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. 2022 Jan 31;12:781315. doi: 10.3389/fcimb.2022.781315

Table 3.

The predicting performance of various diagnostic methods for distinguishing between TB and NTB in the validation cohort (Sino-French New City Hospital).

Variables Cutoff value AUC (95%CI) Sensitivity (95%CI) Specificity (95%CI) PPV (95%CI) NPV (95%CI) PLR (95%CI) NLR (95%CI) Accuracy
Diagnostic model based on TBAg/PHA Ratio 0.047 0.865 (0.815-0.915) 78.49% (69.10%-85.62%) 78.50% (69.81%-85.23%) 70.10% (58.96%-79.27%) 85.03% (81.11%-88.31%) 3.651 (2.836-4.678) 0.274 (0.206-0.363) 78.00%
Diagnostic model based on TBAg/PHA Ratio combined with AFBS 0.225 0.867 (0.817-0.917) 82.26% (73.28%-84.02%) 77.11% (68.31%-84.02%) 74.87% (63.79%-83.42%) 88.89% (85.47%-91.71%) 3.594 (2.798-4.586) 0.230 (0.166-0.318) 80.00%
Diagnostic model based on TBAg/PHA ratio combined with GeneXpert MTB/RIF 0.171 0.912 (0.872-0.953) 84.95% (76.30%-90.82%) 85.05% (77.08%-90.58%) 78.38% (67.41%-86.40%) 89.79% (85.61%-92.89%) 5.682 (3.962-8.100) 0.177 (0.119-0.262) 86.00%

TB, spinal tuberculosis; NTB, non-tuberculosis; AUC, the area under the curve; TBAg, Mycobacterium tuberculosis-specific antigen; PHA, phytohemagglutinin; AFBS, acid-fast bacilli smear; PPV, positive predictive value; NPV, negative predictive value; PLR, positive likelihood ratio; NLR, negative likelihood ratio; CI, confidence interval.