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

  • Simon GamraouiEmail author
  • Guillaume Mathey
  • Marc Debouverie
  • Catherine Malaplate
  • René Anxionnat
  • Francis Guillemin
  • Jonathan Epstein
Original Communication
  • 39 Downloads

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.

Keywords

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

Notes

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.

Compliance with ethical standards

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.

References

  1. 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;122:552–568CrossRefGoogle Scholar
  2. 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):227–231CrossRefGoogle Scholar
  3. 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):121–127CrossRefGoogle Scholar
  4. 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):840–846CrossRefGoogle Scholar
  5. 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):292–302CrossRefGoogle Scholar
  6. 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):162–173CrossRefGoogle Scholar
  7. 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):1863–1874CrossRefGoogle Scholar
  8. 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):1013–1024CrossRefGoogle Scholar
  9. 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):1075–1084CrossRefGoogle Scholar
  10. 10.
    Petzold A. Intrathecal oligoclonal IgG synthesis in multiple sclerosis. J Neuroimmunol. 2013;262(1–2):1–10CrossRefGoogle Scholar
  11. 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):916–921CrossRefGoogle Scholar
  12. 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):1244–1250CrossRefGoogle Scholar
  13. 13.
    Housley WJ, Pitt D, Hafler DA (2015) Biomarkers in multiple sclerosis. Clin Immunol Orlando Fla 161(1):51–58CrossRefGoogle Scholar
  14. 14.
    Green BF. A general solution for the latent class model of latent structure analysis. Psychometrika. 1951;16(2):151–166CrossRefGoogle Scholar
  15. 15.
    Rindskopf D, Rindskopf W (1986) The value of latent class analysis in medical diagnosis. Stat Med 5(1):21–27CrossRefGoogle Scholar
  16. 16.
    Hui SL, Zhou XH (1998) Evaluation of diagnostic tests without gold standards. Stat Methods Med Res 7(4):354–370CrossRefGoogle Scholar
  17. 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):1289–1307CrossRefGoogle Scholar
  18. 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):263–272CrossRefGoogle Scholar
  19. 19.
    Dendukuri N, Joseph L (2001) Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics 57(1):158–167CrossRefGoogle Scholar
  20. 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 14:67CrossRefGoogle Scholar
  21. 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):2688–2697CrossRefGoogle Scholar
  22. 22.
    Torrance-Rynard VL, Walter SD (1997) Effects of dependent errors in the assessment of diagnostic test performance. Stat Med 16(19):2157–2175CrossRefGoogle Scholar
  23. 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):622–625CrossRefGoogle Scholar
  24. 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):677–686CrossRefGoogle Scholar
  25. 25.
    McGee S (2002) Simplifying likelihood ratios. J Gen Intern Med 17(8):647–650CrossRefGoogle Scholar
  26. 26.
    Parikh R, Parikh S, Arun E, Thomas R (2009) Likelihood ratios: clinical application in day-to-day practice. Indian J Ophthalmol 57(3):217–221CrossRefGoogle Scholar
  27. 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):39–44CrossRefGoogle Scholar
  28. 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):34–37CrossRefGoogle Scholar
  29. 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):458–463CrossRefGoogle Scholar
  30. 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):985–987CrossRefGoogle Scholar
  31. 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):68–74CrossRefGoogle Scholar
  32. 32.
    Falip M, Tintoré M, Jardí R, Duran I, Link H, Montalbán X. Clinical usefulness of oligoclonal bands. Rev Neurol. 2001;32(12):1120–1124Google Scholar
  33. 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):525–529CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Inserm CIC-EC 1433, Nancy University HospitalUniversité de LorraineNancyFrance
  2. 2.Department of NeurologyNancy University HospitalNancyFrance
  3. 3.Université de LorraineNancyFrance
  4. 4.Department of Biochemistry, Molecular Biology and NutritionNancy University HospitalNancyFrance
  5. 5.Department of NeuroradiologyNancy University HospitalNancyFrance
  6. 6.CHRU de Nancy-Hôpitaux de BraboisNancyFrance

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