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An Optimal Prognostic Model Based on Multiparameter Ultrasound for Acute-on-Chronic Liver Failure
Lili Wu1, Yuankai Wu2, Jieyang Jin1
1Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, China.
Objective:
Acute-on-chronic liver failure (ACLF) is associated with a considerably high mortality, and accurate prognosis prediction is critical to navigate intervention decisions and improve clinical outcomes. The objective of this study was to establish a better prognostic model for ACLF based on multiparameter ultrasound in combination with clinical features.
Methods:
A total of 149 patients with ACLF were prospectively enrolled and underwent conventional ultrasound, 2-D shear wave elastography (SWE), attenuation imaging, color Doppler sonography, superb microvascular imaging and contrast-enhanced ultrasound (CEUS). Univariate and multivariate analyses were performed to identify independent ultrasound signatures for the prognosis of ACLF, which, when integrated with clinical characteristics, were used to establish a prognostic model.
Results:
Hepatic perfusion features of CEUS differed significantly between the poor and good prognosis groups, among which the time interval (TI) between peak portal vein (PV) velocity and liver parenchyma (LP) enhancement, TI(PV, LP), was independently associated with the prognosis of ACLF. A prediction model comprising TI(PV, LP) and the international normalized ratio was established, and the area under the curve (AUC) was 0.851, which is greater than those of the Model for End-stage Liver Disease (0.785), fall time of LP model (0.754), 2-D SWE nomogram (0.708) and TI(PV, LP) (0.352). Furthermore, the performance of the model was verified in an independent validation cohort (AUC = 0.920).
Conclusion:
The newly developed model performs better than existing tested models; thus, it has potential as a better non-invasive model for predicting the prognosis of patients with ACLF. A future multicenter, large-sample study is required to validate the performance of this model.

