

    Article  PubMed  Google Scholar 

  22. Torrance-Rynard VL, Walter SD (1997) Effects of dependent errors in the assessment of diagnostic test performance. Stat Med 16(19):2157–2175

    Article  CAS  PubMed  Google Scholar 

  23. de Seze J, Debouverie M, Waucquier N, Steinmetz G, Pittion S, Zephir H et al (2007) Primary progressive multiple sclerosis: a comparative study of the diagnostic criteria. Mult Scler Houndmills Basingstoke Engl 13(5):622–625

    Article  Google Scholar 

  24. Swanton JK, Rovira A, Tintore M, Altmann DR, Barkhof F, Filippi M et al (2007) MRI criteria for multiple sclerosis in patients presenting with clinically isolated syndromes: a multicentre retrospective study. Lancet Neurol 6(8):677–686

    Article  Google Scholar 

  25. McGee S (2002) Simplifying likelihood ratios. J Gen Intern Med 17(8):647–650

    Article  PubMed Central  Google Scholar 

  26. Parikh R, Parikh S, Arun E, Thomas R (2009) Likelihood ratios: clinical application in day-to-day practice. Indian J Ophthalmol 57(3):217–221

    Article  PubMed  PubMed Central  Google Scholar 

  27. Gómez-Moreno M, Díaz-Sánchez M, Ramos-González A (2012) Application of the 2010 McDonald criteria for the diagnosis of multiple sclerosis in a Spanish cohort of patients with clinically isolated syndromes. Mult Scler Houndmills Basingstoke Engl 18(1):39–44

    Article  Google Scholar 

  28. Villar LM, García-Barragán N, Sádaba MC, Espiño M, Gómez-Rial J, Martínez-San Millán J et al. Accuracy of CSF and MRI criteria for dissemination in space in the diagnosis of multiple sclerosis. J Neurol Sci. 2008;266(1–2):34–37

    Article  PubMed  Google Scholar 

  29. Kang H, Metz LM, Traboulsee AL, Eliasziw M, Zhao GJ, Cheng Y et al (2014) Application and a proposed modification of the 2010 McDonald criteria for the diagnosis of multiple sclerosis in a Canadian cohort of patients with clinically isolated syndromes. Mult Scler Houndmills Basingstoke Engl 20(4):458–463

    Article  CAS  Google Scholar 

  30. Nielsen JM, Uitdehaag BMJ, Korteweg T, Barkhof F, Polman CH (2010) Performance of the Swanton multiple sclerosis criteria for dissemination in space. Mult Scler Houndmills Basingstoke Engl 16(8):985–987

    Article  CAS  Google Scholar 

  31. Caudie C, Birouk AM, Bancel J, Claudy D, Gignoux L, Vukusic S et al (2005) Cytoimmunological profile of cerebrospinal fluid in diagnosis of multiple sclerosis. Pathol Biol (Paris) 53(2):68–74

    Article  Google Scholar 

  32. Falip M, Tintoré M, Jardí R, Duran I, Link H, Montalbán X. Clinical usefulness of oligoclonal bands. Rev Neurol. 2001;32(12):1120–1124

    CAS  PubMed  Google Scholar 

  33. Bourahoui A, De Seze J, Guttierez R, Onraed B, Hennache B, Ferriby D et al (2004) CSF isoelectrofocusing in a large cohort of MS and other neurological diseases. Eur J Neurol 11(8):525–529

    Article  CAS  PubMed  Google Scholar 

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Funding

Study Funded by the National Institutes for Health and Medical Research (INSERM) and the Nancy university hospital (CHRU). The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author information

Affiliations

  1. Inserm CIC-EC 1433, Nancy University Hospital, Université de Lorraine, 54 000, Nancy, France

    Simon Gamraoui, Marc Debouverie, Francis Guillemin & Jonathan Epstein

  2. Department of Neurology, Nancy University Hospital, 54 000, Nancy, France

    Guillaume Mathey & Marc Debouverie

  3. Université de Lorraine, EA 4360 Apemac, 54 000, Nancy, France

    Guillaume Mathey, Marc Debouverie, Francis Guillemin & Jonathan Epstein

  4. Department of Biochemistry, Molecular Biology and Nutrition, Nancy University Hospital, 54 000, Nancy, France

    Catherine Malaplate

  5. Department of Neuroradiology, Nancy University Hospital, 54 000, Nancy, France

    René Anxionnat

  6. CHRU de Nancy-Hôpitaux de Brabois, Allée du Morvan 54511, Vandœuvre-les-Nancy Cedex, 54511, Nancy, France

    Simon Gamraoui

Authors
  1. Simon Gamraoui
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  2. Guillaume Mathey
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  3. Marc Debouverie
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  4. Catherine Malaplate
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  5. René Anxionnat
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  6. Francis Guillemin
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  7. Jonathan Epstein
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Corresponding author

Correspondence to Simon Gamraoui.

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Conflicts of interest

All authors declare that they have no conflict of interest.

Ethical standards

The study was approved by the institutional review board and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

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Cite this article

Gamraoui, S., Mathey, G., Debouverie, M. et al. High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis. J Neurol 266, 902–909 (2019). https://doi.org/10.1007/s00415-019-09212-4

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  • DOI: https://doi.org/10.1007/s00415-019-09212-4

Keywords

  • Multiple sclerosis
  • Bayesian analysis
  • Cerebrospinal fluid
  • Diagnostic test assessment
  • Latent class model