The Bayesian approach improves the electrocardiographic diagnosis of broad complex tachycardia
The Bayesian approach improves the electrocardiographic diagnosis of broad complex tachycardia
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DOI：
10.1046/j.1460-9592.2000.01519.x
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发表时间：
2000-10-01
影响因子：
1.8
通讯作者：
Griffith, MJ
中科院分区：
文献类型：
--
作者：
Lau, EW;Pathamanathan, RK;Griffith, MJ
尽管在设计用于基于心电图（ECG）诊断宽波心动过速（BCT）的算法方面进行了许多尝试，但误诊仍然很常见。其原因可能在于在实践中难以实现现有的算法，由于不完善的确定的心电图特征。试图重新接近问题的贝叶斯推理的建设围绕似然比（LR）的诊断算法。先前研究的ECG特征根据其LR值最有效地区分室性心动过速（VT）和室上性心动过速伴异常传导（SVTAC），被选择纳入贝叶斯诊断算法。收集了244个BCT ECG的测试集，并向三名独立观察员显示，这些观察员对电生理研究中的诊断不知情。以EPS诊断结果为标准，比较贝叶斯算法与临床判断的诊断准确率。临床判断正确诊断了35%的SVTAC、85%的VT和47%的分支性心动过速。相比之下，通过设计的贝叶斯算法，52%的SVTAC，95%的VT和97%的分支性心动过速被正确诊断。所设计的贝叶斯算法已被证明是上级的临床判断的观察者谁参加了这项研究，并在理论上将解决的问题，不完全确定的心电图特征。因此，它有望成为临床实践中常规使用的有效工具。
Despite numerous attempts at devising algorithms for diagnosing broad complex tachycardia (BCT) on the basis of the electrocardiogram (ECG), misdiagnosis is still common. The reason for this may lie with difficulty in implementing existent algorithms in practice, due to imperfect ascertainment of ECG features within them. An attempt was made to approach the problem afresh with the Bayesian inference by the construction of a diagnostic algorithm centered around the likelihood ratio (LR). Previously studied ECG features most effective in discriminating ventricular tachycardia (VT) from supraventricular tachycardia with aberrant conduction (SVTAC), according to their LR values, were selected for inclusion into a Bayesian diagnostic algorithm. A test set of 244 BCT ECGs was assembled and shown to three independent observers who were blinded to the diagnoses made at electrophysiological study. Their diagnostic accuracy by the Bayesian algorithm was compared against that by clinical judgement with the diagnoses from EPS as the criterial standard. Clinical judgement correctly diagnosed 35% of SVTAC, 85% of VT, and 47% of fascicular tachycardia. In comparison, by the Bayesian algorithm devised, 52% of SVTAC, 95% of VT, and 97% of fascicular tachycardia were correctly diagnosed. The Bayesian algorithm devised has proved to be superior to the clinical judgement of the observers who participated in this study, and theoretically will obviate the problem of imperfect ascertainment of ECG features. Hence, it holds the promise for being an effective tool for routine use in clinical practice.