Shi Huan, Xiang Shoukui, Huang Xiaolin, Wang Long, Hua Fei, Jiang Xiaohong
Department of Endocrinology, First People's Hospital of Changzhou, Changzhou, China.
Ann Transl Med. 2020 Dec;8(24):1675. doi: 10.21037/atm-20-6890.
Obstructive sleep apnea (OSA) is highly prevalent among patients with type 2 diabetes mellitus (T2DM) in China, but few patients with clinical symptoms of OSA are referred for diagnostic polysomnography (PSG). Thus, this study aimed to develop and validate an easy-to-use nomogram that predicts the severity of OSA in patients with T2DM.
This retrospective study included consecutive patients with T2DM admitted to the Endocrinology Department, Third Affiliated Hospital of Soochow University between January 1, 2016 and December 31, 2019. OSA was diagnosed with PSG. Participants were randomly assigned to a training cohort (70%) and a validation cohort (30%). Demographic, anthropometric, and biochemical data were collected. A least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensionality and identify factors for inclusion in the nomogram (training cohort). Nomogram validation was performed in the validation cohort.
The study included 280 participants in the training group and 118 participants in the validation group. OSA prevalence was 58.5%. LASSO regression identified waist-to-hip ratio (WHR), smoking status, body mass index (BMI), serum uric acid (UA), the homeostasis model assessment insulin resistance index (HOMA-IR), and history of fatty liver disease as predictive factors for inclusion in the nomogram. Discrimination and calibration in the training group (C-index =0.88) and validation group (C-index =0.881) were good. The nomogram identified patients with T2DM at risk for OSA with an area under the curve of 0.851 [95% confidence interval (CI), 0.788-0.900].
Our nomogram could be used to facilitate individualized prediction of OSA risk in patients with T2DM and help prioritize patients for diagnostic PSG.
在中国，阻塞性睡眠呼吸暂停（OSA）在2型糖尿病（T2DM）患者中非常普遍，但很少有有OSA临床症状的患者接受诊断性多导睡眠图（PSG）检查。因此，本研究旨在开发并验证一种易于使用的列线图，以预测T2DM患者的OSA严重程度。
这项回顾性研究纳入了2016年1月1日至2019年12月31日期间苏州大学附属第三医院内分泌科收治的连续T2DM患者。通过PSG诊断OSA。参与者被随机分配到训练队列（70%）和验证队列（30%）。收集人口统计学、人体测量学和生化数据。使用最小绝对收缩和选择算子（LASSO）回归模型来降低数据维度，并确定纳入列线图（训练队列）的因素。在验证队列中进行列线图验证。
研究包括训练组的280名参与者和验证组的118名参与者。OSA患病率为58.5%。LASSO回归确定腰臀比（WHR）、吸烟状况、体重指数（BMI）、血清尿酸（UA）、稳态模型评估胰岛素抵抗指数（HOMA-IR）和脂肪肝病史为纳入列线图的预测因素。训练组（C指数=0.88）和验证组（C指数=0.881）的区分度和校准效果良好。列线图识别出T2DM合并OSA风险患者的曲线下面积为0.851[95%置信区间（CI），0.788-0.900]。
我们的列线图可用于促进T2DM患者OSA风险的个体化预测，并有助于确定患者进行诊断性PSG检查的优先级。
