Department of Hepatogastroenterology and Endemic Medicine, Faculty of Medicine.
Department of Computer Science, Faculty of Computers and Information, Cairo University, Cairo, Egypt.
Eur J Gastroenterol Hepatol. 2019 Dec;31(12):1533-1539. doi: 10.1097/MEG.0000000000001458.
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.
食管静脉曲张（EV）是丙型肝炎病毒（HCV）肝硬化的严重并发症。内镜筛查既昂贵又具有侵入性，且患者感到不适。因此，需要采用非侵入性方法来避免不必要的内镜检查。声辐射力脉冲（ARFI）成像技术利用虚拟触诊量化技术进行点剪切波弹性成像，是一种可能的非侵入性 EV 预测方法。本研究旨在通过基于 ARFI 的研究，结合其他非侵入性参数，验证肝脏硬度（LS）和脾脏硬度（SS）的可靠性，以预测 HCV 患者的 EV。此外，我们还旨在通过数据挖掘分析评估一种新的简单预测模型（包含 SS）的诊断性能。
这是一项横断面研究，纳入了 200 名患有晚期纤维化的 HCV 患者。收集了实验室、内镜、超声、LS 和 SS 数据。评估了它们在诊断 EV 方面的准确性，并进行了数据挖掘分析。
90 名患者（F3/F4 患者中有 22%/46%）存在 EV（39 名/30 名/18 名/3 名患者的静脉曲张分级分别为 I/II/III/IV）。ARFI 检测的 LS 和 SS 在区分有无 EV 的患者方面具有重要意义（两者 P 值均=0.000），并且与静脉曲张的分级相关（R 值分别为 0.31 和 0.45，两者 P 值均=0.000）。脾脏长径（SD）、脾静脉直径（SVD）、血小板与脾脏直径比、LOK 指数和 FIB-4 评分是预测 EV 的最佳超声和生化指标（受试者工作特征曲线下面积（AUROC）分别为 0.79、0.76、0.76、0.74 和 0.71）。SS（使用 ARFI）在预测 EV 方面的诊断性能优于 LS（AUROC 分别为 0.76 和 0.70）。使用数据挖掘构建简单预测模型后，诊断性能提高：如果 [（cm）×0.17+（mm）×0.06+（SS）×0.97]大于 6.35，提示 EV 高概率，此时预测模型的 AUROC 为 0.85。
ARFI 检测的 SS 是 HCV 患者预测 EV 的一种可靠的非侵入性工具，特别是将其纳入基于数据挖掘的新预测模型时。
