Table 1.
Characteristics of the patients with significant inflammation in the training set.
Figure 1.
Area under the curve (AUC) of the enrolled variables in differentiating significant inflammation in the training set.
(A) AUC of γ-glutamyl transpeptidase (GGT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and fibrosis (S) in the HBeAg(+) patients; (B) AUC of albumin (ALB), cholinesterase (CHE), and pre-albumin (Pre-ALB) in the HBeAg(+) patients; (C) AUC of GGT, ALT, AST, and fibrosis (S) in the HBeAg(−) patients; (D) AUC of ALB, CHE, and Pre-ALB in the HBeAg(−) patients.
Table 2.
Diagnostic performance of the enrolled variables in differentiating significant inflammation in the training set.
Figure 2.
Area under the curve (AUC) of the fibrosis-based activity score (F-score), Mohamadnejad et al. score (M-score), and LSM-based activity score (L-score) in differentiating significant inflammation.
(A) AUC of the F-Score and M-Score for the HBeAg(+) patients in the training set; (B) AUC of the F-Score and M-Score in the HBeAg(−) patients in the training set; (C) AUC of the F-Score, M-Score, and L-Score in the HBeAg(+) patients in the validation set; (D) AUC of the F-Score, M-Score, and L-Score in the HBeAg(−) patients in the validation set.
Table 3.
Prediction model of significant inflammation.
Table 4.
Efficacy of the prediction model on significant inflammation.