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

• Gabriel Calvo ^[1] ; Carmen Armero ^[1] ; Virgilio Gómez-Rubio ^[2] ; Guido Mazzinari ^[3]
  1. [1] Universitat de València

     Universitat de València

     Valencia, España

     
  2. [2] Universidad de Castilla-La Mancha

     Universidad de Castilla-La Mancha

     Ciudad Real, España

     
  3. [3] Hospital Universitari i Politècnic la Fe, Valencia,

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• Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 45, Nº. 2, 2021, págs. 143-162
• Idioma: inglés
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• Resumen

  • Laparoscopy is an operation carried out in the abdomen through small incisions with 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. A Bayesian nonlinear growth mixed-effects model for the relationship between the insufflation pressure and the intra–abdominal volume generated is discussed as well as its plausibility to represent the data.