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基于四变量筛查工具潜在预测模型预测阻塞性睡眠呼吸暂停低通气综合征(OSAHS)患者冠状动脉疾病风险及其与冠状动脉粥样硬化严重程度相关性的研究

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.

作者信息

Yao Yanli, Li Yu, Chen Yulan, Qiu Xuan, Aimaiti Gulimire, Maimaitimin Ayiguzaili

机构信息

Department of Hypertension, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.

Second Department of Comprehensive Internal Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.

出版信息

Front Cardiovasc Med. 2025 Jun 27;12:1602492. doi: 10.3389/fcvm.2025.1602492. eCollection 2025.

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. correlation coefficients were employed to assess the strengths of associations among all variables and between predictors and Gensini scores, reflected in heat maps and chord diagrams, respectively.

RESULTS

LASSO-logistic regression analysis revealed age ( = 1.07, 95% : 1.05-1.1,  < 0.001), hypertension ( = 1.29, 95% : 1.16-1.44,  < 0.001), AHI ( = 1.02, 95% : 1.01-1.03,  = 0.007), and the 4 V ( = 1.84, 95% : 1.21-2.79,  = 0.004) were independently associated with OSAHS plus CAD. The analysis of the ROC curve revealed that the combined utilization of the aforementioned predictors significantly enhances the potential predictive capability for patients with OSAHS developing CAD. The Hosmer-Lemeshow test, calibration curve, and DCA results indicate that potential predictive model based on the 4 V possesses significant clinical applicability in predicting OSAHS in conjunction with CAD. A comprehensive analysis utilizing the random forest machine learning algorithm demonstrated that the AHI exhibits the highest predictive value. Furthermore, the model's performance, as evaluated through out-of-bag error assessment, suggests robust efficacy. The correlation analysis results showed that the scores of the four-variable screening tool were positively correlated with the Gensini scores.

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.

摘要

目的

本研究旨在评估四变量筛查工具(4V)潜在预测模型在预测阻塞性睡眠呼吸暂停低通气综合征(OSAHS)患者冠心病(CAD)风险中的潜在关联,以及其与通过Gensini评分系统测量的冠状动脉粥样硬化严重程度的相关性。

方法

选取2020年3月至2024年2月在新疆医科大学第一附属医院住院的1197例疑似CAD的OSAHS患者。患者接受冠状动脉造影或冠状动脉计算机断层扫描血管造影(CCTA)检查以确诊。OSAHS合并CAD组423例,OSAHS组774例。进行LASSO回归分析以筛选潜在影响因素。采用倾向评分匹配(PSM)平衡组间协变量,每组按1:1比例纳入293例。采用单变量和多变量逻辑回归分析评估与CAD独立相关的参数并构建列线图模型。采用受试者操作特征(ROC)曲线分析、Hosmer-Lemeshow检验、校准曲线和决策曲线(DCA)分析评估其在CAD中的预测价值。使用随机森林机器学习算法评估各风险因素的重要性。相关系数分别用于评估所有变量之间以及预测因子与Gensini评分之间关联的强度,分别反映在热图和弦图中。

结果

LASSO逻辑回归分析显示年龄(β = 1.07,95%CI:1.05 - 1.1,P < 0.001)、高血压(β = 1.29,95%CI:1.16 - 1.44,P < 0.001)、呼吸暂停低通气指数(AHI)(β = 1.02,95%CI:1.01 - 1.03,P = 0.007)和4V(β = 1.84,95%CI:1.21 - 2.79,P = 0.004)与OSAHS合并CAD独立相关。ROC曲线分析表明,联合使用上述预测因子可显著提高OSAHS患者发生CAD的潜在预测能力。Hosmer-Lemeshow检验、校准曲线和DCA结果表明,基于4V的潜在预测模型在预测OSAHS合并CAD方面具有显著的临床适用性。利用随机森林机器学习算法进行的综合分析表明,AHI具有最高的预测价值。此外,通过袋外误差评估评估的模型性能表明其疗效稳健。相关分析结果显示,四变量筛查工具的评分与Gensini评分呈正相关。

结论

年龄、高血压、AHI和四变量筛查工具是OSAHS患者CAD的独立危险因素。基于4V的潜在预测模型与CAD的预测密切相关,且与冠状动脉粥样硬化的严重程度相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bc6/12245809/955bc4792991/fcvm-12-1602492-g001.jpg

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