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

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 standar...
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Autor*in:	Gamraoui, Simon [verfasserIn] Mathey, Guillaume [verfasserIn] Debouverie, Marc [verfasserIn] Malaplate, Catherine [verfasserIn] Anxionnat, René [verfasserIn] Guillemin, Francis [verfasserIn] Epstein, Jonathan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

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

Übergeordnetes Werk:	Enthalten in: Journal of neurology - [Darmstadt] : Steinkopff, 1891, 266(2019), 4 vom: 01. Feb., Seite 902-909
Übergeordnetes Werk:	volume:266 ; year:2019 ; number:4 ; day:01 ; month:02 ; pages:902-909

Links:	Volltext

DOI / URN:	10.1007/s00415-019-09212-4

Katalog-ID:	SPR005379059

Internformat


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520			\|a 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.
650		4	\|a Multiple sclerosis \|7 (dpeaa)DE-He213
650		4	\|a Bayesian analysis \|7 (dpeaa)DE-He213
650		4	\|a Cerebrospinal fluid \|7 (dpeaa)DE-He213
650		4	\|a Diagnostic test assessment \|7 (dpeaa)DE-He213
650		4	\|a Latent class model \|7 (dpeaa)DE-He213
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700	1		\|a Debouverie, Marc \|e verfasserin \|4 aut
700	1		\|a Malaplate, Catherine \|e verfasserin \|4 aut
700	1		\|a Anxionnat, René \|e verfasserin \|4 aut
700	1		\|a Guillemin, Francis \|e verfasserin \|4 aut
700	1		\|a Epstein, Jonathan \|e verfasserin \|4 aut
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912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
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912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
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Indexfelder

