ArticleFrontiers in cardiovascular medicine2025
A study on predicting the risk of coronary artery disease in OSAHS patients based on a four-variable screening tool potential predictive model and its correlation with the severity of coronary atherosclerosis.
Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Association of the TyG index and its body fat distribution composites with CHD and MACE risk in adults with OSAHS: incremental discrimination and exploratory mediation analysis.Frontiers in endocrinology · 2026Observational
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6 authors.
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Abstract
Objective: This study aims to evaluate the potential association between the four-variable screening tool (the 4 V) potential predictive model in predicting coronary artery disease (CAD) risk in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) and its correlation with the severity of coronary atherosclerosis, as measured by the Gensini scoring system. Methods: 1197 OSAHS patients with suspected CAD who were hospitalized in the First Affiliated Hospital of Xinjiang Medical University between March 2020 and February 2024 were selected. The patients were submitted to coronary angiography or Coronary Computed Tomography Angiography (CCTA) examination to confirm the diagnosis. There were 423 cases in the OSAHS plus CAD group and 774 cases in the OSAHS group. LASSO regression analysis was carried out for screening potential influencing factors. Propensity score matching (PSM) was used to balance covariables between groups, and 293 cases were included per group in a 1:1 ratio. Univariable and multivariable logistic regression analyses were employed to evaluate parameters independently associated with CAD and construct a nomogram model.Receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, calibration curve and decision curve (DCA) analyses were employed to assess its predictive value in CAD. A random forest machine learning algorithm was used to evaluate the importance of each risk factor. Results: LASSO-logistic regression analysis revealed age ( Conclusion: Age, hypertension, AHI, and the four-variable screening tool are independent risk factors for CAD in patients with OSAHS. The potential predictive model based on the 4 V is closely related to the prediction of CAD and its correlation with the severity of coronary atherosclerosis.
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