Coronary calcium scoring improves risk prediction in CAD patients

Risk stratification for myocardial infarction and death in patients with suspected obstructive coronary artery disease (CAD) may be improved by the addition of coronary artery calcium score (CACS) to classical risk factors in simple clinical likelihood (CL) models compared with the basic pretest probability (PTP) model, suggests a recent study.

“Hence the optimized risk factor–weighted clinical likelihood (RF-CL) and CACS-CL models identify 2.5 and 3.3 times more patients, respectively, who may not benefit from further diagnostic testing,” the authors said.

A Danish register (n=41,177) and a North American randomized study (n=3,952) were used to identify cohorts in this study. The authors stratified the incidences of myocardial infarction and death according to categories by the RF-CL and CACS-CL and compared these with categories by the PTP model. All patients were symptomatic and referred for diagnostic testing due to clinical indications.

The annualized event rates of myocardial infarction and death were low in all models: RF-CL (0.51 percent, 95 percent confidence interval [CI], 0.46‒0.56), CACS-CL (0.48 percent, 95 percent CI, 0.44‒0.56), and PTP (0.37 percent, 95 percent CI, 0.31‒0.44). This was despite the substantial down-reclassification of patients to a likelihood ≤5 percent of CAD with either the RF-CL (45 percent) or CACS-CL (60 percent) models compared with the PTP (18 percent) model.

Using Harrell’s C-statistics, comparison of the predictive power of the three models showed the superiority of RF-CL (0.64, 95 percent CI, 0.63‒0.65) and CACS-CL (0.69, 95 percent CI, 0.67‒0.70) models to the PTP model (0.61, 95 percent CI, 0.60‒0.62).