Abstract
AIMS: Risk factor-weighted clinical likelihood (RF-CL) estimates the probability of obstructive coronary artery disease (CAD) in patients without known CAD. We examined whether adding lipoprotein(a) [Lp(a)] measurements to the RF-CL model improves predictions of obstructive CAD.
METHODS AND RESULTS: In a derivation cohort (N = 4262; 54% male; mean age 58 years), the prevalence of obstructive CAD at invasive angiography with fractional flow reserve was assessed by Lp(a)-strata. On the basis of initial results, an Lp(a)-adjusted model (RF-CLLp(a)) was developed: RF-CL was multiplied by 1.5 in patients with elevated Lp(a) (≥125 nmol/L) and otherwise unchanged. Discrimination, calibration, and reclassification were compared. Findings were validated in an external validation cohort (N = 1595; 49% male; mean age 60 years) using a comparative endpoint; significant stenosis at invasive angiography or coronary computed tomography.In the derivation cohort, 473 patients (11.1%) had obstructive CAD; in the validation cohort, 206 patients (12.9%) had significant stenosis. The relative risk in patients with elevated Lp(a) was 1.51 [95% confidence interval (CI) 1.23-1.86] and 1.19 (95% CI 0.88-1.60) in the derivation and validation cohort, respectively. In the derivation cohort, the RF-CLLp(a) model showed a higher area under the receiver operating curve than the RF-CL model [0.743 (standard error 0.011) vs. 0.740 (0.013)] and better calibration in patients with elevated Lp(a). Reclassification from RF-CL to RF-CLLp(a) improved likelihood stratification in the derivation cohort but not in the validation cohort.
CONCLUSION: Adding elevated Lp(a) as a risk factor to the RF-CL model improves accuracy of obstructive CAD in patients with high Lp(a).