One step closer to quantifying ‘clinical likelihood’ in pre-test probability

This editorial refers to ‘Prediction models as gatekeepers for diagnostic testing in angina patients with suspected chronic coronary syndrome’, by L. Bjerking et al. doi:10.1093/ehjqcco/qcac025. In patients presenting with stable chest pain, pre-test probability is a key tenet driving investigation and management decision across guidelines. This is true whether this be the European Society of Cardiology (ESC),1 the American societies,2 or the UK National Institute of Clinical Excellence (NICE) guidelines.3 In patients with a low pre-test probability, no further work-up is required, while in those with an intermediate or high pre-test probability (PTP), further work-up is recommended. Thus the prognostic ability of PTP models for a given population has important implications for both patients and healthcare systems. Despite the centrality of the concept of PTP, all of these guidelines differ in the models they recommend for the assessment of this. The PTP model recommended by NICE only considers the typicality of chest pain, recommending further investigation in typical or atypical chest pain, or non-cardiac chest pain with electrocardiogram (ECG) changes.3 The rational for this was the poor performance of the Diamond Forrester (DF) risk score in contemporary populations for the prediction of obstructive coronary artery disease.4 To address this issue the most recent ESC and American guidelines recommend the use of an updated DF risk score (henceforth known as the ESC PTP model for simplicity).5 This was derived from a meta-analysis of multinational data incorporating the PROMISE trial,6 the CONFIRM registry,4 and a Danish registry,7 to provide evidence from a combined population of 15 815 patients (mean age of 59 ± 11 years old, 52% female).5 The key strength of this updated PTP calculation is its derivation in a large cohort of patients from 14 countries encompassing Europe, America and Asia. While this significantly improved the discrimination of the ESC PTP score, a recent study examining this in the PROMISE trial showed that physician judgement of the probability of coronary artery disease was more accurate than the ESC PTP for the prediction of obstructive coronary artery disease and risk of cardiovascular events.8 In this regard the ESC guidelines were prescient in their inclusion of a modifying step—the physician determined ‘clinical probability’ which should be used when the PTP is 5–15%. While this is a key concept in the guidelines, this ‘clinical probability’ assessment is relatively undefined, with risk factors listed, but with no guidance for how a clinician should weight these to help guide downstream management. A recent study in the Eastern Denmark Heart registry sought to define and weight some of these risk factors, providing a PTP calculation including hypertension, dyslipidaemia, diabetes mellitus, and a family history of coronary artery disease.7 This improved the risk discrimination compared with a model including only age, sex, and nature of chest pain, with the C-statistics improving from 0.86 (95% CI 0.84–0.88) to 0.88 (95% CI 0.86–0.90, P < 0.0001). While the model showed good calibration within this derivation cohort, no external validation was performed.