Development and Validation of the SONA Questionnaire: A Gender-Specific Screening Tool for Moderate-to-Severe Obstructive Sleep Apnoea.

IF 3.9 3区 医学 Q2 CLINICAL NEUROLOGY
Ankur Joshi, Abhishek Goyal, Abhijit Pakhare
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Abstract

Overnight polysomnography (PSG) poses logistical and financial challenges in resource-constrained settings for the diagnosis of OSA. This study aimed to develop a simple, effective screening tool for identifying individuals at risk for OSA. Data from 1015 healthy volunteers enrolled in the BLESS cohort were analysed, all of whom underwent Level I PSG. Optimal thresholds for selected demographic, anthropometric and clinical variables were determined using a bootstrapped matrix maximisation method, with apnoea-hypopnoea index (AHI) as the reference. The dataset was split into training (n = 732) and testing (n = 244) subsets, and four predictive models were developed. A scoring system, termed SONA, was derived from coefficients of the best-performing model. Its performance was compared to existing tools. The final model incorporated four variables (AIC = 699.21, BIC = 726.79, pseudo-R2 = 0.36): waist circumference (> 93 cm in males, > 85 cm in females) and neck circumference (> 37 cm in males, > 32 cm in females) assigned 3 points each; age (> 35 years in males, > 45 years in females) assigned 2 points; and presence of snoring assigned 3 points. The total score ranged from 0 to 11. Diagnostic cut-offs were identified at scores of 5 (i.e., any two variables affirmative) in community settings and 8 (i.e., any three variables affirmative) in hospital settings. At AHI cut-off of 15, the SONA score demonstrated superior discrimination (AUC = 0.83; p value < 0.001) compared to STOPBANG (0.72), NoSAS (0.77), GOAL (0.74) and the Berlin Questionnaire (0.70). The SONA score offers a simpler, more accurate alternative for OSA screening compared to existing tools and is well-suited for implementation in primary care and resource-limited settings.

SONA问卷的开发和验证:中重度阻塞性睡眠呼吸暂停的性别筛选工具。
在资源有限的环境下,夜间多导睡眠图(PSG)对OSA的诊断提出了后勤和财政方面的挑战。本研究旨在开发一种简单、有效的筛查工具来识别有OSA风险的个体。我们分析了1015名入选BLESS队列的健康志愿者的数据,他们都接受了I级PSG检查。以呼吸暂停-低通气指数(AHI)为参考,采用自举矩阵最大化方法确定所选人口统计学、人体测量学和临床变量的最佳阈值。将数据集分为训练子集(n = 732)和测试子集(n = 244),并开发了四个预测模型。一个评分系统,称为SONA,是由最佳表现模型的系数衍生出来的。将其性能与现有工具进行了比较。最终模型包含四个变量(AIC = 699.21, BIC = 726.79,伪r2 = 0.36):腰围(>男性93 cm, >女性85 cm)和颈围(>男性37 cm, >女性32 cm)各取3分;年龄(男性bbbb35岁,女性bbbb45岁)分2分;打鼾者得3分。总分从0到11不等。在社区环境中,诊断临界值为5分(即任何两个变量均为肯定),在医院环境中为8分(即任何三个变量均为肯定)。在AHI截止值为15时,SONA评分显示出较好的辨别能力(AUC = 0.83;p值
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来源期刊
Journal of Sleep Research
Journal of Sleep Research 医学-临床神经学
CiteScore
9.00
自引率
6.80%
发文量
234
审稿时长
6-12 weeks
期刊介绍: The Journal of Sleep Research is dedicated to basic and clinical sleep research. The Journal publishes original research papers and invited reviews in all areas of sleep research (including biological rhythms). The Journal aims to promote the exchange of ideas between basic and clinical sleep researchers coming from a wide range of backgrounds and disciplines. The Journal will achieve this by publishing papers which use multidisciplinary and novel approaches to answer important questions about sleep, as well as its disorders and the treatment thereof.
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