ArticleRisk management and healthcare policy2023
Factors Influencing Functional Coronary Artery Ischemia: A Gender-Specific Predictive Model.
Article in Risk management and healthcare policy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: The objective of this study was to explore factors that impact functional coronary artery ischemia (FCAI) and develop a gender-specific prognostic model that could serve as a benchmark for predicting FCAI in clinical practice. Methods: A cumulative total of 330 patients were enrolled comprising 634 main and branch coronary, consisting of 179 men (359 coronary arteries) and 151 women (275 coronary arteries). Based on the computed tomography-fractional flow reserve (CT-FFR), the coronary arteries of male and female patients were classified into the non-ischemic group (CT-FFR ≥ 0.80) and the ischemic group (CT-FFR < 0.80). We screened for factors related to the CT-FFR values of the coronary arteries in male and female patients and developed corresponding gender-specific models. Results: In male patients, the correlation between FCAI and several indicators, including white blood cell (WBC) count, left anterior descending artery (LAD) lesions, pericoronary fat attenuation index (FAI), and the degree of coronary artery stenosis, was found to be statistically significant. A predictive model was developed using these factors, yielding an area under the curve (AUC) value of 0.812, with a P value of < 0.001 and a 95% confidence interval (CI) ranging from 0.767 to 0.857. This model demonstrated superior predictive capability compared to any individual indicators mentioned above. Significant correlations with FCAI were observed in female patients for hemoglobin (Hb), systolic blood pressure (SBP), FAI, and the degree of coronary artery stenosis. The predictive model, derived from these factors, exhibited robust performance with an area under the curve (AUC) value of 0.818, a P value of < 0.001, and a 95% confidence interval (CI) ranging from 0.764 to 0.872. Conclusion: Gender disparities exist in the factors affecting FCAI, underscoring the need for a gender-specific predictive model to enhance the precision of FCAI prediction.
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