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

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 insufflatio...

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Detalhes bibliográficos
Autores: Calvo, Gabriel, Armero, Carmen, Gómez-Rubio, Virgilio, Mazzinari, Guido
Formato: artículo
Fecha de publicación:2021
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/397821
Acesso em linha:https://hdl.handle.net/2117/397821
https://dx.doi.org/10.2436/20.8080.02.113
Access Level:acceso abierto
Palavra-chave:Mathematical statistics
intra-abdominal pressure
logistic growth function
Markov chain
Monte Carlo methods
random effects
62F Inferència paramètrica
62P Aplicacions
Classificació AMS::62 Statistics::62P Applications
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
Descrição
Resumo: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.