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

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.