School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.
Department of Cardiology, University Hospital of Wales, Heath Park, Cardiff CF14 4XW, UK.
Eur Heart J Cardiovasc Imaging. 2017 Dec 1;18(12):1311-1321. doi: 10.1093/ehjci/jex216.
Our use of modern cardiovascular imaging tools has not kept pace with their technological development. Diagnostic errors are common but seldom investigated systematically. Rather than more impressive pictures, our main goal should be more precise tests of function which we select because their appropriate use has therapeutic implications which in turn have a beneficial impact on morbidity or mortality. We should practise analytical thinking, use checklists to avoid diagnostic pitfalls, and apply strategies that will reduce biases and avoid overdiagnosis. We should develop normative databases, so that we can apply diagnostic algorithms that take account of variations with age and risk factors and that allow us to calculate pre-test probability and report the post-test probability of disease. We should report the imprecision of a test, or its confidence limits, so that reference change values can be considered in daily clinical practice. We should develop decision support tools to improve the quality and interpretation of diagnostic imaging, so that we choose the single best test irrespective of modality. New imaging tools should be evaluated rigorously, so that their diagnostic performance is established before they are widely disseminated; this should be a shared responsibility of manufacturers with clinicians, leading to cost-effective implementation. Trials should evaluate diagnostic strategies against independent reference criteria. We should exploit advances in machine learning to analyse digital data sets and identify those features that best predict prognosis or responses to treatment. Addressing these human factors will reap benefit for patients, while technological advances continue unpredictably.
我们对现代心血管成像工具的使用并没有跟上其技术发展的步伐。诊断错误很常见，但很少进行系统的调查。我们的主要目标不应是更令人印象深刻的图像，而应是更精确的功能测试，我们选择这些测试是因为其合理使用具有治疗意义，而这反过来又对发病率或死亡率产生有益影响。我们应该进行分析性思维，使用清单避免诊断陷阱，并应用可以减少偏差和避免过度诊断的策略。我们应该开发规范的数据库，以便我们可以应用考虑到年龄和危险因素变化的诊断算法，并计算出疾病的预测试概率和报告后测试概率。我们应该报告测试的不准确性，或其置信区间，以便在日常临床实践中可以考虑参考值变化。我们应该开发决策支持工具来提高诊断成像的质量和解释，以便我们选择单一的最佳测试，而不受模态的影响。新的成像工具应该经过严格评估，以便在广泛传播之前确定其诊断性能；这应该是制造商与临床医生的共同责任，从而实现具有成本效益的实施。试验应该根据独立的参考标准评估诊断策略。我们应该利用机器学习的进步来分析数字数据集，并确定那些最能预测预后或对治疗反应的特征。解决这些人为因素将使患者受益，而技术进步则不可预测。
