优化肥胖症患者接受减重手术时的重度阻塞性睡眠呼吸暂停筛查。

Optimizing screening of severe obstructive sleep apnea in patients undergoing bariatric surgery.

机构信息

Sleep Unit, Department of Respiratory Medicine, Hospital Universitari de Bellvitge, Hospitalet de Llobregat, Spain.

出版信息

Surg Obes Relat Dis. 2013 Jul-Aug;9(4):539-46. doi: 10.1016/j.soard.2012.01.020. Epub 2012 Feb 9.

DOI:10.1016/j.soard.2012.01.020

PMID:22445650

Abstract

BACKGROUND

Obstructive sleep apnea is common in patients waiting for bariatric surgery (BS). International consensuses have recommended assessment of obstructive sleep apnea in the preoperative evaluation to avoid perioperative complications. Polysomnography is the standard diagnostic method but is expensive and time-consuming. The aim of our study was to detect those patients who merit treatment before BS using a simple predictor model. The study was conducted at 3 university hospitals (Hospital de Bellvitge, Hospital de la Santa Creu i Sant Pau, Hospital Clinic de Barcelona).

METHODS

A prospective cross-sectional study was conducted of 136 consecutive bariatric subjects. The outcome variable was severe obstructive sleep apnea, defined as an apnea-hypoapnea index of ≥30 events/hr by polysomnography. The predictors evaluated were anthropometric and clinical in the first model, with an oxygen desaturation index of ≥3% added to the second model. Predictive models were constructed using multivariate logistic regression analysis. The best model was selected according to the area under the receiver operating characteristic curve.

RESULTS

The first model identified 4 independent factors: age, waist circumference, systolic blood pressure, and witnessed apnea episodes, with a sensitivity of 78%, specificity of 68%, and area under the receiver operating characteristic curve of .83 (95% confidence interval .76-.90, P < .001). The second model identified 2 independent factors (witness apnea episodes, oxygen desaturation index of ≥3%), with a sensitivity of 91%, specificity of 85%, and area under the receiver operating characteristic curve of .94 (95% confidence interval .89-.98, P < .001). The 2-step model predictive values were sensitivity of 90%, specificity of 91%, and accuracy of 90% (95% confidence interval 84-94%). After applying the first model and then the second, 45% of subjects would have been ruled out (15% and 30%, respectively) and 55% would require additional sleep management before BS.

CONCLUSION

The proposed model could be useful for improving the management of complex patients before BS and optimizing limited polysomnography resources.

摘要

背景

阻塞性睡眠呼吸暂停在等待减重手术（BS）的患者中很常见。国际共识建议在术前评估中评估阻塞性睡眠呼吸暂停，以避免围手术期并发症。多导睡眠图是标准的诊断方法，但昂贵且耗时。我们的研究目的是使用简单的预测模型检测那些在 BS 前需要治疗的患者。该研究在 3 家大学医院（Hospital de Bellvitge、Hospital de la Santa Creu i Sant Pau、Hospital Clinic de Barcelona）进行。

方法

对 136 例连续接受减重手术的患者进行前瞻性横断面研究。结局变量为严重阻塞性睡眠呼吸暂停，定义为多导睡眠图显示呼吸暂停低通气指数≥30 次/小时。在第一个模型中评估的预测因素是人体测量学和临床指标，在第二个模型中添加了氧减饱和指数≥3%。使用多元逻辑回归分析构建预测模型。根据受试者工作特征曲线下面积选择最佳模型。

结果

第一个模型确定了 4 个独立因素：年龄、腰围、收缩压和目击呼吸暂停发作，敏感性为 78%，特异性为 68%，受试者工作特征曲线下面积为 0.83（95%置信区间 0.76-0.90，P<0.001）。第二个模型确定了 2 个独立因素（目击呼吸暂停发作、氧减饱和指数≥3%），敏感性为 91%，特异性为 85%，受试者工作特征曲线下面积为 0.94（95%置信区间 0.89-0.98，P<0.001）。两步骤模型的预测值为敏感性 90%，特异性 91%，准确性 90%（95%置信区间 84-94%）。应用第一个模型后，再应用第二个模型，45%的患者将被排除（分别为 15%和 30%），55%的患者需要在 BS 前进行额外的睡眠管理。