Potential of the Bayesian approach in critical care

Submitted: 31 December 2023
Accepted: 25 February 2024
Published: 21 March 2024
Abstract Views: 956
PDF: 56
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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.

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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