Bayesian Decision Analysis: An Underutilized Tool in Veterinary Medicine.
Bayesian Decision Analysis: An Underutilized Tool in Veterinary Medicine.
复制标题
DOI：
10.3390/ani12233414
复制
发表时间：
2022-12-04
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
兽医学中的决策可能极具难度。通常，不同的选择会伴随着差异巨大的成本、并发症及结果。贝叶斯推理和决策分析这两种工具相结合，能够帮助临床兽医和宠物主人选定更优的行动方案。在这项回顾性病例研究中，我们描述了一只精神萎靡且不再进食的雪貂。我们征求了三位未参与该病例的兽医专家对一系列诊断测试前后可能诊断结果的意见。我们还请最初的临床团队评估不同临床结果的价值。通过整合这些数据，我们得以判断最初的临床团队为这只动物实施手术的决策是否正确。我们还探讨了在诊断中不使用贝叶斯推理的一些弊端、在病例管理决策中可能起作用的一些认知偏差，以及决策分析方法在促进客户与兽医共同决策方面更广泛的效用。
贝叶斯推理和决策分析可用于确定最有可能的鉴别诊断，并利用这些概率在多种备选方案中确定最佳的诊断或治疗选择。在这项回顾性病例分析中，针对一只因精神萎靡和厌食前来就诊的雪貂，我们就几种鉴别诊断的先验概率（考虑其特征和病史）以及不同临床检查结果（体格检查、血液检查、影像学检查等）在给定诊断下的条件概率，对三位专家进行了调查。利用这些数据以及其他临床医生提供的效用估计，我们构建了一个决策树，以回顾性地确定在剖腹探查术和药物治疗之间的最佳治疗选择。我们确定药物治疗为最佳选择，这与最初实施剖腹探查术的临床团队的决策不同。我们讨论了最初临床团队可能存在的认知偏差。我们还探讨了贝叶斯决策分析在兽医临床中的优势（如共同决策）和局限性。贝叶斯决策分析对于回顾性病例分析和前瞻性决策制定，尤其是在决定侵入性干预措施或临终关怀方面，可能是一个有用的工具。专家得出的概率估计存在差异，这使得贝叶斯决策分析的应用颇具挑战性，在像动物医学这样广泛的专业领域尤其如此。
Decision making in veterinary medicine can be extremely difficult. Often, different choices can have vastly different costs, complications, and outcomes associated with them. Bayesian inference and decision analysis are two tools that, when combined, can help clinicians and pet owners decide on the preferred course of action. In this retrospective case study, we describe a lethargic ferret that is no longer eating. We solicited opinions from three expert veterinarians who were not involved with the case on what the diagnosis could be before and after a series of diagnostic tests. We also asked the original clinical team to estimate how valuable different clinical outcomes were. By combining these data, we were able to assess if the original clinical team was right to take the animal to surgery. We also discuss some of the pitfalls of not using Bayesian inference in diagnosis, some cognitive biases that may have played a role in the case management decisions, and the wider usefulness of decision-analysis methods to help foster shared decision making between client and veterinarian. Bayesian inference and decision analysis can be used to identify the most probable differential diagnosis and use those probabilities to identify the best choice of diagnostic or treatment among several alternatives. In this retrospective case analysis, we surveyed three experts on the prior probability of several differential diagnoses, given the signalment and history of a ferret presenting for lethargy and anorexia, and the conditional probability of different clinical findings (physical, bloodwork, imaging, etc.), given a diagnosis. Using these data and utility estimates provided by other clinicians, we constructed a decision tree to retrospectively identify the optimal treatment choice between exploratory laparotomy and medical management. We identified medical management as the optimal choice, in contrast to the original clinical team which performed an exploratory laparotomy. We discuss the potential cognitive biases of the original clinical team. We also discuss the strengths, e.g., shared decision making, and limitations of a Bayesian decision analysis in the veterinary clinic. Bayesian decision analysis can be a useful tool for retrospective case analysis and prospective decision making, especially for deciding on invasive interventions or end-of-life care. The dissimilarity of expert-derived probability estimates makes Bayesian decision analysis somewhat challenging to apply, particularly in wide-ranging specialties like zoological medicine.
影响因子：
1.6
作者：
KASSIRER, JP;KOPELMAN, RI
通讯作者：
KOPELMAN, RI
影响因子：
158.5
作者：
PAUKER, SG;KASSIRER, JP
通讯作者：
KASSIRER, JP
影响因子：
158.5
作者：
PAUKER, SG;KASSIRER, JP
通讯作者：
KASSIRER, JP