糖尿病患者睡眠呼吸紊乱的氧减指数诊断准确性。
Diagnostic Accuracy of Oxygen Desaturation Index for Sleep-Disordered Breathing in Patients With Diabetes.
机构信息
Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China.
Sleep Medicine Center, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
出版信息
Front Endocrinol (Lausanne). 2021 Mar 9;12:598470. doi: 10.3389/fendo.2021.598470. eCollection 2021.
BACKGROUND
Polysomnography (PSG) is the gold standard for diagnosis of sleep-disordered breathing (SDB). But it is impractical to perform PSG in all patients with diabetes. The objective was to develop a clinically easy-to-use prediction model to diagnosis SDB in patients with diabetes.
METHODS
A total of 440 patients with diabetes were recruited and underwent overnight PSG at West China Hospital. Prediction algorithms were based on oxygen desaturation index (ODI) and other variables, including sex, age, body mass index, Epworth score, mean oxygen saturation, and total sleep time. Two phase approach was employed to derivate and validate the models.
RESULTS
ODI was strongly correlated with apnea-hypopnea index (AHI) (r = 0.941). In the derivation phase, the single cutoff model with ODI was selected, with area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.917-0.994), 0.962 (95%CI 0.943-0.981), and 0.976 (95%CI 0.956-0.996) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. We identified the cutoff of ODI 5/h, 15/h, and 25/h, as having important predictive value for AHI ≥5/h, ≥15/h, and ≥30/h, respectively. In the validation phase, the AUC of ODI was 0.941 (95%CI 0.904-0.978), 0.969 (95%CI 0.969-0.991), and 0.949 (95%CI 0.915-0.983) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. The sensitivity of ODI ≥5/h, ≥15/h, and ≥25/h was 92%, 90%, and 93%, respectively, while the specificity was 73%, 89%, and 85%, respectively.
CONCLUSIONS
ODI is a sensitive and specific tool to predict SDB in patients with diabetes.
背景
多导睡眠图（PSG）是诊断睡眠呼吸障碍（SDB）的金标准。但对所有糖尿病患者进行 PSG 检查并不实际。本研究旨在开发一种临床易于使用的预测模型，以诊断糖尿病患者的 SDB。
方法
共招募 440 例糖尿病患者，在华西医院行整夜 PSG 检查。预测算法基于氧减指数（ODI）和其他变量，包括性别、年龄、体重指数、Epworth 评分、平均氧饱和度和总睡眠时间。采用两阶段法推导和验证模型。
结果
ODI 与呼吸暂停低通气指数（AHI）高度相关（r=0.941）。在推导阶段，选择了单一截断值模型，ODI 的曲线下面积（AUC）分别为 0.956（95%CI 0.917-0.994）、0.962（95%CI 0.943-0.981）和 0.976（95%CI 0.956-0.996），用于预测 AHI≥5/h、≥15/h 和≥30/h。我们确定了 ODI 5/h、15/h 和 25/h 的截断值，分别对 AHI≥5/h、≥15/h 和≥30/h 具有重要的预测价值。在验证阶段，ODI 的 AUC 分别为 0.941（95%CI 0.904-0.978）、0.969（95%CI 0.969-0.991）和 0.949（95%CI 0.915-0.983），用于预测 AHI≥5/h、≥15/h 和≥30/h。ODI≥5/h、≥15/h 和≥25/h 的灵敏度分别为 92%、90%和 93%，特异性分别为 73%、89%和 85%。
结论
ODI 是一种敏感和特异的工具，可用于预测糖尿病患者的 SDB。
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