Division of Gastroenterology and Hepatology, Medical University of Vienna, Vienna, Austria; Vienna Hepatic Hemodynamic Lab, Medical University of Vienna, Vienna, Austria; Department of Medicine III and Clinical Research Group Mechanisms in Portal Hypertension, Medical University of Vienna, Vienna, Austria.
Gastroenterology and Hepatology Department, Clinical and Translational Research in Digestive Diseases, Valdecilla Research Institute, Marqués de Valdecilla University Hospital, Santander, Spain.
Lancet Gastroenterol Hepatol. 2024 Dec;9(12):1111-1120. doi: 10.1016/S2468-1253(24)00234-6. Epub 2024 Sep 23.
In patients with compensated advanced chronic liver disease (cACLD), risk of clinically significant portal hypertension (CSPH) can be estimated by applying non-invasive tests such as liver stiffness measurement (LSM), platelet count, and, in some cases, BMI. We aimed to assess the diagnostic utility of spleen stiffness measurement (SSM) at 100 Hz as a standalone non-invasive test for CSPH and to evaluate its incremental value compared with the ANTICIPATE±NASH model in patients with cACLD.
For this modelling study, patients were recruited from 16 expert centres in Europe. Patients who underwent characterisation by hepatic venous pressure gradient (HVPG) and non-invasive tests (ie, LSM, platelet count, and SSM at 100 Hz) at one of the participating centres between Jan 1, 2020, and Dec 31, 2023, were considered for inclusion. Only patients aged 18 years or older with Child-Pugh class A cACLD, shown by LSM 10 kPa or more or F3 or F4 fibrosis on liver histology, were included. The overall cohort was split into the derivation cohort (patients recruited between Jan 1, 2020, and Dec 31, 2022) and the temporal validation cohort (patients recruited between Jan 1, 2023, and Dec 31, 2023). The ANTICIPATE±NASH model was applied to assess individual CSPH probability and SSM was investigated as a standalone non-invasive test for CPSH; in combination with platelet count and BMI; and in a full model of SSM, LSM, platelet count, and BMI (ie, the Non-Invasive CSPH Estimated Risk [NICER] model). All models were binary logistic regression models. The primary outcome was CSPH. We evaluated the discriminative utility of the models by calculating the area under the receiver operating characteristics curve (AUC) and creating calibration plots and calibration of intercept, slope, and integrated calibration index.
407 patients with cACLD were included, 202 (50%) in the derivation cohort and 205 (50%) in the validation cohort. Median age was 60·0 years (IQR 55·0-66·8); 275 (68%) of 407 patients were male and 132 (32%) were female. 164 (40%) of 407 patients had metabolic dysfunction-associated steatotic liver disease (MASLD), 133 (33%) had MASLD with increased alcohol intake or alcohol-related liver disease, 75 (18%) had viral hepatitis (61 [81%] of whom had sustained virologic response of hepatitis C virus or suppression of hepatitis B virus DNA), and 35 (9%) had other chronic liver diseases. 241 (59%) patients had CSPH. Median SSM was 45·0 kPa (32·1-65·4) and LSM was 21·4 kPa (14·1-31·6). SSM and LSM had similar AUCs for prediction of CSPH in the derivation cohort (0·779 [95% CI 0·717-0·842] vs 0·781 [0·718-0·844]; p=0·97) and in the validation cohort (0·830 [0·772-0·887] vs 0·804 [0·743-0·864]; p=0·50). The SSM-based model comprising platelet count and BMI had a similar AUC as the ANTICIPATE±NASH model in both the derivation cohort (0·849 [0·794-0·903] vs 0·849 [0·794-0·903]; p=0·999) and in the validation cohort (0·873 [0·819-0·922] vs 0·863 [0·810-0·916]; p=0·75). The NICER model had a significantly higher AUC for prediction of CSPH than the ANTICIPATE±NASH model in the derivation cohort (0·889 [0·843-0·934] vs 0·849 [0·794-0·903]; p=0·022) and in the validation cohort (0·906 [0·864-0·946] vs 0·863 [0·810-0·916]; p=0·012).
