Pulmonary and Critical Care Medicine, People's Hospital of Xuancheng City, Anhui, China.
Suzhou Ninth Hospital Affiliated to Soochow University, Jiang Su, China.
Oxid Med Cell Longev. 2022 Jun 27;2022:5497134. doi: 10.1155/2022/5497134. eCollection 2022.
Obstructive sleep apnea syndrome (OSAS) is common in patients with chronic coronary syndrome (CCS); however, a predictive model of OSAS in patients with CCS remains rarely reported. The study aimed to construct a novel nomogram scoring system to predict OSAS comorbidity in patients with CCS.
Consecutive CCS patients scheduled for sleep monitoring at our hospital from January 2019 to September 2020 were enrolled in the current study. Coronary CT angiography or coronary angiography was used for the diagnosis of CCS, and clinical characteristics of the patients were collected. Significant predictors for OSAS in patients with moderate/severe CCS were estimated via logistic regression analysis, and a clinical nomogram was constructed. A calibration plot, examining discrimination (Harrell's concordance index) and decision curve analysis (DCA), was applied to validate the nomogram's predictive performance. Internal validity of the predictive model was assessed using bootstrapping (1000 replications).
The nomograms were constructed based on available clinical variables from 527 patients which were significantly associated with moderate/severe OSAS in patients with CCS, including body mass index, impaired glucose tolerance, hypertension, diabetes mellitus, nonalcoholic fatty liver disease, and routine laboratory indices such as neutrophil to lymphocyte ratio, platelet-to-lymphocyte ratio, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The C-index (0.793) and AUC (0.771, 95% CI: 0.731-0.811) demonstrated a favorable discriminative ability of the nomogram. Moreover, calibration plots revealed consistency between moderate/severe OSAS predicted by the nomogram and validated by the results of sleep monitoring. Clinically, DCA showed that the nomogram had good discriminative ability to predict moderate/severe OSAS in patients with CCS.
The risk nomogram constructed via the routinely available clinical variables in patients with CCS showed satisfying discriminative ability to predict comorbid moderate/severe OSAS, which may be useful for identification of high-risk patients with OSAS in patients with CCS.
阻塞性睡眠呼吸暂停综合征(OSAS)在慢性冠状动脉综合征(CCS)患者中较为常见,但用于预测 CCS 患者 OSAS 的模型却鲜有报道。本研究旨在构建一种新的列线图评分系统,以预测 CCS 患者合并 OSAS 的情况。
连续纳入 2019 年 1 月至 2020 年 9 月在我院接受睡眠监测的 CCS 患者,采用冠状动脉 CT 血管造影或冠状动脉造影诊断 CCS,并收集患者的临床特征。采用 logistic 回归分析评估中重度 CCS 患者发生 OSAS 的显著预测因素,并构建临床列线图。通过校准图、区分度(Harrell 一致性指数)和决策曲线分析(DCA)评估列线图的预测性能。采用 bootstrap 法(1000 次重复)评估预测模型的内部有效性。
基于与中重度 CCS 患者 OSAS 显著相关的 527 例患者的可用临床变量构建了列线图,这些变量包括体重指数、糖耐量受损、高血压、糖尿病、非酒精性脂肪性肝病以及中性粒细胞与淋巴细胞比值、血小板与淋巴细胞比值、高密度脂蛋白胆固醇和低密度脂蛋白胆固醇等常规实验室指标。C 指数(0.793)和 AUC(0.771,95%CI:0.731-0.811)表明该列线图具有良好的判别能力。此外,校准图显示列线图预测的中重度 OSAS 与睡眠监测结果验证的结果一致。临床实践中,DCA 表明该列线图对预测 CCS 患者中重度 OSAS 具有良好的判别能力。
基于 CCS 患者常规临床变量构建的风险列线图对预测合并中重度 OSAS 具有良好的判别能力,有助于识别 CCS 患者中合并 OSAS 的高危患者。