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| 重度阻塞性睡眠呼吸暂停综合征临床风险预测模型的构建和验证 |
| Construction and validation of a clinical risk prediction model for severe obstructive sleep apnea syndrome |
| 投稿时间：2024-07-27 |
| DOI：10.3969/j.issn.1000-0399.2025.02.004 |
| 中文关键词: 阻塞性睡眠呼吸暂停综合征 重度 预测模型 列线图 |
| 英文关键词: Obstructive sleep apnea syndrome Severe Predictive model Nomogram |
| 基金项目:安徽医科大学校科研基金资助项目（编号：2021xkj033） |
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| 中文摘要: |
| 目的 探讨重度阻塞性睡眠呼吸暂停综合征（OSAS）的独立危险因素，构建风险预测模型，评估其临床效能并验证。方法 收集 2021年 1月至 2023年 12月在安徽医科大学第二附属医院诊治并经多导睡眠图（PSG）确诊的 252例 OSAS患者（按 7∶3随机分为训练集176例，验证集76例）资料。按PSG结果呼吸暂停低通气指数（AHI）是否≥30次/小时分为非重度组和重度组。运用单因素分析和多因素logistic回归分析筛选出重度OSAS的独立危险因素，运行RStudio软件，采用logistic回归模型构建风险预测模型并绘制列线图，通过受试者工作特征（ROC）曲线、校准曲线和临床决策曲线从区分度、校准度和临床实用性三个维度对模型进行评估并验证。结果 多因素分析结果显示，年龄、吸烟史、低密度脂蛋白胆固醇（LDL-C）、尿酸（UA）和合并症个数是重度OSAS的独立危险因素（P<0.05）。模型在训练集和验证集中的ROC曲线下面积（AUC）分别为0.96和0.91，校准曲线显示模型拟合度较高，临床决策曲线显示在0.1~0.6的阈值概率范围内模型具有良好的临床适用性。结论 年龄、吸烟史、LDL-C、UA和合并症个数，构建的风险预测模型具有较高的预测效能和临床价值，可为早期识别重度OSAS提供参考依据，进一步指导临床诊断和治疗。 |
| 英文摘要: |
| Objective To investigate the independent risk factors for severe obstructive sleep apnea syndrome (OSAS) and construct a risk prediction model to evaluate and validate its clinical efficacy. Methods Data from 252 OSAS patients diagnosed via polysomnography (PSG) at the Second Affiliated Hospital of Anhui Medical University from January 2021 to December 2023 were collected. Patients were randomly assigned into a training set (176 cases) and a validation set (76 cases) at a 7∶3 ratio. Based on PSG results, patients were categorized into the severe and non-severe groups according to whether the apnea hypoventilation index (AHI) exceeded 30 events per hour. .Independent risk factors for severe OSAS were identified using univariate analysis and multivariate Logistic regression. RStudio software was used to construct a risk prediction model using Logistic regression and create a nomogram. Results Multivariate analysis indicated that age, smoking history, lowdensity lipoprotein cholesterol (LDL-C), uric acid, and the number of comorbidities were independent risk factors for severe OSAS (P<0.05). The model’s area under the ROC curve (AUC) was 0.96 in the training set and 0.91 in the validation set. The calibration curve indicated a high degree of model fit, and the clinical decision curve demonstrated favorable clinical applicability within a threshold probability range of 0.1~0.6. Conclusion The risk prediction model developed in this study demonstrates high predictive efficacy and clinical value, offering a reference for the early identification of severe OSAS and further supporting clinical diagnosis and treatment. |
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