Development and verification of a discriminate algorithm for diagnosing post-neurosurgical bacterial meningitis-A multicenter observational study.

Objective: To evaluate the diagnostic accuracy of cerebrospinal fluid (CSF)-based routine clinical examinations for post-neurosurgical bacterial meningitis (PNBM) in multicenter post-neurosurgical patients.

Methods: The diagnostic accuracies of routine examinations to distinguish between PNBM and post-neurosurgical aseptic meningitis (PNAM) were evaluated by determining the values of the area under the curve (AUC) of the receiver operating characteristic curve in a retrospective analysis of post-neurosurgical patients in four centers.

Results: An algorithm was constructed using the logistic analysis as a classical method to maximize the capacity for differentiating the two classes by integrating the measurements of five variables. The AUC value of this algorithm was 0.907, which was significantly higher than those of individual routine blood/CSF examinations. The predicted value from 70 PNBM patients was greater than the cutoff value, and the diagnostic accuracy rate was 75.3%. The results of 181 patients with PNAM showed that 172 patients could be correctly identified with specificity of 95.3%, while the overall correctness rate of the algorithm was 88.6%.

Conclusions: Routine biomarkers such as CSF/blood glucose ratio (C/B-Glu), CSF lactate (C-Lac), CSF glucose concentration (C-Glu), CSF leukocyte count (C-Leu), and blood glucose concentration (B-Glu) can be used for auxiliary diagnosis of PNBM. The multicenter retrospective research revealed that the combination of the five abovementioned biomarkers can effectively improve the efficacy of the PNBM diagnosis.