| 龚 琴,王海燕,谢晓玮,陈一冰,石丽丽.中重度阻塞性睡眠呼吸暂停低通气综合征的危险因素分析及风险预测模型的构建[J].,2023,(17):3268-3272 |
| 中重度阻塞性睡眠呼吸暂停低通气综合征的危险因素分析及风险预测模型的构建 |
| Analysis of Risk Factors and Construction of a Risk Prediction Model for Moderate to Severe Obstructive Sleep Apnea Hypopnea Syndrome |
| 投稿时间：2023-02-09 修订日期：2023-03-06 |
| DOI：10.13241/j.cnki.pmb.2023.17.013 |
| 中文关键词: 阻塞性睡眠呼吸暂停低通气综合征 危险因素 预测模型 糖尿病 改良Mallampati分级 |
| 英文关键词: Obstructive sleep apnea hypopnea syndrome Risk factors Predictive model Diabetes Modified Mallampati grade |
| 基金项目:北京市科技计划项目（Z181100001718115） |
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| 中文摘要: |
| 摘要 目的：分析中重度阻塞性睡眠呼吸暂停低通气综合征（OSAHS）的危险因素并构建其风险预测模型。方法：选取2017年1月～2021年1月我院接受多导睡眠监测（PSG）的97例OSAHS患者，根据PSG结果分为中重度组46例和轻度组51例。收集所有患者临床资料，采用单因素和多因素Logistic回归分析中重度OSAHS的危险因素和构建风险预测模型，受试者工作特征（ROC）曲线分析中重度OSAHS风险预测模型的应用价值。结果：单因素分析显示，中重度组男性、收缩压、舒张压、颈围、糖尿病、高血压、高血脂、Mallampati分级Ⅲ～Ⅳ级的比例和体质指数（BMI）、Epworth嗜睡量表（ESS）评分、C反应蛋白、血尿酸高于轻度组（P均＜0.05）。多因素Logistic回归分析显示，男性、糖尿病、高血压、改良Mallampati分级Ⅲ～Ⅳ级和BMI升高、C反应蛋白升高、血尿酸升高为中重度OSAHS的独立危险因素（P均＜0.05）。中重度OSAHS风险预测模型方程y=-12.558+0.950×性别+ 0.030×BMI+1.808×糖尿病+0.046×高血压+1.787×改良Mallampati分级+1.925×C反应蛋白+0.570×血尿酸。ROC曲线分析显示，该模型预测中重度OSAHS的曲线下面积（AUC）、敏感度、特异度分别为0.914（95％CI：0.839～0.961）、80.43％、90.20％。结论：男性、BMI升高、糖尿病、高血压、改良Mallampati分级Ⅲ～Ⅳ级、C反应蛋白升高、血尿酸升高是中重度OSAHS的危险因素，根据上述因素构建的预测模型对中重度OSAHS具有良好的预测价值。 |
| 英文摘要: |
| ABSTRACT Objective: To analyze the risk factors of moderate to severe obstructive sleep apnea ypopnea syndrome (OSAHS) and construct a risk prediction model for it. Methods: 97 patients with OSAHS who received polysomnography (PSG) in our hospital from January 2017 to January 2021 were selected, and they were divided into moderate severe group with 46 cases and mild group with 51 cases according to the PSG results. The clinical data of all patients were collected, the risk factors of moderate and severe OSAHS were analyzed by univariate and multivariate Logistic regression analysis, and the risk prediction model was constructed. The application value of the risk prediction model of moderate and severe OSAHS was analyzed by the receiver operating characteristic(ROC) curve. Results: The univariate analysis showed that the proportion of male, systolic blood pressure, diastolic blood pressure, neck circumference, diabetes, hypertension, hyperlipidemia, Mallampati grade III-IV, body mass index (BMI), Epworth Sleepiness scale (ESS) score, C-reactive protein and serum uric acid in the moderate and severe group were higher than those in the mild group (all P<0.05). Multivariate Logistic regression analysis showed that male, diabetes, hypertension, the modified Mallampati grade Ⅲ~Ⅳ, increased BMI, elevated C-reactive protein, and elevated serum uric acid were independent risk factors for moderate to severe OSAHS (all P<0.05). Moderate and severe OSAHS risk prediction model equation y=-12.558+0.950×gender+0.030×BMI＋1.808×diabetes+0.046×hypertension+1.787×modified Malampati grade+1.925×C-reactive protein+0.570 ×serum uric acid. ROC curve analysis showed that the area under curve (AUC), sensitivity and specificity of the model for predicting moderate and severe OSAHS were 0.914 (95% CI:0.839～0.961), 80.43% and 90.20%, respectively. Conclusion: Male, increased BMI, diabetes, hypertension, modified Mallampati grade III-IV, elevated C-reactive protein, and elevated serum uric acid are risk factors for moderate to severe OSAHS, the prediction model based on the above factors has good predictive value for moderate to severe OSAHS. |
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