Prediction of Esophageal Varices in Viral Hepatitis C Cirrhosis: Performance of Combined Ultrasonography and Clinical Predictors.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2023-09-15 eCollection Date: 2023-01-01 DOI:10.1155/2023/7938732
Puwitch Charoenchue, Wittanee Na Chiangmai, Amonlaya Amantakul, Wasuwit Wanchaitanawong, Taned Chitapanarux, Suwalee Pojchamarnwiputh
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引用次数: 0

Abstract

Objectives: This study is aimed at evaluating the diagnostic performance of clinical predictors and the Doppler ultrasonography in predicting esophageal varices (EV) in patients with hepatitis C-related cirrhosis and exploring the practical predictors of EV.

Methods: We conducted a prospective study from July 2020 to January 2021, enrolling 65 patients with mild hepatitis C-related cirrhosis. We obtained clinical data and performed grayscale and the Doppler ultrasound to explore the predictors of EV. Esophagogastroduodenoscopy (EGD) was performed as the reference test by the gastroenterologist within a week.

Results: The prevalence of EV in the study was 41.5%. Multivariable regression analysis revealed that gender (female, OR = 4.04, p = 0.02), platelet count (<150000 per ml, OR = 3.13, p = 0.09), splenic length (>11 cm, OR = 3.64, p = 0.02), and absent right hepatic vein (RHV) triphasicity (OR = 3.15, p = 0.03) were significant predictors of EV. However, the diagnostic accuracy indices for isolated predictors were not good (AUROC = 0.63-0.66). A combination of these four predictors increases the diagnostic accuracy in predicting the presence of EV (AUROC = 0.80, 95% CI 0.69-0.91). Furthermore, the Doppler assessment of the right hepatic vein waveform showed good reproducibility (κ = 0.76).

Conclusion: Combining clinical and Doppler ultrasound features can be used as a screening test for predicting the presence of EV in patients with hepatitis C-related cirrhosis. The practical predictors identified in this study could serve as an alternative to invasive EGD in EV diagnosis. Further studies are needed to explore the diagnostic accuracy of additional noninvasive predictors, such as elastography, to improve EV screening.

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Abstract Image

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病毒性丙型肝炎肝硬化食管静脉曲张的预测:联合超声检查和临床预测指标的表现。
目的:本研究旨在评估临床预测指标和多普勒超声在预测丙型肝炎相关肝硬化患者食管静脉曲张(EV)方面的诊断性能,并探索EV的实用预测指标。方法:我们于2020年7月至2021年1月进行了一项前瞻性研究,纳入65名轻度丙型肝炎相关肝硬变患者。我们获得了临床数据,并进行了灰阶和多普勒超声检查,以探索EV的预测因素。胃肠科医生在一周内进行了食管胃十二指肠镜检查(EGD)作为参考测试。结果:研究中EV的患病率为41.5%。多因素回归分析显示,性别(女性,OR=4.04,p=0.02)、血小板计数(p=0.09)、脾脏长度(>11 cm,OR=3.64,p=0.02)和无右肝静脉(RHV)三相性(OR=3.15,p=0.03)是EV的显著预测因素。然而,单独预测因素的诊断准确性指数不好(AUROC=0.63-0.66)。这四个预测因素的组合提高了预测EV存在的诊断准确性(AUROC=0.80,95%CI 0.69-0.91)。此外,结论:结合临床和多普勒超声特征,可以作为预测丙型肝炎相关肝硬化患者EV存在的筛查试验。本研究中确定的实用预测因子可作为EV诊断中侵入性EGD的替代方案。需要进一步的研究来探索其他非侵入性预测因子的诊断准确性,如弹性成像,以改进EV筛查。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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