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

  1. Gabriel Calvo 1
  2. Carmen Armero 1
  3. Virgilio Gómez-Rubio 2
  4. Guido Mazzinari 3
  1. 1 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

  2. 2 Universidad de Castilla-La Mancha
    info

    Universidad de Castilla-La Mancha

    Ciudad Real, España

    ROR https://ror.org/05r78ng12

  3. 3 Hospital Universitari i Politècnic la Fe, Valencia,
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Zeitschrift:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Datum der Publikation: 2021

Ausgabe: 45

Nummer: 2

Seiten: 143-162

Art: Artikel

DOI: 10.2436/20.8080.02.113 DIALNET GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: Sort: Statistics and Operations Research Transactions

Zusammenfassung

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.

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