Liver Unit, Department of Internal Medicine, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona, Barcelona, Spain.
Liver Unit, Department of Internal Medicine, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona, Barcelona, Spain.
Clin Gastroenterol Hepatol. 2024 Aug;22(8):1637-1645.e9. doi: 10.1016/j.cgh.2023.08.004. Epub 2023 Aug 11.
BACKGROUND & AIMS: Individual risk prediction of liver-related events (LRE) is needed for clinical assessment of nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) patients. We aimed to provide point-of-care validated liver stiffness measurement (LSM)-based risk prediction models for the development of LRE in patients with NAFLD, focusing on selecting patients for clinical trials at risk of clinical events.
Two large multicenter cohorts were evaluated, 2638 NAFLD patients covering all LSM values as the derivation cohort and 679 more advanced patients as the validation cohort. We used Cox regression to develop and validate risk prediction models based on LSM alone, and the ANTICIPATE and ANTICIPATE-NASH models for clinically significant portal hypertension. The main outcome of the study was the rate of LRE in the first 3 years after initial assessment.
The 3 predictive models had similar performance in the derivation cohort with a very high discriminative value (c-statistic, 0.87-0.91). In the validation cohort, the LSM-LRE alone model had a significant inferior discrimination (c-statistic, 0.75) compared with the other 2 models, whereas the ANTICIPATE-NASH-LRE model (0.81) was significantly better than the ANTICIPATE-LRE model (0.79). In addition, the ANTICIPATE-NASH-LRE model presented very good calibration in the validation cohort (integrated calibration index, 0.016), and was better than the ANTICIPATE-LRE model.
The ANTICIPATE-LRE models, and especially the ANTICIPATE-NASH-LRE model, could be valuable validated clinical tools to individually assess the risk of LRE at 3 years in patients with NAFLD/NASH.
需要对非酒精性脂肪性肝病（NAFLD）/非酒精性脂肪性肝炎（NASH）患者进行临床评估，以预测与肝脏相关的事件（LRE）的个体风险。我们旨在提供基于即时检验的肝硬度测量（LSM）的风险预测模型，用于预测 NAFLD 患者发生 LRE 的情况，重点是选择有临床事件风险的患者参加临床试验。
评估了两个大型多中心队列，2638 例 NAFLD 患者涵盖了 LSM 的所有值，作为推导队列，另外 679 例更为严重的患者作为验证队列。我们使用 Cox 回归来开发和验证基于 LSM 的风险预测模型，以及用于临床显著门静脉高压的 ANTICIPATE 和 ANTICIPATE-NASH 模型。本研究的主要结局是初始评估后 3 年内 LRE 的发生率。
在推导队列中，这 3 个预测模型的性能相似，具有很高的区分能力（c 统计值，0.87-0.91）。在验证队列中，与其他 2 个模型相比，LSM-LRE 单独模型的判别能力明显较差（c 统计值，0.75），而 ANTICIPATE-NASH-LRE 模型（0.81）明显优于 ANTICIPATE-LRE 模型（0.79）。此外，ANTICIPATE-NASH-LRE 模型在验证队列中具有很好的校准度（综合校准指数，0.016），并且优于 ANTICIPATE-LRE 模型。
ANTICIPATE-LRE 模型，特别是 ANTICIPATE-NASH-LRE 模型，可以作为有价值的验证性临床工具，用于单独评估 NAFLD/NASH 患者 3 年内发生 LRE 的风险。
