Zhu Xiaoxiao, Xu Bei, Yang Chengcan, Ma Shuai, Hao Guihua, Gu Fen, Wang Bing, Yao Ying
School of Nursing, Shanghai Jiao Tong University, Shanghai, China.
Department of Nursing, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Obes Surg. 2025 Sep 8. doi: 10.1007/s11695-025-08093-z.
Our study aimed to develop a predictive model for the risk of obstructive sleep apnea (OSA) in bariatric surgery candidates for utilization during the preoperative evaluation.
Relevant clinical data were retrospectively collected for 453 patients who met the inclusion criteria and did not meet the exclusion criteria; the patients were randomized into training and test cohorts. Univariate analysis was performed on the training set. Multiple risk factors associated with OSA were identified using multivariate analysis. These factors were incorporated into a regression model and used to construct a nomogram to predict the risk of OSA. The model was validated with a calibration curve and an operating characteristic curve. The models were verified for discrimination, consistency, and accuracy by calibration and subject operating characteristic curves. Finally, decision curve analysis was used to determine the model's utility.
In this study, non-alcoholic fatty liver disease (NAFLD), age, chest circumference (CC), and average SpO were found to be independent risk factors for developing OSA in bariatric surgery candidates. The AUC for the training cohort was 0.88 with a sensitivity and specificity of 0.93 (95% CI: 0.84-1.00) and 0.70 (95% CI: 0.64-0.75). The Hosmer-Lemeshow test of the calibration curves for the training and validation sets revealed a P > 0.05 (training cohort: P = 0.955; test cohort: P = 0.440).
We constructed a prediction model that included NAFLD, age, CC, and mean SpO, which showed superior predictive performance compared to existing models. This model offers a convenient, cost-effective alternative to PSG, particularly useful in preoperative screening of bariatric surgery patients. In the future, the relationship between NAFLD and OSA needs to be further explored, and the prediction model needs to be externally validated. Key Points 1. OSA increases the risk of postoperative complications in bariatric surgery patients, but there is a lack of tools to effectively predict the risk of OSA in bariatric surgery. 2. NAFLD, age, CC, and average SpO2 were found to be independent risk factors for developing OSA in bariatric surgery candidates. 3. A nomogram was constructed to predict the risk of incidence of OSA in patients undergoing bariatric surgery, offering a practical alternative to PSG.
我们的研究旨在开发一种预测模型,用于在术前评估中预测肥胖症手术候选患者发生阻塞性睡眠呼吸暂停(OSA)的风险。
回顾性收集了453例符合纳入标准且不符合排除标准的患者的相关临床数据;将患者随机分为训练组和测试组。对训练集进行单因素分析。使用多因素分析确定与OSA相关的多个风险因素。将这些因素纳入回归模型,并用于构建列线图以预测OSA的风险。通过校准曲线和操作特征曲线对模型进行验证。通过校准曲线和受试者操作特征曲线对模型的区分度、一致性和准确性进行验证。最后,使用决策曲线分析来确定模型的实用性。
在本研究中,发现非酒精性脂肪性肝病(NAFLD)、年龄、胸围(CC)和平均血氧饱和度(SpO)是肥胖症手术候选患者发生OSA的独立风险因素。训练组的AUC为0.88,敏感性和特异性分别为0.93(95%CI:0.84-1.00)和0.70(95%CI:0.64-0.75)。训练集和验证集校准曲线的Hosmer-Lemeshow检验显示P>0.05(训练组:P=0.955;测试组:P=0.440)。
我们构建了一个包含NAFLD、年龄、CC和平均SpO的预测模型,与现有模型相比,该模型显示出更好的预测性能。该模型为多导睡眠图(PSG)提供了一种方便、经济高效的替代方法,在肥胖症手术患者的术前筛查中特别有用。未来,需要进一步探索NAFLD与OSA之间的关系,并且该预测模型需要进行外部验证。要点:1. OSA增加了肥胖症手术患者术后并发症的风险,但缺乏有效预测肥胖症手术中OSA风险的工具。2. 发现NAFLD、年龄、CC和平均SpO2是肥胖症手术候选患者发生OSA的独立风险因素。3. 构建了列线图以预测肥胖症手术患者发生OSA的风险,为PSG提供了一种实用的替代方法。