Electrocardiography Unit, Instituto Dante Pazzanese de Cardiologia, Avenida Dr. Dante Pazzanese No. 500, Vila Mariana, São Paulo 04012-091, Brazil.
J Electrocardiol. 2023 Nov-Dec;81:295-299. doi: 10.1016/j.jelectrocard.2023.10.006. Epub 2023 Oct 17.
Electrocardiograms (ECGs) are a cornerstone in cardiac care. Traditional statistical metrics like sensitivity and specificity are commonly used for diagnostic evaluations but are limited when applied in clinical settings due to their inability to incorporate pre-test likelihoods or individual patient context. Traditional diagnostic metrics do not provide a complete picture in clinical scenarios. Bayesian reasoning allows for a more nuanced approach, integrating pre-test probabilities and individual patient context to produce more accurate post-test probabilities. This was demonstrated through Bayesian analysis of four clinical cases. Bayesian reasoning enhances diagnostic accuracy and personalizes patient care by integrating prior probabilities into diagnostic decision-making. This shift toward Bayesian reasoning is crucial for improving patient outcomes in the era of evidence-based medicine.
心电图（ECG）是心脏护理的基石。传统的统计指标，如敏感性和特异性，常用于诊断评估，但由于其无法纳入预测试验可能性或个体患者背景，在临床环境中应用受到限制。传统的诊断指标在临床情况下无法提供完整的信息。贝叶斯推理允许采用更细致的方法，将预测试验概率和个体患者背景结合起来，生成更准确的后测试概率。这通过对四个临床病例的贝叶斯分析得到了证明。贝叶斯推理通过将先验概率纳入诊断决策过程，提高了诊断准确性并实现了患者护理的个性化。在循证医学时代，这种向贝叶斯推理的转变对于改善患者结局至关重要。
