Utility of Ultrasound-Guided Attenuation Parameter for Grading Steatosis With Reference to MRI-PDFF in a Large Cohort
Utility of Ultrasound-Guided Attenuation Parameter for Grading Steatosis With Reference to MRI-PDFF in a Large Cohort
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DOI：
10.1016/j.cgh.2021.11.003
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发表时间：
2022-10-25
影响因子：
12.6
通讯作者：
Kumada, Takashi
中科院分区：
文献类型：
--
作者：
Imajo, Kento;Toyoda, Hidenori;Kumada, Takashi
背景与目的：超声引导衰减参数（UGAP）是近年来发展起来的一种无创性评价脂肪变性的方法。然而，关于其在临床实践中的有用性的报告是有限的。这项前瞻性多中心研究分析了参考磁共振成像的质子密度脂肪分数（MRI-PDFF），一种非侵入性的方法，具有较高的准确性，在一个大的coherent.METHODS：共1010例慢性肝病患者接受了MRI-PDFF和UGAP招募和前瞻性招募6个日本肝脏中心的脂肪变性分级的诊断准确性。使用MRI-PDFF和UGAP值之间的组内相关系数评价线性。通过Bland-Altman分析评估偏倚，偏倚定义为MRI-PDFF和UGAP值之间的平均差异。采用受试者工作特征曲线下面积（AUROC）分析确定基于成对MRI-PDFF的脂肪变性分级的UGAP截止值。然而，由于PDFF值不呈正态分布，因此对其进行对数转换（MRI-logPDFF）。UGAP值与MRI-logPDFF显著相关（组内相关系数= 0.768）。此外，BlandAltman分析显示MRI-logPDFF和UGAP之间具有良好的一致性，平均偏倚为0.0002%，一致性范围较窄（95%置信区间[CI]，-0.015至0.015）。用于区分脂肪变性等级>= 1的AUROC（MRI-PDFF >= 5.2%），>= 2（MRI-PDFF >= 11.3%），3（MRI-PDFF >= 17.1%）为0.910（95% CI，0.891-0.928），0.912（95% CI，0.894-0.929）和0.894结论：UGAP与MRI-PDFF相比，对脂肪变性的分级诊断准确性较高。此外，UGAP具有良好的线性和可忽略的偏倚，表明UGAP具有优异的技术性能特征，可广泛用于临床试验和患者护理。
BACKGROUND & AIMS: Ultrasound-guided attenuation parameter (UGAP) is recently developed for noninvasive evaluation of steatosis. However, reports on its usefulness in clinical practice are limited. This prospective multicenter study analyzed the diagnostic accuracy of grading steatosis with reference to magnetic resonance imaging-based proton density fat fraction (MRI-PDFF), a noninvasive method with high accuracy, in a large cohort.METHODS: Altogether, 1010 patients with chronic liver disease who underwent MRI-PDFF and UGAP were recruited and prospectively enrolled from 6 Japanese liver centers. Linearity was evaluated using intraclass correlation coefficients between MRI-PDFF and UGAP values. Bias, defined as the mean difference between MRI-PDFF and UGAP values, was assessed by Bland-Altman analysis. UGAP cutoffs for pairwise MRI-PDFF-based steatosis grade were determined using area under the receiver-operating characteristic curve (AUROC) analyses.RESULTS: UGAP values were shown to be normally distributed. However, because PDFF values were not normally distributed, they were log-transformed (MRI-logPDFF). UGAP values significantly correlated with MRI-logPDFF (intraclass correlation coefficient = 0.768). Additionally, BlandAltman analysis showed good agreement between MRI-logPDFF and UGAP with a mean bias of 0.0002% and a narrow range of agreement (95% confidence interval [CI], -0.015 to 0.015). The AUROCs for distinguishing steatosis grade >= 1 (MRI-PDFF >= 5.2%), >= 2 (MRI-PDFF >= 11.3%), and 3 (MRI-PDFF >= 17.1%) were 0.910 (95% CI, 0.891-0.928), 0.912 (95% CI, 0.894-0.929), and 0.894 (95% CI, 0.873-0.916), respectively.CONCLUSIONS: UGAP has excellent diagnostic accuracy for grading steatosis with reference to MRI-PDFF. Additionally, UGAP has good linearity and negligible bias, suggesting that UGAP has excellent technical performance characteristics that can be widely used in clinical trials and patient care.