The addition of SSM to LSM, BMI, and platelet count outperformed the ANTICIPATE±NASH model for CSPH risk stratification in our cohort of contemporary patients with cACLD. SSM improves the non-invasive diagnosis of CSPH, supporting its implementation into clinical practice.
Echosens.
在代偿性晚期慢性肝病(cACLD)患者中,可通过肝硬度测量(LSM)、血小板计数等非侵入性检查,以及在某些情况下通过体重指数(BMI),评估临床显著门静脉高压(CSPH)的风险。我们旨在评估 100 Hz 脾脏硬度测量(SSM)作为 CSPH 的独立非侵入性检测的诊断效用,并评估其在 cACLD 患者中与 ANTICIPATE±NASH 模型相比的增量价值。
这项建模研究招募了来自欧洲 16 个专家中心的患者。在参与中心之一,于 2020 年 1 月 1 日至 2023 年 12 月 31 日之间,对接受肝静脉压力梯度(HVPG)和非侵入性检查(即 LSM、血小板计数和 100 Hz SSM)的患者进行了特征描述,符合纳入标准的患者被纳入研究。仅纳入年龄在 18 岁及以上、LSM 10 kPa 或更高、或肝组织学显示 F3 或 F4 纤维化的 Child-Pugh 分级 A cACLD 患者。整个队列分为推导队列(2020 年 1 月 1 日至 2022 年 12 月 31 日招募的患者)和时间验证队列(2023 年 1 月 1 日至 2023 年 12 月 31 日招募的患者)。应用 ANTICIPATE±NASH 模型评估个体 CSPH 概率,将 SSM 作为 CPSH 的独立非侵入性检测进行研究;并与血小板计数和 BMI 结合;以及在 SSM、LSM、血小板计数和 BMI 的全模型中(即,非侵入性 CSPH 估计风险[ NICER ]模型)。所有模型均为二元逻辑回归模型。主要结局为 CSPH。我们通过计算接收者操作特征曲线(ROC)的曲线下面积(AUC)、绘制校准图和校准截距、斜率和综合校准指数,评估模型的鉴别能力。
纳入了 407 例 cACLD 患者,其中 202 例(50%)来自推导队列,205 例(50%)来自验证队列。中位年龄为 60.0 岁(IQR 55.0-66.8);407 例患者中,275 例(68%)为男性,132 例(32%)为女性。164 例(40%)患有代谢功能相关脂肪性肝病(MASLD),133 例(33%)患有 MASLD 合并酒精摄入增加或酒精性肝病,75 例(18%)患有病毒性肝炎(61 例[81%]为丙型肝炎病毒持续病毒学应答或乙型肝炎病毒 DNA 抑制),35 例(9%)患有其他慢性肝病。241 例(59%)患者存在 CSPH。中位 SSM 为 45.0 kPa(32.1-65.4),LSM 为 21.4 kPa(14.1-31.6)。SSM 和 LSM 对推导队列(0.779 [95%CI 0.717-0.842] vs 0.781 [0.718-0.844];p=0.97)和验证队列(0.830 [0.772-0.887] vs 0.804 [0.743-0.864];p=0.50)的 CSPH 预测均具有相似的 AUC。基于 SSM 的模型,包含血小板计数和 BMI,与推导队列和验证队列中的 ANTICIPATE±NASH 模型具有相似的 AUC(0.849 [0.794-0.903] vs 0.849 [0.794-0.903];p=0.999)和(0.873 [0.819-0.922] vs 0.863 [0.810-0.916];p=0.75)。在推导队列(0.889 [0.843-0.934] vs 0.849 [0.794-0.903];p=0.022)和验证队列(0.906 [0.864-0.946] vs 0.863 [0.810-0.916];p=0.012)中,NICER 模型对 CSPH 预测的 AUC 显著高于 ANTICIPATE±NASH 模型。
在我们这组当代 cACLD 患者中,SSM 联合 LSM、BMI 和血小板计数用于 CSPH 风险分层的效果优于 ANTICIPATE±NASH 模型。SSM 提高了 CSPH 的非侵入性诊断,支持将其纳入临床实践。
Echosens。