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

• Main Authors: Jia-Yun Lin, Chi-Hao Zhang, Lei Zheng, Chen-Lu Song, Wen-Sheng Deng, Yi-Ming Zhu, Li Zheng, Li-Zhong Wu, Long-Ci Sun, Meng Luo
• Format: Article
• Language: English
• Published: BMC 2020-08-01
• Series: BMC Veterinary Research
• Subjects: Biofluid mechanics; Non-invasive; Portal hypertension; Portal pressure
• Online Access: http://link.springer.com/article/10.1186/s12917-020-02478-1

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.