Satici Celal, Azakli Damla, Sokucu Sinem Nedime, Aydin Senay, Atasever Furkan, Ozdemir Cengiz
Yedikule Chest Disease and Thoracic Surgery Training and Research Hospital, Pulmonology, Istanbul, Turkey.
Başakşehir Çam and Sakura City Hospital, Pulmonology, Istanbul, Turkey.
Sleep Breath. 2025 Jan 14;29(1):77. doi: 10.1007/s11325-024-03239-2.
Polysomnography (PSG) is resource-intensive but remains the gold standard for diagnosing Obstructive Sleep Apnea (OSA). We aimed to develop a screening tool to better allocate resources by identifying individuals at higher risk for OSA, overcoming limitations of current tools that may under-diagnose based on self-reported symptoms.
A total of 884 patients (490 diagnosed with OSA) were included, which was divided into the training, validation, and test sets. Using multivariate logistic regression analyses, we developed a scoring system incorporating male sex, age, sawtooth pattern, area under the inspiratory flow-volume curve (AreaFI), and neck circumference to objectively identify patients at higher risk of OSA. Sensitivity and specificity were evaluated using area under the curve (AUC) metrics. The M-APNE Score was compared to other non-symptom-based tools, the No-Apnea Score and the Symptomless Multivariable Apnea Prediction (sMVAP) model, using the Delong test.
The M-APNE Score showed sensitivity rates of 79.3% in the training set, 70.8% in the test, and 80% in the validation set. ROC analysis for M-APNE score yielded AUCs of 0.82 in the training, 0.76 in the test, 0.82 in the validation set. The discriminative accuracy of M-APNE Score were found to be better than the No-Apnea Score (AUC = 0.82 vs. 0.76, p < 0.001) and the sMVAP (AUC = 0.82 vs. 0.75, p = 0.001) in the training set. Hosmer Lemeshow test indicated good calibration for M-Apne Score (p = 0.46).
The M-APNE Score is a robust and objective tool for OSA screening, potentially reducing classification errors and improving accuracy.
多导睡眠图（PSG）资源消耗大，但仍是诊断阻塞性睡眠呼吸暂停（OSA）的金标准。我们旨在开发一种筛查工具，通过识别OSA风险较高的个体来更好地分配资源，克服当前基于自我报告症状的工具可能诊断不足的局限性。
共纳入884例患者（490例被诊断为OSA），分为训练集、验证集和测试集。使用多因素逻辑回归分析，我们开发了一种评分系统，纳入男性、年龄、锯齿波模式、吸气流量-容积曲线下面积（AreaFI）和颈围，以客观识别OSA风险较高的患者。使用曲线下面积（AUC）指标评估敏感性和特异性。使用德龙检验将M-APNE评分与其他非基于症状的工具、无呼吸暂停评分和无症状多变量呼吸暂停预测（sMVAP）模型进行比较。
M-APNE评分在训练集中的敏感性为79.3%，在测试集中为70.8%，在验证集中为80%。M-APNE评分的ROC分析在训练集中的AUC为0.82，在测试集中为0.76，在验证集中为0.82。在训练集中，发现M-APNE评分的判别准确性优于无呼吸暂停评分（AUC = 0.82对0.76，p < 0.001）和sMVAP（AUC = 0.82对0.75，p = 0.001）。Hosmer Lemeshow检验表明M-呼吸暂停评分校准良好（p = 0.46）。
M-APNE评分是一种用于OSA筛查的可靠且客观的工具，可能减少分类错误并提高准确性。
