Division of Gastroenterology and Hepatology, Nippon Medical School, Tokyo, Japan.
Core Research Facilities, The Jikei University School of Medicine, Tokyo, Japan.
PLoS One. 2021 Sep 10;16(9):e0257166. doi: 10.1371/journal.pone.0257166. eCollection 2021.
Evaluating liver fibrosis is crucial for disease severity assessment, treatment decisions, and hepatocarcinogenic risk prediction among patients with chronic hepatitis C. In this retrospective multicenter study, we aimed to construct a novel model formula to predict cirrhosis. A total of 749 patients were randomly allocated to training and validation sets at a ratio of 2:1. Liver stiffness measurement (LSM) was made via transient elastography using FibroScan. Patients with LSM ≥12.5 kPa were regarded as having cirrhosis. The best model formula for predicting cirrhosis was constructed based on factors significantly and independently associated with LSM (≥12.5 kPa) using multivariate regression analysis. Among the 749 patients, 198 (26.4%) had LSM ≥12.5 kPa. In the training set, multivariate analysis identified logarithm natural (ln) type IV collagen 7S, ln hyaluronic acid, and ln Wisteria floribunda agglutinin positive Mac-2-binding protein (WFA+-Mac-2 BP) as the factors that were significantly and independently associated with LSM ≥12.5 kPa. Thus, the formula was constructed as follows: score = -6.154 + 1.166 × ln type IV collagen 7S + 0.526 × ln hyaluronic acid + 1.069 × WFA+-Mac-2 BP. The novel formula yielded the highest area under the curve (0.882; optimal cutoff, -0.381), specificity (81.5%), positive predictive values (62.6%), and predictive accuracy (81.6%) for predicting LSM ≥12.5 kPa among fibrosis markers and indices. These results were almost similar to those in the validated set, indicating the reproducibility and validity of the novel formula. The novel formula scores were significantly, strongly, and positively correlated with LSM values in both the training and validation data sets (correlation coefficient, 0.721 and 0.762; p = 2.67 × 10-81 and 1.88 × 10-48, respectively). In conclusion, the novel formula was highly capable of diagnosing cirrhosis in patients with chronic hepatitis C and exhibited better diagnostic performance compared to conventional fibrosis markers and indices.
评估肝纤维化对于慢性丙型肝炎患者的疾病严重程度评估、治疗决策和肝癌风险预测至关重要。在这项回顾性多中心研究中,我们旨在构建一种新的模型公式来预测肝硬化。总共 749 名患者按 2:1 的比例随机分配到训练集和验证集。使用 FibroScan 通过瞬时弹性成像进行肝硬度测量(LSM)。LSM≥12.5kPa 的患者被认为患有肝硬化。基于多元回归分析,根据与 LSM(≥12.5kPa)显著且独立相关的因素,构建预测肝硬化的最佳模型公式。在 749 名患者中,有 198 名(26.4%)LSM≥12.5kPa。在训练集中,多元分析确定 IV 型胶原 7S 的自然对数(ln)、透明质酸和 Wisteria floribunda agglutinin 阳性 Mac-2 结合蛋白(WFA+-Mac-2BP)的对数作为与 LSM≥12.5kPa 显著且独立相关的因素。因此,公式构建如下:分数=-6.154+1.166×IV 型胶原 7S 的 ln+0.526×透明质酸的 ln+1.069×WFA+-Mac-2BP 的 ln。新公式在纤维化标志物和指标中预测 LSM≥12.5kPa 的曲线下面积最高(0.882;最佳截断值,-0.381)、特异性(81.5%)、阳性预测值(62.6%)和预测准确率(81.6%)。这些结果在验证集中几乎相似,表明新公式的可重复性和有效性。新公式评分与训练和验证数据集的 LSM 值显著、强烈且呈正相关(相关系数分别为 0.721 和 0.762;p=2.67×10-81 和 1.88×10-48)。总之,该新公式能够高度诊断慢性丙型肝炎患者的肝硬化,并且与传统的纤维化标志物和指标相比,具有更好的诊断性能。