. Author manuscript; available in PMC: 2023 May 1.

Published in final edited form as: Hepatology. 2022 Nov 3;77(2):530–545. doi: 10.1002/hep.32777

Table 3.

Linear regression models predicting LSM in BA (n=187) with biomarkers added individually to conventional laboratory measurements and clinical features

Best parsimonious model using conventional labs and clinical factors R²= 0.473 (95% CI: 0.361, 0.574); Adj-R²=0.461(0.349, 0.564)
Term		Estimate		p-value
Spleen Size		0.0108		0.06
PELD		0.0165		<0.001
Platelets		−0.00079		<0.001
AST		0.0014		<0.001
Separate linear regression models predicting LSM with best parsimonious model based on conventional labs + target biomarker one at a time
Term	R² (95% CI)		Adj-R² (95% CI)		p-value
Log₁₀(LOX)	0.478 (0.367, 0.579)		0.463 (0.351, 0.566)		0.19
Log₁₀(MMP3)	0.474 (0.363, 0.575)		0.459 (0.347, 0.562)		0.55
Log₁₀(Endoglin)	0.474 (0.363, 0.576)		0.460 (0.348, 0.563)		0.48
TIMP1	0.505 (0.397, 0.602)		0.491 (0.383, 0.590)		<0.001
Log₁₀(Mac2)	0.477 (0.366, 0.578)		0.462 (0.351, 0.565)		0.25
Log₁₀(Periostin)	0.480 (0.369, 0.581)		0.465 (0.353, 0.568)		0.15
Log₁₀(IL8)	0.523 (0.416, 0.617)		0.509 (0.402, 0.606)		<0.001
Log₁₀(CTGF)	0.475 (0.364, 0.577)		0.461 (0.349, 0.564)		0.35
MMP7	0.526 (0.419, 0.621)		0.513 (0.404, 0.610)		<0.001