Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina.
Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
JACC Cardiovasc Imaging. 2017 Mar;10(3):264-275. doi: 10.1016/j.jcmg.2016.12.010.
Cardiovascular diagnostic imaging tests are increasingly used in everyday clinical practice, but are often imperfect, just like any other diagnostic test. The performance of a cardiovascular diagnostic imaging test is usually expressed in terms of sensitivity and specificity compared with the reference standard (gold standard) for diagnosing the disease. However, evidence-based application of a diagnostic test also requires knowledge about the pre-test probability of disease, the benefit of making a correct diagnosis, the harm caused by false-positive imaging test results, and potential adverse effects of performing the test itself. To assist in clinical decision making regarding appropriate use of cardiovascular diagnostic imaging tests, we reviewed quantitative concepts related to diagnostic performance (e.g., sensitivity, specificity, predictive values, likelihood ratios), as well as possible biases and solutions in diagnostic performance studies, Bayesian principles, and the threshold approach to decision making.
心血管诊断影像学检查在日常临床实践中应用越来越广泛，但与任何其他诊断性检查一样，并不完美。心血管诊断影像学检查的性能通常用其与诊断疾病的参考标准（金标准）相比的敏感性和特异性来表示。然而，基于证据的诊断性检查的应用还需要了解疾病的术前概率、正确诊断的益处、假阳性影像学检查结果造成的危害以及进行检查本身可能产生的不良反应。为了协助临床决策，我们对心血管诊断影像学检查的合理应用进行了回顾，内容涉及诊断性能的定量概念（如敏感性、特异性、预测值、似然比），以及诊断性能研究中的可能偏倚和解决方案、贝叶斯原理和决策的阈值方法。
