ClinicAl verSus algorIthmic predictioN of Obstructive Coronary Artery Disease - the CASINO study

Abstract

Abstract Background The estimation of pretest probability (PTP) is a key step when evaluating patients with suspected coronary artery disease (CAD). Current European guidelines recommend using an algorithm based on age, sex, and symptom typicality. However, many cardiologists do not regularly use this method, relying instead on overall clinical impression. The aim of this study was to compare clinical and algorithmic prediction of obstructive CAD in a set of symptomatic patients with suspected chronic coronary syndrome (CCS). Methods In this survey study, 10 cardiologists from were asked to estimate the probability of obstructive CAD (on a scale of 1-99% and without the aid of any score or algorithm) for 120 anonymized clinical vignettes of outpatients who underwent diagnostic workup for suspected CCS. The provided information included age, sex, symptom description, cardiovascular risk factors, and know comorbidities. Whenever non-invasive tests had been performed, physicians were also asked to estimate the probability of obstructive CAD after knowing that results. Obstructive CAD was defined as diameter stenosis ≥50% confirmed by invasive coronary angiography. Results Among the 120 included patients (62 women, mean age 61±10 years), symptoms consisted of chest pain in 104 and dyspnea in the remainder. The median PTP of obstructive CAD was 15% (IQR 10-25%) according to the ESC algorithm, and 30% (IQR 15-50%) according to cardiologists’ estimates (p<0.001). The observed prevalence of obstructive CAD was 20% (n=24), yielding suitable calibration for the ESC model (predicted vs. observed p=0.631), and significant overestimation for clinicians’ assessments (predicted vs. observed p=0.014) – Figure 1A. Despite using the same data, cardiologists’ PTP estimates varied widely within the same patients (mean inter-observer coefficient of variation 56%±22%). In a subset of 29 patients whose PTP was appraised twice by the same physicians, the mean absolute difference between assessments (intra-observer variability) was 22%±8%. The ESC algorithm showed fair discriminative power to identify patients with obstructive CAD (C-statistic 0.68, 95% CI 0.58-0.79, p=0.005), whereas the C-statistic for clinicians ranged from 0.53 to 0.72 (average 0.62±0.05, p values for comparisons 0.001-0.677) – Fig 1B. In the subset of 70 patients with prior non-invasive tests, clinicians significantly changed their predictions after knowing test results (mean absolute difference 22%±12%, p<0.001), improving their discriminative power significantly (mean change in C-statistic of 0.08±0.09, p=0.020). Conclusion Clinicians’ assessments of PTP in symptomatic patients display high inter- and intra-observer variability, and tend to overestimate the likelihood of obstructive CAD. Despite using more clinical data, cardiologists fail to outperform the ESC algorithm. The systematic use of this tool should be promoted in order to improve disease prediction and guide non-invasive testing.