Abstract: Objective To explore a rapid and accurate method for the diagnosis of smear negative tuberculosis meningitis (TBM). Methods Sixty-seven patients with TBM were selected from Tianjin Haihe Hospital from June 2014 to June 2016, and 118 patients with non-tuberculous meningitis (NTBM) in the same period were chosen as control group, including bacterial meningitis (BM) group (n=61) and viral meningitis (VM) group (n=57). The laboratory routine, biochemical and immune indicators were tested with the specimens of both the blood and cerebrospinal fluid of all the patients. The Logistic regression equation was established for the diagnosis of TBM, and the diagnostic efficacy of which was evaluated by the receiver operating characteristic curve (ROC). Results The predictive regression equations of the TBM with BM, VM and NTBM (BM + VM) were obtained when BM group was used as a control: PRE_BM=1/1 + e -(-5.298+0.196×ESAT- 6+ 0.119×CFP-10-2.968×PCT+2.206×ADA_CSF+ 0.705×GLU_CSF+ 0.093×LDH_CSF), PRE_VM=1/1+e-(-6.907+0.394×ESAT- 6-0.120× Na+2.633×ADA_CSF- 0.088×Cl_CSF) and PRE_NTBM=1/1+e-(0.683+0.099×ESAT-6+0.063×CFP-10-2.645×PCT +1.393×ADA_CSF+ 1.342×TbAb_CSF）respectively. When BM group was served as a control, the sensitivity, specificity, positive and negative predictive values of the regression for the diagnosis of TBM were 97.01% (89.63%- 99.64% ), 98.36% (91.20%- 99.96% ), 98.48% (91.84%- 99.96% ) and 96.77% (88.83%- 99.61% ), respectively.When VM group was served as a control, which were 94.03% (85.41%- 98.35% ), 94.74% (85.38%- 98.90% ), 95.45% (87.29%- 99.05% ) and 93.10% (83.27%- 98.09% ), respectively. When NTBM group was served as control, which were 94.03% (85.41% ~98.35% ), 90.68% (83.93%- 95.25% ), 85.14% (74.96%- 92.34% ) and 96.40% (91.03%- 99.01% ), respectively. Conclusion The predictive regression equation could be used as early diagnostic TBM with high sensitivity and specificity, which should be popularized in clinical practice, while, according to the higher negative predictive value, the negative results of which could be used to rule out of the TBM and non-empirical medication.

Key words: tuberculosis, meningeal, meningitis, bacterial, meningitis, viral, Logistic models, forecasting, sensitivity and specificity