Skotko Brian G, Macklin Eric A, Muselli Marco, Voelz Lauren, McDonough Mary Ellen, Davidson Emily, Allareddy Veerasathpurush, Jayaratne Yasas S N, Bruun Richard, Ching Nicholas, Weintraub Gil, Gozal David, Rosen Dennis
Down Syndrome Program, Division of Medical Genetics, Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts.
Department of Pediatrics, Harvard Medical School, Boston, Massachusetts.
Am J Med Genet A. 2017 Apr;173(4):889-896. doi: 10.1002/ajmg.a.38137. Epub 2017 Jan 26.
Obstructive sleep apnea (OSA) occurs frequently in people with Down syndrome (DS) with reported prevalences ranging between 55% and 97%, compared to 1-4% in the neurotypical pediatric population. Sleep studies are often uncomfortable, costly, and poorly tolerated by individuals with DS. The objective of this study was to construct a tool to identify individuals with DS unlikely to have moderate or severe sleep OSA and in whom sleep studies might offer little benefit. An observational, prospective cohort study was performed in an outpatient clinic and overnight sleep study center with 130 DS patients, ages 3-24 years. Exclusion criteria included previous adenoid and/or tonsil removal, a sleep study within the past 6 months, or being treated for apnea with continuous positive airway pressure. This study involved a physical examination/medical history, lateral cephalogram, 3D photograph, validated sleep questionnaires, an overnight polysomnogram, and urine samples. The main outcome measure was the apnea-hypopnea index. Using a Logic Learning Machine, the best model had a cross-validated negative predictive value of 73% for mild obstructive sleep apnea and 90% for moderate or severe obstructive sleep apnea; positive predictive values were 55% and 25%, respectively. The model included variables from survey questions, medication history, anthropometric measurements, vital signs, patient's age, and physical examination findings. With simple procedures that can be collected at minimal cost, the proposed model could predict which patients with DS were unlikely to have moderate to severe obstructive sleep apnea and thus may not need a diagnostic sleep study.
阻塞性睡眠呼吸暂停（OSA）在唐氏综合征（DS）患者中很常见，报告的患病率在55%至97%之间，而在神经典型的儿科人群中为1%-4%。睡眠研究通常让人不舒服、成本高，且唐氏综合征患者耐受性差。本研究的目的是构建一种工具，以识别不太可能患有中度或重度睡眠呼吸暂停的唐氏综合征患者，这些患者可能从睡眠研究中获益不大。在一家门诊和过夜睡眠研究中心对130名3至24岁的唐氏综合征患者进行了一项观察性前瞻性队列研究。排除标准包括既往腺样体和/或扁桃体切除、过去6个月内进行过睡眠研究或正在接受持续气道正压通气治疗睡眠呼吸暂停。本研究包括体格检查/病史、头颅侧位片、3D照片、经过验证的睡眠问卷、过夜多导睡眠图和尿液样本。主要结局指标是呼吸暂停低通气指数。使用逻辑学习机，最佳模型对轻度阻塞性睡眠呼吸暂停的交叉验证阴性预测值为73%，对中度或重度阻塞性睡眠呼吸暂停的交叉验证阴性预测值为90%；阳性预测值分别为55%和25%。该模型包括来自调查问卷、用药史、人体测量、生命体征、患者年龄和体格检查结果的变量。通过可以以最小成本收集的简单程序，所提出的模型可以预测哪些唐氏综合征患者不太可能患有中度至重度阻塞性睡眠呼吸暂停，因此可能不需要进行诊断性睡眠研究。
