Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China.
Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin, China.
Clin Transl Gastroenterol. 2020 Nov;11(11):e00262. doi: 10.14309/ctg.0000000000000262.
To establish and verify a simple noninvasive model based on the left gastric vein (LGV) to predict the grade of esophageal varices (EV) and high-risk EV (HEV), to facilitate clinical follow-up and timely treatment.
We enrolled 320 patients with B-viral cirrhosis. All patients underwent endoscopy, laboratory tests, liver and spleen stiffness (SS), and ultrasonography. HEV were analyzed using the χ test/t test and logistic regression in the univariate and multivariate analyses, respectively. EV grades were analyzed using the variance/rank-sum test and logistic regression. A prediction model was derived from the multivariate predictors.
In the training set, multivariate analysis showed that the independent factors of different EV grades were SS, LGV diameter, and platelet count (PLT). We developed the LGV diameter-SS to PLT ratio index (LSPI) and LGV diameter/PLT models without SS. The area under the receiver operating characteristic curve of the LSPI for diagnosis of small EV, medium EV, large EV, and HEV was 0.897, 0.899, 0.853, and 0.954, respectively, and that of the LGV/PLT was 0.882, 0.890, 0.837, and 0.942, respectively. For the diagnosis of HEV, the negative predictive value was 94.07% when LSPI < 19.8 and the positive predictive value was 91.49% when LSPI > 23.0. The negative predictive value was 95.92% when LGV/PLT < 5.15, and the positive predictive value was 86.27% when LGV/PLT > 7.40. The predicted values showed similar accuracy in the validation set.
Under appropriate conditions, the LSPI was an accurate method to detect the grade of EV and HEV. Alternatively, the LGV/PLT may also be useful in diagnosing the varices when condition limited.
建立并验证一种基于胃左静脉（LGV）的简单非侵入性模型，以预测食管静脉曲张（EV）和高危食管静脉曲张（HEV）的程度，便于临床随访和及时治疗。
我们纳入了 320 例乙型病毒性肝硬化患者。所有患者均行内镜、实验室检查、肝脏和脾脏硬度（SS）及超声检查。采用 χ 检验/t 检验及单因素和多因素分析分别对 HEV 进行分析。采用方差/秩和检验和 logistic 回归对 EV 分级进行分析。从多变量预测因子中得出预测模型。
在训练集中，多因素分析显示不同 EV 分级的独立因素为 SS、LGV 直径和血小板计数（PLT）。我们开发了 LGV 直径-SS 与 PLT 比值指数（LSPI）和无 SS 的 LGV 直径/PLT 模型。LSPI 诊断小 EV、中 EV、大 EV 和 HEV 的受试者工作特征曲线下面积分别为 0.897、0.899、0.853 和 0.954，LGV/PLT 分别为 0.882、0.890、0.837 和 0.942。对于 HEV 的诊断，LSPI<19.8 时阴性预测值为 94.07%，LSPI>23.0 时阳性预测值为 91.49%。LGV/PLT<5.15 时阴性预测值为 95.92%，LGV/PLT>7.40 时阳性预测值为 86.27%。验证集中的预测值显示出类似的准确性。
在适当的条件下，LSPI 是一种准确的方法，可以检测 EV 和 HEV 的程度。此外，LGV/PLT 在条件有限时也可能有助于诊断静脉曲张。
