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

Table 1 Results of univariate and multivariate logistic regression analysis in the study cohort, n (%)

Parameter	Univariate analysis				Multivariate analysis
	Non-significant fibrosis (F0-F1) (n = 182; 57.6%)	Significant fibrosis (F2-F4) (n = 134; 42.4%)	Spearman ρ	P value	OR (95%CI)	P value
Gender			0.049	0.38	-	-
Males	96 (52.7)	64 (47.7)
Females	86 (47.3)	70 (52.3)
Age (year), mean ± SD	46.23 ± 14.5	48.10 ± 10.93	0.070	0.192	-	-
Body mass index, mean ± SD	24.03 ± 3.7	23.96 ± 3.2	0.0500	0.659	-	-
Diabetes			0.082	0.146	-	-
Yes	22 (12)	24 (17.91)
No	160 (88)	110 (82.09)
Platelet count (× 109/L)	157.7 ± 30.93	127.15 ± 28.00	-0.4421	< 0.001	0.72 (0.60-0.86)	< 0.001
Bilirubin (mg/dL)	0.726 ± 0.29	0.818 ± 0.31	0.1056	0.007	-	-
AST (U/L)	33.2 (21, 50)	49 (30, 66)	0.4768	< 0.001	1.18 (1.05-1.33)	0.003
ALT (U/L)	36 (24, 49)	47 (30, 64)	0.33	< 0.001	-	-
GGT (U/L)	26 (16, 33)	46 (25, 79)	0.50	< 0.001	1.07 (1.02-1.13)	0.01
ALP (U/L)	66 (52, 77)	81 (64, 97)	0.4298	< 0.001	1.10 (1.02-1.13)	0.01
Serum albumin (g/dL)	3.83 ± 0.29	3.16 ± 0.36	-0.6278	< 0.001	0.81 (0.68-0.96)	0.02
Total protein (g/dL)	7.082 ± 1.23	6.912 ± 1.12	-0.07	0.2	-	-
Total cholesterol (mg/dL)	160.4 ± 21.1	129.1 ± 23.9	-0.6156	< 0.001	0.84 (0.76-1.00)	0.01
Triglycerides (mg/dL)	131 (120, 170)	126 (115, 167)	-0.108	0.25	-	-
HDL (mg/dL)	37 (30, 54)	30 (26, 44)	-0.4414	< 0.001	-	-
LDL (mg/dL)	90.0 ± 4.3	65.4 ± 5.2	-0.5992	< 0.001	0.84 (0.76-1.00)	0.01
VLDL (mg/dL)	24.97 ± 12.11	23.45 ± 18.50	-0.1656	0.1424	-	-

OR: Odds ratio; CI: Confidence interval; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; GGT: γ-glutamyl transferase; ALP: Alkaline phosphatase; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; VLDL: Very low-density lipoprotein.

Table 2 Comparison of baseline characteristics and biochemical parameters between the training and validation cohorts, n (%)

Parameters	Training dataset (n = 214)	Validation dataset (n = 102)	P value
Group			0.815
F0-F1	106 (49.5)	49 (48)
F2	36 (16.8)	21 (20.5)
F3	38 (17.8)	18 (17.6)
F4	34 (15.9)	14 (13.7)
Gender			0.994
Males	109 (50.9)	52 (51)
Females	105 (49.1)	50 (49)
Age (years)	46.88 ± 13.5	47.12 ± 11.6	0.871
Body mass index	23.86 ± 3.44	24.01 ± 3.39	0.715
Diabetes			0.70
Yes	30 (14)	16 (15.6)
No	184 (86)	86 (84.3)
Platelet count (× 109/L)	145.44 ± 30.83	144.1 ± 29.2	0.708
Bilirubin (mg/dL)	0.75 ± 0.26	0.756 ± 0.27	0.852
AST (U/L)	36 (25, 60)	40 (25, 63)	0.55
ALT (U/L)	42 (23, 47)	39 (25, 51)	0.66
GGT (U/L)	34 (21, 55)	36 (20, 53)	0.90
ALP (U/L)	59 (48, 77)	63.7 (54, 80)	0.07
Serum albumin (g/dL)	3.53 ± 0.26	3.57 ± 0.33	0.284
Total protein (g/dL)	7.02 ± 1.21	7.01 ± 1.13	0.943
Total cholesterol (mg/dL)	148.33 ± 19.12	146.6 ± 22.61	0.505
Triglycerides (mg/dL)	128 (111, 167)	133.2 (112, 174)	0.38
HDL (mg/dL)	35 (27, 43)	31.9 (25, 48)	0.98
LDL (mg/dL)	80.11 ± 14.3	78.33 ± 15.2	0.323
VLDL (mg/dL)	24.32 ± 11.16	23.89 ± 17.68	0.822

ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; GGT: γ-glutamyl transferase; ALP: Alkaline phosphatase; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; VLDL: Very low-density lipoprotein.

Table 3 Newly developed fibrosis risk score system and its scoring scheme

Parameter		Points
Platelet count < 150 × 109/L		5
AST > 45 U/L		3
Serum albumin < 3.5 g/dL		3
Total cholesterol < 140 mg/dL		3
LDL < 80 mg/dL		3
ALP > 75 U/L		2
GGT > 40 U/L		1
Interpretation of the score
Total score	Risk category	Interpretation
< 5	Low risk	Minimal risk of significant fibrosis
≥ 5 and < 9	Intermediate risk	Borderline risk → further evaluation suggested
9-20	High risk	Strongly predictive of F2-F4 fibrosis

AST: Aspartate aminotransferase; GGT: γ-glutamyl transferase; ALP: Alkaline phosphatase; LDL: Low-density lipoprotein.

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.

Table 5 Comparison of machine learning algorithms for predicting significant hepatic fibrosis

Parameter	Model	AUC (95%CI)	Sensitivity	Specificity	PPV	NPV	F1 score	Brier score
Training	Random forest	0.921 (0.889-0.953)	0.825	0.900	0.892	0.837	0.857	0.141
	AdaBoost	0.881 (0.842-0.921)	0.800	0.775	0.780	0.795	0.790	0.157
	SVM	0.791 (0.742-0.842)	0.675	0.850	0.818	0.723	0.740	0.188
	Logistic regression	0.750 (0.698-0.803)	0.625	0.825	0.781	0.688	0.694	0.198
	Naive Bayes	0.751 (0.700-0.806)	0.925	0.525	0.661	0.875	0.771	0.217
	KNN	0.658 (0.602-0.716)	0.850	0.450	0.607	0.750	0.708	0.227
Validation	Random forest	0.905 (0.870-0.940	0.800	0.880	0.870	0.820	0.835	0.152
	AdaBoost	0.860 (0.820-0.903)	0.770	0.750	0.760	0.765	0.765	0.169
	SVM	0.760 (0.710-0.812)	0.650	0.820	0.790	0.700	0.710	0.205
	Logistic regression	0.735 (0.680-0.788)	0.600	0.800	0.760	0.660	0.675	0.210
	Naive Bayes	0.740 (0.690-0.795)	0.900	0.500	0.640	0.840	0.745	0.230
	KNN	0.630 (0.575-0.690)	0.820	0.420	0.580	0.720	0.685	0.245

AUC: Area under the curve; CI: Confidence interval; SVM: Support vector machine; KNN: K-nearest neighbors; NPV: Negative predictive value; PPV: Positive predictive value.