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. 2018 Jan 17;15:20. doi: 10.1186/s12974-017-1041-0

Table 2.

Diagnostic value of standard CSF parameters, metabolites, and their combinations

Z. men_enc vs. Z. facial vs. Z. segmental vs.
Control Z. segmental Z. facial Ent. men Bell’s Z. segmental Bell’s Control Bell’s Control
(A) Standard CSF parameter
 Leukocyte count 0.97*** 0.94*** 0.63 0.68 0.96*** 0.70 0.72 0.80* 0.54 0.68
 Protein concentration 0.81* 0.67 0.62 0.62 0.73 0.52 0.59 0.69 0.61 0.68
 IgG index 0.74* 0.62 0.60 0.66 0.90** 0.51 0.77* 0.58 0.83** 0.63
 Lactate 0.86** 0.82** 0.71 0.70 0.88** 0.67 0.75* 0.74* 0.65 0.65
(B) Metabolites
 Best internally validated markera SM C16:1 SM C16:1 PC ae C34.0 lysoPC a C26:1 Glycine PC aa C32:0 SM(OH) C14:1 Tryptophan Arginine Serine
 AUC 0.92*** 0.90*** 0.83** 0.87** 0.96*** 0.76* 0.86** 0.84** 0.90*** 0.77*
(C) Best classifierb
 No. of markers 2 3 1 2 2 2 3 4 1 3
 Markers (frequency) Glycine
(1.0)
PC aa C30:0
(0.9)
Leukocytes
(1.0)
SM C16:1
(1.0)
total DMA
(0.60)
PC ae C34:0
(1.0)
lysoPC a C26:1
(1.0)
PC aa C32:0
(1.0)
Leukocytes
(1.0)
Glycine
(1.0)
Leukocytes
(0.73)
PC aa C32:1
(1.0)
SM(OH) C14:1
(1.0)
Glycine
(0.96)
Tryptophan
(1.0)
Tryptophan
(1.0)
SM(OH) C14:1
(0.71)
Leukocytes
(1.0)
Serine
(0.84)
Arginine
(1.0)
Serine
(1.0)
PC ae C36:3
(0.88)
Methionine
(0.67)
 AUC 1.00 0.95 0.83 0.90 1.00 0.77 0.92 0.86 0.90 0.86
 95% CI 0.87–1.0 0.76–1.0 N/A 0.57–1.0 0.89–1.0 0.51–0.91 0.61–0.98 0.55–0.98 N/A 0.53–0.96

Values correspond to areas under the receiver operator characteristic (ROC) curve (AUC) performed on continuous variables. Asymptotic significance (not corrected for multiple-hypothesis testing): *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. Italic values: lower CI > 0.5

Bell’s Bell’s palsy, CI confidence interval, DMA dimethylarginine, Ent. men enteroviral meningitis, N/A not applicable, PC phosphatidylcholine, SM sphingomyelin, SM (OH) hydroxysphingomyelin, Z. zoster, Z. men_enc Z. meningoencephalitis

aAccording to the frequency of selection (0 = never selected; 1 = always selected) in the leave-one-out cross-validation. The marker with the higher AUC was selected if two markers had the same frequencies

bBiomarker combination with the highest discriminatory ability identified by random forest construction as outlined in Methods. CIs of AUCs were evaluated based on 1000 bootstrap samples of the same size as the original data drawn with replacement