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基于生物流体力学模型评估犬门静脉压力的计算。

Assessment of a biofluid mechanics-based model for calculating portal pressure in canines.

机构信息

Department of General Surgery, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, No. 639 Zhizaoju Road, Shanghai, 200011, China.

Department of Plastic Surgery, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, No. 639 Zhizaoju Road, Shanghai, 200011, China.

出版信息

BMC Vet Res. 2020 Aug 26;16(1):308. doi: 10.1186/s12917-020-02478-1.

DOI:10.1186/s12917-020-02478-1
PMID:32843036
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7507948/
Abstract

BACKGROUND

Portal hypertension is a severe complication caused by various chronic liver diseases. The standard methods for detecting portal hypertension (hepatic venous pressure gradient and free portal pressure) are available in only a few hospitals due to their technical difficulty and invasiveness; thus, non-invasive measuring methods are needed. This study aimed to establish and assess a novel model to calculate free portal pressure based on biofluid mechanics.

RESULT

Comparison of each dog's virtual and actual free portal pressure showed that a biofluid mechanics-based model could accurately predict free portal pressure (mean difference: -0.220, 95% CI: - 0.738 to 0.298; upper limit of agreement: 2.24, 95% CI: 1.34 to 3.14; lower limit of agreement: -2.68, 95% CI: - 3.58 to - 1.78; intraclass correlation coefficient: 0.98, 95% CI: 0.96 to 0.99; concordance correlation coefficient: 0.97, 95% CI: 0.93 to 0.99) and had a high AUC (0.984, 95% CI: 0.834 to 1.000), sensitivity (92.3, 95% CI: 64.0 to 99.8), specificity (91.7, 95% CI: 61.5 to 99.8), positive likelihood ratio (11.1, 95% CI: 1.7 to 72.8), and low negative likelihood ratio (0.08, 95% CI: 0.01 to 0.6) for detecting portal hypertension.

CONCLUSIONS

Our study suggests that the biofluid mechanics-based model was able to accurately predict free portal pressure and detect portal hypertension in canines. With further research and validation, this model might be applicable for calculating human portal pressure, detecting portal hypertensive patients, and evaluating disease progression and treatment efficacy.

摘要

背景

门静脉高压是由各种慢性肝病引起的严重并发症。由于技术难度和侵入性,检测门静脉高压的标准方法(肝静脉压力梯度和自由门静脉压力)仅在少数几家医院可用;因此,需要非侵入性测量方法。本研究旨在建立和评估一种基于生物流体力学的新模型来计算自由门静脉压力。

结果

比较每只狗的虚拟和实际自由门静脉压力表明,基于生物流体力学的模型可以准确预测自由门静脉压力(平均差值:-0.220,95%置信区间:-0.738 至 0.298;一致性界限上限:2.24,95%置信区间:1.34 至 3.14;一致性界限下限:-2.68,95%置信区间:-3.58 至-1.78;组内相关系数:0.98,95%置信区间:0.96 至 0.99;一致性相关系数:0.97,95%置信区间:0.93 至 0.99),且 AUC(0.984,95%置信区间:0.834 至 1.000)、敏感度(92.3,95%置信区间:64.0 至 99.8)、特异性(91.7,95%置信区间:61.5 至 99.8)、阳性似然比(11.1,95%置信区间:1.7 至 72.8)和阴性似然比(0.08,95%置信区间:0.01 至 0.6)都很高,可用于检测门静脉高压。

结论

我们的研究表明,基于生物流体力学的模型能够准确预测自由门静脉压力并检测犬门静脉高压。通过进一步的研究和验证,该模型可能适用于计算人类门静脉压力、检测门静脉高压患者以及评估疾病进展和治疗效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/0d2a26b74135/12917_2020_2478_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/d8017f33d61c/12917_2020_2478_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/09b5fc62c7b7/12917_2020_2478_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/86eab9b9dc5d/12917_2020_2478_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/004355d213d3/12917_2020_2478_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/91388e3b56f6/12917_2020_2478_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/0d2a26b74135/12917_2020_2478_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/d8017f33d61c/12917_2020_2478_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/09b5fc62c7b7/12917_2020_2478_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/86eab9b9dc5d/12917_2020_2478_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/004355d213d3/12917_2020_2478_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/91388e3b56f6/12917_2020_2478_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2f/7510133/0d2a26b74135/12917_2020_2478_Fig6_HTML.jpg

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EBioMedicine. 2018 Oct;36:151-158. doi: 10.1016/j.ebiom.2018.09.023. Epub 2018 Sep 27.
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