Key result
Adding HDL, LDL, fibrinogen, and CRP improves the c-statistic of obstructive CAD prediction to 0.72.
Why the study?
The performance of a recently proposed prediction model to diagnose obstructive CAD needed external validation and potential extension to improve patient selection for coronary angiography.
Does an extended diagnostic prediction model improve the identification of obstructive CAD in patients with suspected CAD referred for elective coronary angiography?
Population
4888 patients with suspected CAD undergoing elective coronary angiography in Austria
Comparison
Original prediction model vs extended model including HDL, LDL, fibrinogen, and CRP
Design
Prospective single-centre cardiology cohort with cross-sectional predictive evaluation
Authors
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Model validation in elective CAD patients supports further testing; leaves open impact on angiography selection in practice.
Cross-Sectional (n=4,888)
No
Does an extended diagnostic prediction model improve the identification of obstructive CAD in patients with suspected CAD referred for elective coronary angiography?
Effect estimate: c-statistic 0.72 (95% CI 0.71 to 0.74)
Absolute Event Rate: 0.72% vs 0.69%
p-value: p=<0.001
Extending a standard clinical prediction model with easily available laboratory markers (HDL, LDL, fibrinogen, CRP) provides a small but significant improvement in diagnosing obstructive CAD.
Edlinger et al. (2017) conducted a cross-sectional in Obstructive coronary artery disease (n=4,888). Extended diagnostic prediction model (including HDL, LDL, fibrinogen, and CRP) vs. Previously proposed clinical prediction model was evaluated on Discrimination (c-statistic) for diagnosing obstructive coronary artery disease (c-statistic 0.72, 95% CI 0.71 to 0.74, p=<0.001). Extension of a clinical prediction model with HDL and LDL cholesterol, fibrinogen, and C-reactive protein significantly improved discrimination for diagnosing obstructive coronary artery disease (c-statistic 0.72 vs 0.69).
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