Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models

Table 4 Comparison of the predictive performance of the newly developed fibrosis risk score and established clinical scoring systems

Models	Cut-off	Training set					Validation set
		AUC (95%CI)	Sensitivity	Specificity	NPV	PPV	AUC (95%CI)	Sensitivity	Specificity	NPV	PPV
FRS	9	0.83 (0.75-0.88)	0.71	0.85	0.79	0.71	0.82 (0.76-0.90)	0.72	0.84	0.79	0.72
APRI	1.5	0.59 (0.49-0.66)	0.31	0.87	0.67	0.61	0.61 (0.48-0.73)	0.31	0.83	0.65	0.62
FIB-4	1.45	0.65 (0.57-0.75)	0.50	0.78	0.72	0.59	0.69 (0.59-0.78)	0.48	0.81	0.65	0.63
GPR	0.40	0.67 (0.55-0.76)	0.49	0.83	0.71	0.68	0.70 (0.59-0.80)	0.48	0.83	0.69	0.70

CI: Confidence interval; FRS: Fibrosis risk score; APRI: Aspartate-to-platelet ratio index; FIB-4: Fibrosis-4 score; GPR: γ-glutamyl transferase-to-platelet ratio; AUC: Area under the curve; NPV: Negative predictive value; PPV: Positive predictive value.