Abstract：
BACKGROUND: Non-selective beta-blockers (NSBB) are used for primary prophylaxis in patients with liver cirrhosis and high-risk varices (HRVs). Assessing therapeutic response is challenging due to the invasive nature of hepatic venous pressure gradient (HVPG) measurement. This study aims to define a noninvasive machine-learning based approach to determine response to NSBB in patients with liver cirrhosis and HRVs.
METHODS: We conducted a prospective study on a cohort of cirrhotic patients with documented HRVs receiving NSBB treatment. Patients were followed-up with clinical and elastography appointments at 3, 6, and 12 months after NSBB treatment initiation. NSBB response was defined as stationary or downstaging variceal grading at the 12-month esophagogastroduodenoscopy (EGD). In contrast, non-response was defined as upstaging variceal grading at the 12-month EGD or at least one variceal hemorrhage episode during the 12-month follow-up. We chose cut-off values for univariate and multivariate model with 100% specificity.
RESULTS: According to least absolute shrinkage and selection operator (LASSO) regression, spleen stiffness (SS) and liver stiffness (LS) percentual decrease, along with changes in heart rate (HR) at 3 months were the most significant predictors of NSBB response. A decrease > 11.5% in SS, > 16.8% in LS, and > 25.3% in HR was associated with better prediction of clinical response to NSBB. SS percentual decrease showed the highest accuracy (86.4%) with high sensitivity (78.8%) when compared to LS and HR. The multivariate model incorporating SS, LS, and HR showed the highest discrimination and calibration metrics (AUROC = 0.96), with the optimal cut-off of 0.90 (sensitivity 94.2%, specificity 100%, PPV 95.7%, NPV 100%, accuracy 97.5%).
METHODS: We conducted a prospective study on a cohort of cirrhotic patients with documented HRVs receiving NSBB treatment. Patients were followed-up with clinical and elastography appointments at 3, 6, and 12 months after NSBB treatment initiation. NSBB response was defined as stationary or downstaging variceal grading at the 12-month esophagogastroduodenoscopy (EGD). In contrast, non-response was defined as upstaging variceal grading at the 12-month EGD or at least one variceal hemorrhage episode during the 12-month follow-up. We chose cut-off values for univariate and multivariate model with 100% specificity.
RESULTS: According to least absolute shrinkage and selection operator (LASSO) regression, spleen stiffness (SS) and liver stiffness (LS) percentual decrease, along with changes in heart rate (HR) at 3 months were the most significant predictors of NSBB response. A decrease > 11.5% in SS, > 16.8% in LS, and > 25.3% in HR was associated with better prediction of clinical response to NSBB. SS percentual decrease showed the highest accuracy (86.4%) with high sensitivity (78.8%) when compared to LS and HR. The multivariate model incorporating SS, LS, and HR showed the highest discrimination and calibration metrics (AUROC = 0.96), with the optimal cut-off of 0.90 (sensitivity 94.2%, specificity 100%, PPV 95.7%, NPV 100%, accuracy 97.5%).
摘要：
背景：非选择性β受体阻滞剂（NSBB）用于肝硬化和高危静脉曲张（HRV）患者的初级预防。由于肝静脉压力梯度（HVPG）测量的侵入性，评估治疗反应具有挑战性。本研究旨在定义一种基于非侵入性机器学习的方法，以确定肝硬化和HRV患者对NSBB的反应。
方法：我们对接受NSBB治疗的有记录的HRV的肝硬化患者队列进行了前瞻性研究。NSBB治疗开始后3、6和12个月，对患者进行临床和弹性成像随访。NSBB反应定义为12个月食管胃十二指肠镜检查（EGD）时固定或分期的静脉曲张分级。相比之下,无反应定义为在12个月EGD时静脉曲张分级升级或在12个月随访期间至少发生一次静脉曲张出血.我们选择了具有100%特异性的单变量和多变量模型的截止值。
结果：根据最小绝对收缩和选择运算符（LASSO）回归，脾脏僵硬度(SS)和肝脏僵硬度(LS)百分比下降，3个月时心率（HR）的变化是NSBB反应的最重要预测因素。SS下降>11.5%，LS>16.8%，HR>25.3%与更好地预测NSBB的临床反应相关。与LS和HR相比，SS百分比下降显示出最高的准确性（86.4％）和高灵敏度（78.8％）。结合SS的多变量模型，LS,HR显示出最高的辨别和校准指标(AUROC=0.96)，最佳截止值为0.90(灵敏度为94.2%，特异性100%，PPV95.7%,净现值100%，准确率97.5%)。
方法：我们对接受NSBB治疗的有记录的HRV的肝硬化患者队列进行了前瞻性研究。NSBB治疗开始后3、6和12个月，对患者进行临床和弹性成像随访。NSBB反应定义为12个月食管胃十二指肠镜检查（EGD）时固定或分期的静脉曲张分级。相比之下,无反应定义为在12个月EGD时静脉曲张分级升级或在12个月随访期间至少发生一次静脉曲张出血.我们选择了具有100%特异性的单变量和多变量模型的截止值。
结果：根据最小绝对收缩和选择运算符（LASSO）回归，脾脏僵硬度(SS)和肝脏僵硬度(LS)百分比下降，3个月时心率（HR）的变化是NSBB反应的最重要预测因素。SS下降>11.5%，LS>16.8%，HR>25.3%与更好地预测NSBB的临床反应相关。与LS和HR相比，SS百分比下降显示出最高的准确性（86.4％）和高灵敏度（78.8％）。结合SS的多变量模型，LS,HR显示出最高的辨别和校准指标(AUROC=0.96)，最佳截止值为0.90(灵敏度为94.2%，特异性100%，PPV95.7%,净现值100%，准确率97.5%)。
