Potential of the Bayesian approach in critical care

Submitted: 31 December 2023
Accepted: 25 February 2024
Published: 21 March 2024
Abstract Views: 1065
PDF: 122
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Bayesian statistics are becoming increasingly popular in medical data analysis and decision-making. Because of the difficulties that RCTs face in critical care, these methods may be particularly useful. We explain the fundamental concepts and examine recent relevant literature in the field.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Gill CJ, Sabin L, Schmid CH. Why clinicians are natural bayesians. BMJ 2005;330:1080-3. DOI: https://doi.org/10.1136/bmj.330.7499.1080
Yarnell CJ, Abrams D, Baldwin MR, et al. Clinical trials in critical care: can a Bayesian approach enhance clinical and scientific decision making? Lancet Respiratory Med 2021;9:207-16. DOI: https://doi.org/10.1016/S2213-2600(20)30471-9
Solomon T, Hart IJ, Beeching NJ. Viral encephalitis: a clinician's guide. Practical Neurol 2007;7:288. DOI: https://doi.org/10.1136/jnnp.2007.129098
Bartoš F, Aust F, Haaf JM. Informed Bayesian survival analysis. BMC Med Res Methodol 2022;22:238. DOI: https://doi.org/10.1186/s12874-022-01676-9
Kalil AC, Sun J. Bayesian methodology for the design and interpretation of clinical trials in critical care medicine: A primer for clinicians. Critical Care Med 2014;42:2267-77. DOI: https://doi.org/10.1097/CCM.0000000000000576
Hackenberger BK. Bayes or not Bayes, is this the question? Croatian Med J 2019;60:50. DOI: https://doi.org/10.3325/cmj.2019.60.50
Grant DC, Keim SM, Telfer J. Teaching Bayesian analysis to emergency medicine residents. J Emerg Med 2006;31:437-40. DOI: https://doi.org/10.1016/j.jemermed.2006.04.015
Neckebroek M, Ionescu CM, Van Amsterdam K, et al. A comparison of propofol-to-BIS post-operative intensive care sedation by means of target controlled infusion, Bayesian-based and predictive control methods: an observational, open-label pilot study. J Clinical Monitoring Computing 2019;33:675-86. DOI: https://doi.org/10.1007/s10877-018-0208-2
Qiu P, Cui X, Sun J, et al. Antitumor necrosis factor therapy is associated with improved survival in clinical sepsis trials: a meta-analysis. Critical Care Med 2013;41:2419-29. DOI: https://doi.org/10.1097/CCM.0b013e3182982add
Kalil AC. Deciphering the sepsis riddle: We can learn from Star Trek. Critical Care Med 2013;41:2458-460. DOI: https://doi.org/10.1097/CCM.0b013e3182a11ebe
Tomlinson G, Al-Khafaji A, Conrad SA, et al. Bayesian methods: a potential path forward for sepsis trials. Critical Care 2023;27:432. DOI: https://doi.org/10.1186/s13054-023-04717-x
Kwok H, Lewis RJ. Bayesian hierarchical modeling and the integration of heterogeneous information on the effectiveness of cardiovascular therapies. Circulation: Cardiovascular Quality and Outcomes 2011;4:657-66. DOI: https://doi.org/10.1161/CIRCOUTCOMES.111.960724
Kalil AC, Sun J. Low-dose steroids for septic shock and severe sepsis: the use of Bayesian statistics to resolve clinical trial controversies. Intensive Care Med 2011;37:420-9. DOI: https://doi.org/10.1007/s00134-010-2121-0
Harhay MO, Wagner J, Ratcliffe SJ, et al. Outcomes and statistical power in adult critical care randomized trials. Am J Respiratory Critical Care Med 2014;189:1469-78. DOI: https://doi.org/10.1164/rccm.201401-0056CP

How to Cite

Cerantola, C. (2024). Potential of the Bayesian approach in critical care. Acute Care Medicine Surgery and Anesthesia, 2(1). https://doi.org/10.4081/amsa.2024.40