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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Revista:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Año de publicación: 2021

Volumen: 45

Número: 2

Páginas: 143-162

Tipo: Artículo

DOI: 10.2436/20.8080.02.113 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Sort: Statistics and Operations Research Transactions

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.

Referencias bibliográficas

  • Becker, C., Plymale, M. A., Wennergren, J., Totten, C., Stigall, K., Roth, J. S. (2017) Compliance of the abdominal wall during laparoscopic insuffation. Surgical Endoscopy, 31, 1947–1951
  • Box, G. E. P. (1980). Sampling and Bayes’s inference in scientifc modelling and robustness (with discussion). Journal of the Royal Statistical Society, Series A, 143, 383-430.
  • Chen, M.-H., Shao, Q.-M., and Ibrahim, J. G. (2000). Monte Carlo Methods in Bayesian Computation. New York: Springer.
  • Colon Cancer Laparoscopic or Open Resection Study Group; Buunen, M., Veldkamp, R., Hop, W. C., Kuhry, E., Jeekel, J., Haglind, E., et al. (2009). Survival after laparoscopic surgery versus open surgery for colon cancer: long-term outcome of a randomised clinical trial. The Lancet Oncology 10(1), 44–52.
  • Cramer, J. S. (2004). The early origins of the logit model. Studies in History and Philosophy of Biological and Biomedical Sciences 35, 613–626.
  • Davidian, M. (2008). Non-linear mixed-effects model. In Longitudinal data analysis. Chapman and Hall/CRC. p. 121–156.
  • Diaz-Cambronero, O., Flor Lorente, B., Mazzinari, G., Vila Montañes´ M, Garcı́a Gregorio, N,, Robles Hernández, D. et al. (2020). A multifaceted individualized pneumoperitoneum strategy for laparoscopic colorectal surgery: a multicenter observational feasibility study. Surgical Endoscopy, 33, 252–260.
  • Diaz-Cambronero, O., Mazzinari, G., Flor Lorente, B., Garcı́a Gregorio, N., RoblesHernández, D., Olmedilla Arnal, L. E. et al. (2020) Effect of an individualized versus standard pneumoperitoneum pressurestrategy onpostoperative recovery: a randomized clinical trial in laparoscopic colorectal surgery. British Journal of Surgery, 107, 1605–1614.
  • Fang, Z. and Bailey, R. L. (2001. Nonlinear Mixed Effects Modeling for Slash Pine Dominant Height Growth Following Intensive Silvicultural Treatments. Forest Science, 47, 287–300.
  • Forstemann, T., Trzewik, J., Holste, J., Batke, B., Konerding, M. A., Wolloscheck, T., Hartung C. (2011). Forces and deformations of the abdominal wall–a mechanical and geometrical approach to the linea alba. Journal of Biomechanics, 44, 600–606.
  • Funatogawa, I., Funatogawa, T. (2018). Nonlinear Mixed Effects Models, Growth Curves, and Autoregressive Linear Mixed Effects Models. In: JSS Research Series in Statistics (ed). Longitudinal Data Analysis, pp 99–117. Springer, Singapore.
  • Gelfand, A. E., Dey, D. K., and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods. In Bayesian Statistics 4 (Eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith). Oxford: Oxford University Press, 165–180.
  • Gelfand, A. E. (1996). Model determination using sampling-based methods. In Markov Chain Monte Carlo in Practice 4 (Eds. W. Gilks, S. Richardson, and D. Spiegelhalter). Chapman & Hall, 145–161.
  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. (2014). Bayesian Data Analysis, Third Edition. Boca Raton: Chapman and Hall.
  • Giltinan, D. M. (2006). Pharmacokinetics and pharmacodynamics. In P. Armitageand T. Colton (eds), Encyclopedia of Biostatistics, 2nd ed,, pp. 600–606. Wiley, Hoboken, NJ.
  • Laird, N. M. and Ware, J. H. (1982). Random-Effects for Longitudinal Data. Biometrics, 38, 963–974.
  • Lee, Y. and Nelder, J. A. (2004). Conditional and Marginal Models: Another View . Statistical Science, 19(2), 219–238.
  • Lindsey, J. K. (2001). Nonlinear Models in Medical Statistics. Oxford University Press, Oxford.
  • Lindstrom, M. J. and Bates, D. M. (1990). Nonlinear Mixed Effects Models for Repeated Measures Data. Biometrics, 46(3), 673–687.
  • Loredo, T. J. (1989) From laplace to supernova sn 1987a: Bayesian inference in astrophysics. In: Fougère PF (ed). Maximum entropy and Bayesian methods, pp 81–142. Kluwer Academic publishers, Dordrecht.
  • Loredo, T. J. (1992) Promise of Bayesian inference for astrophysics. In: Feigelson E, Babu G (eds). Statistical challenges in modern astronomy, pp 275–297. Springer, New York.
  • Mazzinari, G., Diaz-Cambronero, O., Alonso-Iñigo, J. M., Garcı́a Gregorio, N., AyasMontero, B. et al. (2020). Intraabdominal pressure targeted positive end-expiratory pressure during laparoscopic surgery: an open-label, nonrandomized, crossover, clinical trial. Anesthesiology, 132, 667–677.
  • Mazzinari, G., Diaz-Cambronero, O., Serpa Neto, A., Martı́nez Cañada, A., and Rovira, L., Argente Navarro, M. P., et al. (2021). Modeling intra-abdominal volume and respiratory driving pressure during pneumoperitoneum insuffation − a patient-level data meta-analysis. Journal of Applied Physiology 130(3), 721–728.
  • Mulier, J., Dillemans, B., Crombach, M., Missant, C., Sels, A. (2009). On the abdominal pressure volume relationship. The Internet Journal of Anesthesiology 21, 1–7. 5221.
  • Neudecker, J., Sauerland, S., Neugebauer, E. A. M. et al. (2002) The EAES clinical practice guidelines on the pneumoperitoneum for laparoscopic surgery. Surgical Endoscopy, 16(7), 1121–43
  • Neugebauer, E. A. M, Becker, M., Buess, G. F. et al. (2010). EAES recommendations on methodology of innovation management in endoscopic surgery. Surgical Endoscopy, 24(7), 1594–1615.
  • Ntzoufras, I. (2011). Bayesian modeling using WinBUGS. John Wiley & Sons.
  • Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 6464 (366), 447–453.
  • Pache, B., Hübner, M., Jurt, J., Demartines, N., and Grass, F. (2017). Minimally invasive surgery and enhanced recovery after surgery: the ideal combination? Journal of Surgical Oncology, 116(5), 613–616.
  • Peek, M. S., Russek-Cohen, E., Wait, D. A. and Forseth, I. N. (2002). Physiological response curve analysis using nonlinear mixed models. Oecologia, 132, 175–180.
  • Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd international workshop on distributed statistical computing. p. 1–10.
  • Senn, S. (2016). Mastering variation: variance components personalised medicine. Statistics in Medicine, 30(7), 966–977.
  • Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and Van Der Linde, A. (2002). Bayesian measures of model complexity and ft. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64, 583-639.
  • Vehtari, A. and Ojanen, J. (2012). A survey of Bayesian predictive methods for model assessment, selection and comparison. Journal of Biomechanics, 6, 142–228.
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