author_variant	s g sg g m gm m d md c m cm r a ra f g fg j e je
matchkey_str	article:14321459:2019----::ihefracocrbopnlliimngouignlssodan
hierarchy_sort_str	2019
bklnumber	44.90
publishDate	2019
allfields	10.1007/s00415-019-09212-4 doi (DE-627)SPR005379059 (SPR)s00415-019-09212-4-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.90 bkl Gamraoui, Simon verfasserin aut High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Multiple sclerosis (dpeaa)DE-He213 Bayesian analysis (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnostic test assessment (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213 Mathey, Guillaume verfasserin aut Debouverie, Marc verfasserin aut Malaplate, Catherine verfasserin aut Anxionnat, René verfasserin aut Guillemin, Francis verfasserin aut Epstein, Jonathan verfasserin aut Enthalten in Journal of neurology [Darmstadt] : Steinkopff, 1891 266(2019), 4 vom: 01. Feb., Seite 902-909 (DE-627)242065155 (DE-600)1421299-7 1432-1459 nnns volume:266 year:2019 number:4 day:01 month:02 pages:902-909 https://dx.doi.org/10.1007/s00415-019-09212-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 266 2019 4 01 02 902-909
spelling	10.1007/s00415-019-09212-4 doi (DE-627)SPR005379059 (SPR)s00415-019-09212-4-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.90 bkl Gamraoui, Simon verfasserin aut High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Multiple sclerosis (dpeaa)DE-He213 Bayesian analysis (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnostic test assessment (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213 Mathey, Guillaume verfasserin aut Debouverie, Marc verfasserin aut Malaplate, Catherine verfasserin aut Anxionnat, René verfasserin aut Guillemin, Francis verfasserin aut Epstein, Jonathan verfasserin aut Enthalten in Journal of neurology [Darmstadt] : Steinkopff, 1891 266(2019), 4 vom: 01. Feb., Seite 902-909 (DE-627)242065155 (DE-600)1421299-7 1432-1459 nnns volume:266 year:2019 number:4 day:01 month:02 pages:902-909 https://dx.doi.org/10.1007/s00415-019-09212-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 266 2019 4 01 02 902-909
allfields_unstemmed	10.1007/s00415-019-09212-4 doi (DE-627)SPR005379059 (SPR)s00415-019-09212-4-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.90 bkl Gamraoui, Simon verfasserin aut High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Multiple sclerosis (dpeaa)DE-He213 Bayesian analysis (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnostic test assessment (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213 Mathey, Guillaume verfasserin aut Debouverie, Marc verfasserin aut Malaplate, Catherine verfasserin aut Anxionnat, René verfasserin aut Guillemin, Francis verfasserin aut Epstein, Jonathan verfasserin aut Enthalten in Journal of neurology [Darmstadt] : Steinkopff, 1891 266(2019), 4 vom: 01. Feb., Seite 902-909 (DE-627)242065155 (DE-600)1421299-7 1432-1459 nnns volume:266 year:2019 number:4 day:01 month:02 pages:902-909 https://dx.doi.org/10.1007/s00415-019-09212-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 266 2019 4 01 02 902-909
allfieldsGer	10.1007/s00415-019-09212-4 doi (DE-627)SPR005379059 (SPR)s00415-019-09212-4-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.90 bkl Gamraoui, Simon verfasserin aut High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Multiple sclerosis (dpeaa)DE-He213 Bayesian analysis (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnostic test assessment (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213 Mathey, Guillaume verfasserin aut Debouverie, Marc verfasserin aut Malaplate, Catherine verfasserin aut Anxionnat, René verfasserin aut Guillemin, Francis verfasserin aut Epstein, Jonathan verfasserin aut Enthalten in Journal of neurology [Darmstadt] : Steinkopff, 1891 266(2019), 4 vom: 01. Feb., Seite 902-909 (DE-627)242065155 (DE-600)1421299-7 1432-1459 nnns volume:266 year:2019 number:4 day:01 month:02 pages:902-909 https://dx.doi.org/10.1007/s00415-019-09212-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 266 2019 4 01 02 902-909
allfieldsSound	10.1007/s00415-019-09212-4 doi (DE-627)SPR005379059 (SPR)s00415-019-09212-4-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.90 bkl Gamraoui, Simon verfasserin aut High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Multiple sclerosis (dpeaa)DE-He213 Bayesian analysis (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnostic test assessment (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213 Mathey, Guillaume verfasserin aut Debouverie, Marc verfasserin aut Malaplate, Catherine verfasserin aut Anxionnat, René verfasserin aut Guillemin, Francis verfasserin aut Epstein, Jonathan verfasserin aut Enthalten in Journal of neurology [Darmstadt] : Steinkopff, 1891 266(2019), 4 vom: 01. Feb., Seite 902-909 (DE-627)242065155 (DE-600)1421299-7 1432-1459 nnns volume:266 year:2019 number:4 day:01 month:02 pages:902-909 https://dx.doi.org/10.1007/s00415-019-09212-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 266 2019 4 01 02 902-909
language	English
source	Enthalten in Journal of neurology 266(2019), 4 vom: 01. Feb., Seite 902-909 volume:266 year:2019 number:4 day:01 month:02 pages:902-909
sourceStr	Enthalten in Journal of neurology 266(2019), 4 vom: 01. Feb., Seite 902-909 volume:266 year:2019 number:4 day:01 month:02 pages:902-909
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multiple sclerosis Bayesian analysis Cerebrospinal fluid Diagnostic test assessment Latent class model
dewey-raw	610
isfreeaccess_bool	false
container_title	Journal of neurology
authorswithroles_txt_mv	Gamraoui, Simon @@aut@@ Mathey, Guillaume @@aut@@ Debouverie, Marc @@aut@@ Malaplate, Catherine @@aut@@ Anxionnat, René @@aut@@ Guillemin, Francis @@aut@@ Epstein, Jonathan @@aut@@
publishDateDaySort_date	2019-02-01T00:00:00Z
hierarchy_top_id	242065155
dewey-sort	3610
id	SPR005379059
language_de	englisch
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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. 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author	Gamraoui, Simon
spellingShingle	Gamraoui, Simon ddc 610 bkl 44.90 misc Multiple sclerosis misc Bayesian analysis misc Cerebrospinal fluid misc Diagnostic test assessment misc Latent class model High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis
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topic_title	610 ASE 44.90 bkl High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis Multiple sclerosis (dpeaa)DE-He213 Bayesian analysis (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnostic test assessment (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213
topic	ddc 610 bkl 44.90 misc Multiple sclerosis misc Bayesian analysis misc Cerebrospinal fluid misc Diagnostic test assessment misc Latent class model
topic_unstemmed	ddc 610 bkl 44.90 misc Multiple sclerosis misc Bayesian analysis misc Cerebrospinal fluid misc Diagnostic test assessment misc Latent class model
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title	High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis
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title_full	High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis
author_sort	Gamraoui, Simon
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author_browse	Gamraoui, Simon Mathey, Guillaume Debouverie, Marc Malaplate, Catherine Anxionnat, René Guillemin, Francis Epstein, Jonathan
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title_sort	high performance of cerebrospinal fluid immunoglobulin g analysis for diagnosis of multiple sclerosis
title_auth	High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis
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.
abstractGer	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.
abstract_unstemmed	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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container_issue	4
title_short	High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis
url	https://dx.doi.org/10.1007/s00415-019-09212-4
remote_bool	true
author2	Mathey, Guillaume Debouverie, Marc Malaplate, Catherine Anxionnat, René Guillemin, Francis Epstein, Jonathan
author2Str	Mathey, Guillaume Debouverie, Marc Malaplate, Catherine Anxionnat, René Guillemin, Francis Epstein, Jonathan
ppnlink	242065155
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s00415-019-09212-4
up_date	2024-07-03T15:55:16.115Z
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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. 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High performance of cerebrospinal fluid immunoglobulin G analysis for diagnosis of multiple sclerosis

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