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. Author manuscript; available in PMC: 2022 May 1.

Published in final edited form as: Tuberculosis (Edinb). 2021 Apr 10;128:102082. doi: 10.1016/j.tube.2021.102082

Table 5.

Multi-marker models for responder classification prediction at diagnosis.

Responder Classification		Resubstitution classification matrix
Markers (all unstimulated)	AUC (95% CI)	Sensitivity (95% CI)	Specificity (95% CI)	PPV	NPV
sgp130_Nil SAP P_Nil IFN-α2_Nil^* sIL-1R2_Nil^* EGF_Nil	0.97 (0.93 – 1.00)	87.5% (67.6 – 97.3%)	100.0% (79.4 – 100.0%)	1.00	0.81
		Leave-one-out crossvalidation
		Sensitivity (95% CI)	Specificity (95% CI)	PPV	NPV
		79.2% (57.8 – 92.9%)	92.3% (64.0 – 99.8%)	0.95	0.71
		Resubstitution classification matrix
Markers (all stimulated)	AUC (95% CI)	Sensitivity (95% CI)	Specificity (95% CI)	PPV	NPV
EGF_Ag-Nil MCP-3_Ag-Nil^* MIP-1β_Ag-Nil^* IFN-γ_Ag-Nil^* CRP_Ag-Nil^*	0.89 (0.79 – 0.99)	70.8% (48.9 – 87.4%)	84.6% (54.6 – 98.1%)	0.89	0.61
		Leave-one-out crossvalidation
		Sensitivity (95% CI)	Specificity (95% CI)	PPV	NPV
		66.7% (44.7 – 84.4%)	76.9% (46.2 – 95.0%)	0.84	0.56

^*

Winsorized