Department of Otorhinolaryngology Head and Neck Surgery, Department of Sleep Medicine, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Clin Respir J. 2023 Sep;17(9):931-940. doi: 10.1111/crj.13682. Epub 2023 Aug 2.
Many scales are designed to screen for obstructive sleep apnoea-hypopnoea syndrome (OSAHS); however, there is a lack of an efficiently and easily diagnostic tool, especially for Chinese. Therefore, we conduct a cross-sectional study in China to develop and validate an efficient and simple clinical diagnostic model to help screen patients at risk of OSAHS.
This study based on 782 high-risk patients (aged >18 years) admitted to the Sleep Medicine department of the Sixth Affiliated Hospital, Sun Yat-sen University from 2015 to 2021. Totally 34 potential predictors were evaluated. We divided all patients into training and validation dataset to develop diagnostic model. The univariable and multivariable logistic regression model were used to build model and nomogram was finally built.
Among 602 high-risk patients with median age of 46 (37, 56) years, 23.26% were women. After selecting using the univariate logistic model, 15 factors were identified. We further used the stepwise method to build the final model with five factors: age, BMI, total bilirubin levels, high Berlin score, and symptom of morning dry mouth or mouth breathing. The AUC was 0.780 (0.711, 0.848), with sensitivity of 0.848 (0.811, 0.885), specificity of 0.629 (0.509, 0.749), accuracy of 0.816 (0.779, 0.853). The discrimination ability had been verified in the validation dataset. Finally, we established a nomogram model base on the above final model.
We developed and validated a predictive model with five easily acquire factors to diagnose OSAHS patient in high-risk population with well discriminant ability. Accordingly, we finally build the nomogram model.
许多量表旨在筛查阻塞性睡眠呼吸暂停低通气综合征（OSAHS）；然而，缺乏一种高效且易于诊断的工具，尤其是针对中国人。因此，我们在中国进行了一项横断面研究，旨在开发和验证一种高效且简单的临床诊断模型，以帮助筛查 OSAHS 高危患者。
本研究基于 2015 年至 2021 年中山大学附属第六医院睡眠医学科收治的 782 例高危患者（年龄>18 岁）。共评估了 34 个潜在预测因素。我们将所有患者分为训练和验证数据集，以开发诊断模型。使用单变量和多变量逻辑回归模型构建模型，最终建立了列线图。
在 602 例中位年龄为 46（37，56）岁的高危患者中，女性占 23.26%。在使用单变量逻辑模型进行选择后，确定了 15 个因素。我们进一步使用逐步法建立了最终模型，包含 5 个因素：年龄、BMI、总胆红素水平、高柏林评分和早晨口干或口呼吸症状。AUC 为 0.780（0.711，0.848），灵敏度为 0.848（0.811，0.885），特异度为 0.629（0.509，0.749），准确性为 0.816（0.779，0.853）。该模型在验证数据集中的区分能力得到了验证。最后，我们基于上述最终模型建立了列线图模型。
我们开发并验证了一种预测模型，该模型具有 5 个易于获取的因素，可用于诊断高危人群中的 OSAHS 患者，具有良好的判别能力。因此，我们最终建立了列线图模型。
