Key result
Sex-specific diagnostic models predict CAD better than overall models, reaching 0.89 AUC in women.
Why the study?
The study was conducted to assess the diagnostic value of non-acute chest pain characteristics for coronary artery disease in women and men referred to outpatient cardiology clinics.
Do sex-specific prediction models improve the diagnostic accuracy for coronary artery disease in patients with non-acute chest pain compared to an overall model?
Observational (n=1,028)
Yes
Do sex-specific prediction models improve the diagnostic accuracy for coronary artery disease in patients with non-acute chest pain compared to an overall model?
Effect estimate: AUC 0.89 (women), AUC 0.84 (men) (95% CI 0.81-0.96 (women), 0.73-0.90 (men))
Sex-specific diagnostic models incorporating chest pain characteristics and risk factors significantly improve the prediction of coronary artery disease, especially in women.
May support sex-specific CAD prediction models in women with chest pain; leaves open prospective validation and outcome impact before practice change.
Objectives To assess the diagnostic value of non-acute chest pain characteristics for coronary artery disease in women and men referred to outpatient cardiology clinics. Design and setting This is an observational study performed at outpatient cardiology centres of the Netherlands. Participants The study population consisted of 1028 patients with non-acute chest pain (505 women). Analysis and results Twenty-four women (5%) and 75 men (15%) were diagnosed with coronary artery disease by invasive coronary angiography or CT angiography during regular care follow-up. Elastic net regression was performed to assess which chest pain characteristics and risk factors were of diagnostic value. The overall model selected age, provocation by temperature or stress, relief at rest and functional class as determinants and was accurate in both sexes (area under the curve (AUC) of 0.76 (95% CI 0.68 to 0.85) in women and 0.83 (95% CI 0.78 to 0.88) in men). Both sex-specific models selected age, pressuring nature, radiation, duration, frequency, progress, provocation and relief at rest as determinants. The female model additionally selected dyspnoea, body mass index, hypertension and smoking while the male model additionally selected functional class and diabetes. The sex-specific models performed better than the overall model, but more so in women (AUC: 0.89, 95% CI 0.81 to 0.96) than in men (AUC: 0.84, 95% CI 0.73 to 0.90). Conclusions In both sexes, the diagnostic value of non-acute chest pain characteristics and risk factors for coronary artery disease was high. Provocation, relief at rest and functional class of chest pain were the most powerful diagnostic predictors in both women and men. When stratified by sex the performance of the model improved, mostly in women.
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Groepenhoff et al. (2020) conducted an observational in Coronary artery disease (n=1,028). Sex-specific diagnostic models vs. Overall diagnostic model was evaluated on Diagnosis of coronary artery disease by invasive coronary angiography or CT angiography (AUC 0.89 (women), AUC 0.84 (men), 95% CI 0.81-0.96 (women), 0.73-0.90 (men)). Sex-specific diagnostic models using chest pain characteristics and risk factors predicted coronary artery disease better than an overall model, especially in women (AUC 0.89; 95% CI 0.81-0.96).
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