Abstract
Esophageal varices (EV) are serious complications of hepatitis C virus (HCV) cirrhosis. Endoscopic screening is expensive, invasive, and uncomfortable. Accordingly, noninvasive methods are mandatory to avoid unnecessary endoscopy. Acoustic radiation forced impulse (ARFI) imaging using point shear wave elastography as demonstrated with virtual touch quantification is a possible noninvasive EV predictor. We aimed to validate the reliability of liver stiffness (LS) and spleen stiffness (SS) by an ARFI-based study together with other noninvasive parameters for EV prediction in HCV patients. Also, we aimed to evaluate the diagnostic performance of a new simple prediction model (incorporating SS) using data mining analysis. This cross-sectional study included 200 HCV patients with advanced fibrosis. Labs, endoscopic, ultrasonographic, LS, and SS data were collected. Their accuracy in diagnosing EV was assessed and a data mining analysis was carried out. Ninety patients (22/46% of F3/F4 patients) had EV (39/30/18/3 patients had grade I/II/III/IV, respectively). LS and SS by ARFI showed high significance in differentiating not only patients with/without EV (P = 0.000 for both) but also correlated with the grading of varices (R = 0.31 and 0.45, respectively; P = 0.000 for both). Spleen longitudinal diameter (SD), splenic vein diameter (SVD), platelets to spleen diameter ratio, LOK index, and FIB-4 score were the best ultrasonographic and biochemical predictors for the prediction of EV [area under receiver operating characteristic (AUROC) 0.79, 0.76, 0.76, 0.74, and 0.71, respectively]. SS (using ARFI) had better diagnostic performance than LS for the prediction of EV (AUROC = 0.76 and 0.70, respectively). The diagnostic performance increased using data mining to construct a simple prediction model: high probability for EV if [(SD cm) × 0.17 + (SVD mm) × 0.06 + (SS) × 0.97] more than 6.35 with AUROC 0.85. SS by ARFI represents a reliable noninvasive tool for the prediction of EV in HCV patients, especially when incorporated into a new data mining-based prediction model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.