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Published on: August 9, 2024
Simple risk stratification for a complex coronary phenotype: association of the ACEF score with coronary slow flow
Bihter Senturk1, Mehmet Kıs1, Huseyin Dursun1
1Department of Cardiology, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey.
Insights
The ACEF score effectively predicts coronary slow flow (CSF), showing superior accuracy compared to HbA1c. This score is a valuable tool for identifying patients with CSF, independent of its individual components.
Area of Science:
- Cardiology
- Medical Diagnostics
- Predictive Analytics
Background:
- Coronary slow flow (CSF) is a condition lacking robust predictive markers.
- The association between the ACEF score and CSF prediction requires further investigation.
Purpose of the Study:
- To evaluate the predictive accuracy of the ACEF score for coronary slow flow (CSF).
- To compare the predictive performance of the ACEF score against HbA1c and LDL-C for CSF.
Main Methods:
- Retrospective analysis of 257 patients undergoing coronary angiography.
- Stratification into CSF (n=121) and normal coronary flow (NCF) (n=136) groups.
- Receiver operating characteristic (ROC) curve analysis and logistic regression to assess predictive values.
Main Results:
- The ACEF score demonstrated an Area Under the Curve (AUC) of 0.763 for CSF prediction, outperforming HbA1c (AUC=0.610).
- A 0.1-unit increase in the ACEF score correlated with a 28% increased risk of CSF.
- The ACEF score provided significant incremental predictive value over its individual components.
Conclusions:
- The ACEF score is a strong independent predictor of coronary slow flow (CSF).
- The ACEF score offers superior discriminatory ability for CSF compared to HbA1c.
Introduction:
There is a lack of scientific evidence regarding the association between the ACEF score and the prediction of coronary slow flow (CSF).
Aim:
To investigate the relationship between the ACEF score and CSF.
Material And Methods:
This retrospective, single-center study enrolled 257 patients who underwent elective coronary angiography between January 2024 and August 2025. Patients were stratified into two groups: the CSF group (n = 121) and the normal coronary flow (NCF) group (n = 136). Receiver operating characteristic (ROC) curve analysis evaluated the accuracy of the ACEF score, glycated hemoglobin (HbA1c), and low-density lipoprotein cholesterol (LDL-C) in predicting CSF. The incremental value of the ACEF score relative to its components was assessed using C-statistics, IDI, and NRI. Logistic regression was conducted to evaluate independent predictors of CSF presence. The model included sex, hypertension, peripheral artery disease, atrial fibrillation, hemoglobin, white blood cell count, platelet count, HbA1c, triglycerides, LDL-C, high-density lipoprotein cholesterol (HDL-C), and the ACEF score.
Results:
In ROC analysis, the cut-off value of the ACEF risk score was 0.94, with 81% sensitivity and 53% specificity for predicting CSF (AUC = 0.763, 95% CI: 0.706-0.821). For HbA1c, the optimal cut-off value was 5.44%, with a sensitivity of 70% and a specificity of 52% to predict CSF (AUC = 0.610, 95% CI: 0.538-0.682). The risk of CSF increased by 28% for every 0.1-unit increase in the ACEF score (OR per 0.1-unit increase: 1.278, 95% CI: 1.168-1.396). The ACEF score demonstrated superior discrimination (AUC: 0.763 vs. 0.729, p = 0.018) and significant incremental predictive value (IDI = 0.052, p < 0.001) compared to its individual components.
Conclusions:
This study suggests that the ACEF score is closely associated with the presence of CSF, independent of its individual components. The discriminatory ability of the ACEF score was superior to that of HbA1c in patients with CSF.
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