Key result
A novel laboratory-based prediction model demonstrated superior discrimination for obstructive coronary artery disease with an AUC of 0.816 compared to the Duke Clinical Score (AUC 0.66).
Why the study?
Existing obstructive CAD prediction tools were derived in Caucasian cohorts with unknown performance in China, and mostly rely on non-laboratory variables.
Does a novel laboratory-based model improve the prediction of obstructive CAD compared to traditional clinical scores in Chinese inpatients with suspected stable chest pain?
Population
8963 inpatients with suspected stable chest pain referred for CAG in China
Comparison
Novel laboratory-based model vs CAD1/2, DCS, and DF scores
Design
Model development and internal/external validation cohort study
Authors
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May enhance CAD prediction in practice; leaves open prospective validation before adoption.
Cohort (n=8,963)
Yes
Does a novel laboratory-based model improve the prediction of obstructive CAD compared to traditional clinical scores in Chinese inpatients with suspected stable chest pain?
Effect estimate: NRI 0.60
Absolute Event Rate: 0.816% vs 0.66%
p-value: p=<0.001
A novel laboratory-based prediction model significantly improved risk stratification for obstructive CAD in Chinese patients with suspected stable chest pain compared to traditional clinical scores.
Zhou et al. (2020) conducted a cohort in Suspected stable chest pain (n=8,963). Novel laboratory-based prediction model vs. CAD Consortium 1/2 Score, Duke Clinical Score, and Diamond-Forrester Score was evaluated on Area under the receiver-operating curve (AUC) for predicting obstructive CAD in the external validation set (NRI 0.60, p=<0.001). A novel laboratory-based prediction model demonstrated superior discrimination for obstructive coronary artery disease with an AUC of 0.816 compared to the Duke Clinical Score (AUC 0.66).
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