Table 2: Performance of tests for diagnosis of significant fibrosis () and cirrhosis () as estimated by classical 2 × 2 analysis (liver biopsy as gold standard) and Latent Class Analysis (without gold standard) considering the model that better fitted data (2LC).


Sensitivity
(95% CI)
Specificity
(95% CI)
Positive LRNegative LR
Classical 2 × 2LCAClassical 2 × 2LCA

Significant fibrosis (F ≥ 2)
TE0.87
(0.78–0.96)
0.92
(0.86–0.98)
0.71
(0.60–0.82)
0.79
(0.72–0.86)
3.10.2
APRI0.41
(0.27–0.55)
0.47
(0.40–0.54)
0.92
(0.89–0.95)
0.99
(0.95–1.00)
5.10.6
ELF0.78
(0.67–0.89)
0.81
(0.74–0.88)
0.73
(0.62–0.84)
0.78
(0.71–0.85)
2.90.3
Liver biopsy0.86
(0.68–1.00)
0.91
(0.79–1.00)

Cirrhosis (F = 4)
TE1.000.92
(0.76–1.00)
0.80
(0.71–0.89)
0.94
(0.91–0.97)
4.5<0.1
APRI0.50
(0.16–0.84)
0.57
(0.37–0.77)
0.87
(0.81–0.93)
0.97
(0.93–1.00)
3.90.6
ELF0.88
(0.68–1.00)
0.94
(0.84–1.00)
0.73
(0.64–0.82)
0.88
(0.82–0.94)
3.30.2
Liver biopsy0.30
(0.12–0.48)
1.00

standard by definition. 2LC, two latent class; TE, transient elastography; APRI, aspartate-to-platelet-ratio-index; ELF, enhanced liver fibrosis; CI, confidence interval; LCA, Latent Class Analysis; LR, likelihood ratio; AUROC, area under the receiver operator curve. Positive LR and AUROC were calculated by classical analysis using liver biopsy as gold standard. Models that data better fitted (2LC) for diagnosis of significant fibrosis [ of 9.9504 ( value = 0.1268)/Bayesian information criteria = −18.6226] and cirrhosis [ of 5.6494 ( value = 0.4636)/Bayesian information criteria = −22.9237] were considered for Latent Class Analysis.