Numerical simulation and analysis of risk factors leading to pancreaticobiliary reflux: Insights from a computational...

The analysis and prediction of pancreaticobiliary reflux (PBR) play a crucial role in planning surgical interventions for hepato-biliary-pancreatic diseases, considering the uncertain mechanism behind it. However, current practices are limited by fragmented clinical observations, making it challenging to visualize the complex phenomenon in the pancreaticobiliary junction (PBJ) through imaging and radiography experiments. This study aimed to comprehensively describe the retrograde flow characteristics in various PBR scenarios and assess the factors leading to PBR using simulations based on idealized geometry and boundary conditions.

By Cadence Pointwise, we developed a computational fluid dynamics (CFD) model using an idealized PBJ system. Standard parameters such as pressure and viscosity were applied, along with typical assumptions relevant to fluid dynamic modeling. Subsequently, based on the aforementioned basic idealized model, we analyzed 8 hypothetical PBR conditions, covering a range of high (shorter) and low (longer) values or different positions for each specific parameter, at a representative stage of a peristaltic propagation cycle of the Oddi's sphincter.

We modeled a two-dimensional PBJ with the propagation of a peristaltic wave. These findings demonstrated that the shortened septum, the extended ampulla, the increased wavelength and enhanced amplitude of the Oddi's sphincterial peristalsis, the widened diameter difference and the increased pressure difference between the common bile duct (CBD) and the main pancreatic duct (MPD), as well as the gravitational effect (position), strongly impacted PBR, while the viscosity of bile and pancreatic juice had a weaker influence. Additionally, an inequality incorporating these risk factors was developed for the evaluation of whether reflux occurs.

Numerical simulation can be used to describe the reflux flow field, offering the possibility to visualize and analyze PBR, which has the potential to significantly revolutionize the understanding of PBR and improve clinical decision-making. Future work should focus on bridging the gap between CFD and clinical practice.