评估 8 种非侵入性模型预测 MASLD 和进展的疗效：一项前瞻性研究。
Evaluating the efficacy of 8 non-invasive models in predicting MASLD and progression: a prospective study.
机构信息
Phase I Clinical Research Center, First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130021, China.
Department of Radiology, First Hospital of Jilin University, Changchun, China.
出版信息
BMC Gastroenterol. 2024 Oct 14;24(1):365. doi: 10.1186/s12876-024-03449-8.
BACKGROUND
Selecting the optimal non-invasive diagnostic model for MASLD (Metabolic Dysfunction-Associated Steatosis Liver Disease) and steatosis progression is a critical issue given the variety of available models. We aimed to compare the performance of eight clinical prediction models for diagnosing and predicting the progression of hepatic steatosis using MRI-PDFF (Magnetic Resonance Imaging-Derived Proton Density Fat Fraction), and validate the findings with FibroScan and histopathological results.
METHODS
In this study, 846 participants were initially enrolled, with 108 undergoing liver biopsy and 706 completing one-year follow-up, including 26 who underwent repeat biopsy. We calculated scores for eight clinical prediction models (FAST, KNAFLD, HSI, FLI, Liver Fat Score, Liver Fat Equation, BAAT, LAP) using collected clinical data and defined steatosis progression as a 30% relative increase in liver fat content (LFC) measured by MRI-PDFF. CAP(Controlled Attenuation Parameter) and LSM (Liver Stiffness Measurement) were obtained by Fibroscan. MRI-PDFF served as the reference standard for evaluating model accuracy, and sensitivity analyses were performed using liver biopsy and Fibroscan results.
RESULTS
Among the eight clinical models, NAS (nonalcoholic fatty liver disease activity score) showed higher correlation with the FAST and KNAFLD models (r: 0.62 and 0.52, respectively). Among the whole cohort (N = 846), KNAFLD was the best model for predicting different degrees of hepatic steatosis (AUC = 0.84). When the KNAFLD score was above 2.935, LFC was significantly higher (4.4% vs. 19.7%, P < 0.001). After 1 year of follow-up (N = 706), FAST performed best in predicting MASLD progression (AUC = 0.84); with dFAST > -0.02, LFC increased (8.6-10.9%, P < 0.05), mean LSM increased by 0.51 kPa, and with dFAST < -0.02, LFC significantly decreased (11.5-8.5%, P < 0.05), mean LSM and NAS decreased by 0.87 kPa and 0.76, respectively (both P < 0.05).
CONCLUSIONS
Most models demonstrated good diagnostic and prognostic capabilities for hepatic steatosis, with FAST and KNAFLD showing particular promise as primary non-invasive tools in clinical practice.
TRAIL REGISTRATION
Chinese Clinical Trial Registry NO: ChiCTR2100054743, Registered December 26, 2021.
背景
鉴于目前有多种可供选择的模型，选择最佳的非侵入性诊断 MASLD（代谢相关脂肪性肝病）和脂肪变性进展模型是一个关键问题。我们旨在比较八种临床预测模型在使用 MRI-PDFF（磁共振成像衍生质子密度脂肪分数）诊断和预测肝脂肪变性进展方面的性能，并结合 FibroScan 和组织病理学结果进行验证。
方法
本研究最初纳入 846 名参与者，其中 108 名接受肝活检，706 名完成一年随访，包括 26 名接受重复活检。我们使用收集到的临床数据计算了八种临床预测模型（FAST、KNAFLD、HSI、FLI、肝脂肪评分、肝脂肪方程、BAAT、LAP）的评分，并将 MRI-PDFF 测量的肝脂肪含量（LFC）相对增加 30%定义为脂肪变性进展。CAP（受控衰减参数）和 LSM（肝脏硬度测量）由 Fibroscan 获得。MRI-PDFF 被用作评估模型准确性的参考标准，并使用肝活检和 Fibroscan 结果进行了敏感性分析。
结果
在这八种临床模型中，NAS（非酒精性脂肪性肝病活动评分）与 FAST 和 KNAFLD 模型的相关性更高（r 值分别为 0.62 和 0.52）。在整个队列（N=846）中，KNAFLD 是预测不同程度肝脂肪变性的最佳模型（AUC=0.84）。当 KNAFLD 评分高于 2.935 时，LFC 显著升高（4.4%对 19.7%，P<0.001）。在 1 年随访后（N=706），FAST 在预测 MASLD 进展方面表现最佳（AUC=0.84）；dFAST>-0.02 时，LFC 增加（8.6-10.9%，P<0.05），平均 LSM 增加 0.51 kPa，dFAST<-0.02 时，LFC 显著降低（11.5-8.5%，P<0.05），平均 LSM 和 NAS 分别降低 0.87 kPa 和 0.76（均 P<0.05）。
结论
大多数模型对肝脂肪变性具有良好的诊断和预后能力，FAST 和 KNAFLD 作为临床实践中的主要非侵入性工具具有特殊的应用前景。
试验注册
中国临床试验注册中心注册号：ChiCTR2100054743，登记日期：2021 年 12 月 26 日。
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