The rationale for the primacy of coronary CT angiography in the National Institute for Health and Care Excellence (NICE) guideline (CG95) for the investigation of chest pain of recent onset.
J Cardiovasc Comput Tomogr. 2018 Nov - Dec;12(6):516-522. doi: 10.1016/j.jcct.2018.09.001. Epub 2018 Sep 11.
Kelion AD1, Nicol ED2.
The National Institute for Health and Care Excellence (NICE) provides independent evidence-based guidance for England's National Health Service. Its 2010 guideline for the "assessment and diagnosis of recent onset chest pain or discomfort of suspected cardiac origin" (CG95) recommended a variety of first-line investigations in stable patients, depending on the pre-test probability (PTP) of obstructive coronary artery disease (CAD). Following a limited review, NICE produced an updated version of CG95 in 2016. Formal calculation of PTP is no longer advised. Coronary computed tomographic angiography (CCTA) is recommended as the first-line investigation for all patients with angina (or non-anginal pain but an abnormal electrocardiogram) and no prior CAD, with second-line functional imaging if the CCTA is equivocal. Notwithstanding some controversies regarding NICE's methodology, the updated version of CG95 can be justified on several levels. The focus on angina reflects evidence that patients with non-anginal pain have a similar prevalence of CAD to an asymptomatic population, and may not benefit from further investigation. The elimination of PTP is reasonable in patients required to have cardiac-sounding (anginal) symptoms. The ability of CCTA to identify non-obstructive atheroma, invisible to functional testing, might lead to improved medical treatment. Conversely the argument sometimes made for first-line functional testing, that ischemia-guided coronary revascularization leads to improved outcomes, has little hard evidence to support it. The performance of a separate functional test following equivocal CCTA may improve diagnostic specificity, and similar information is now obtainable from the CT study itself via computational flow dynamics.