Hu Meixi, Duan Anqi, Huang Zhihua, Zhao Zhihui, Zhao Qing, Yan Lu, Zhang Yi, Li Xin, Jin Qi, An Chenhong, Luo Qin, Liu Zhihong
Center for Respiratory and Pulmonary Vascular Disease, Department of Cardiology, Fuwai Hospital, National Clinical Research Center for Cardiovascular Diseases, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Department of Cardiology, Shanghai Institute of Cardiovascular Disease, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
Nat Sci Sleep. 2022 Aug 9;14:1375-1386. doi: 10.2147/NSS.S372447. eCollection 2022.
Patients with pulmonary arterial hypertension (PAH) are at high risk for obstructive sleep apnea (OSA), which may adversely affect pulmonary hemodynamics and long-term prognosis. However, there is no clinical prediction model to evaluate the probability of OSA among patients with PAH. Our study aimed to develop and validate a nomogram for predicting OSA in the setting of PAH.
From May 2020 to November 2021, we retrospectively analyzed the medical records of 258 patients diagnosed with PAH via right-heart catheterization. All participants underwent overnight cardiorespiratory polygraphy for OSA assessment. General clinical materials and biochemical measurements were collected and compared between PAH patients with or without OSA. Lasso regression was performed to screen potential predictors. Multivariable logistic regression analysis was conducted to establish the nomogram. Concordance index, calibration curve, and decision curve analysis were used to determine the discrimination, calibration, and clinical usefulness of the nomogram.
OSA was present in 26.7% of the PAH patients, and the prevalence did not differ significantly between male (29.7%) and female (24.3%) patients. Six variables were selected to construct the nomogram, including age, body mass index, hypertension, uric acid, glycated hemoglobin, and interleukin-6 levels. Based on receiver operating characteristic analysis, the nomogram demonstrated favorable discrimination accuracy with an area under the curve (AUC) of 0.760 for predicting OSA, exhibiting a better predictive value in contrast to ESS (AUC = 0.528) ( < 0.001). Decision curve analysis and clinical impact curve analysis also indicated the clinical utility of the nomogram.
By establishing a comprehensive and practical nomogram, we were able to predict the presence of OSA in patients with PAH, which may facilitate the early identification of patients that benefit from further diagnostic confirmation and intervention.
肺动脉高压(PAH)患者患阻塞性睡眠呼吸暂停(OSA)的风险很高,这可能会对肺血流动力学和长期预后产生不利影响。然而,目前尚无临床预测模型来评估PAH患者中OSA的发生概率。我们的研究旨在开发并验证一种用于预测PAH患者中OSA的列线图。
2020年5月至2021年11月,我们回顾性分析了258例经右心导管检查诊断为PAH的患者的病历。所有参与者均接受了夜间心肺多导睡眠图检查以评估OSA。收集了一般临床资料和生化指标,并对有或无OSA的PAH患者进行了比较。采用套索回归筛选潜在预测因素。进行多变量逻辑回归分析以建立列线图。采用一致性指数、校准曲线和决策曲线分析来确定列线图的区分度、校准度和临床实用性。
26.7%的PAH患者存在OSA,男性(29.7%)和女性(24.3%)患者的患病率无显著差异。选择了六个变量来构建列线图,包括年龄、体重指数、高血压、尿酸、糖化血红蛋白和白细胞介素-6水平。基于受试者工作特征分析,列线图在预测OSA方面显示出良好的区分准确性,曲线下面积(AUC)为0.760,与爱泼沃斯思睡量表(ESS)(AUC = 0.528)相比,具有更好的预测价值(<0.001)。决策曲线分析和临床影响曲线分析也表明了列线图的临床实用性。
通过建立一个全面实用的列线图,我们能够预测PAH患者中OSA的存在,这可能有助于早期识别那些将从进一步诊断确认和干预中获益的患者。