Bayesian hierarchical nonlinear modelling of intra-abdominal volume during pneumoperitoneum for laparoscopic surgery

TL;DR

This paper develops Bayesian hierarchical nonlinear models to analyze the relationship between insufflation pressure and intra-abdominal volume during laparoscopic surgery, aiming to optimize surgical safety and efficacy.

Contribution

It introduces a novel Bayesian nonlinear hierarchical modeling approach for intra-abdominal volume data in laparoscopic surgery.

Findings

Identifies critical insufflation pressure points.

Quantifies intra-abdominal volume growth dynamics.

Provides a statistical framework for surgical planning.

Abstract

Laparoscopy is an operation carried out in the abdomen or pelvis through small incisions with external visual control by a camera. This technique needs the abdomen to be insufflated with carbon dioxide to obtain a working space for surgical instruments' manipulation. Identifying the critical point at which insufflation should be limited is crucial to maximizing surgical working space and minimizing injurious effects. Bayesian nonlinear growth mixed-effects models are applied to data coming from a repeated measures design. This study allows to assess the relationship between the insufflation pressure and the intra--abdominal volume.