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简单且无偏倚的阻塞性睡眠呼吸暂停初步筛查:一种新的形态学阻塞性睡眠呼吸暂停预测评分的介绍

Simple and Unbiased OSA Prescreening: Introduction of a New Morphologic OSA Prediction Score.

作者信息

Laharnar Naima, Herberger Sebastian, Prochnow Lisa-Kristin, Chen Ning-Hung, Cistulli Peter A, Pack Allan I, Schwab Richard, Keenan Brendan T, Mazzotti Diego R, Fietze Ingo, Penzel Thomas

机构信息

Department of Internal Medicine and Dermatology, Interdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Department of Pulmonary and Critical Care Medicine, Sleep Center, Chang Gung Memorial Hospital, Taipei, Taiwan.

出版信息

Nat Sci Sleep. 2021 Nov 9;13:2039-2049. doi: 10.2147/NSS.S333471. eCollection 2021.

Abstract

PURPOSE

An early prescreening in suspected obstructive sleep apnea (OSA) patients is desirable to expedite diagnosis and treatment. However, the accuracy and applicability of current prescreening tools is insufficient. We developed and tested an unbiased scoring system based solely on objective variables, which focuses on the diagnosis of severe OSA and exclusion of OSA.

PATIENTS AND METHODS

The OSA prediction score was developed (n = 150) and validated (n = 50) within German sleep center patients that were recruited as part of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Six objective variables that were easy to assess and highly correlated with the apnea-hypopnea index were chosen for the score, including some known OSA risk factors: body-mass index, neck circumference, waist circumference, tongue position, male gender, and age (for women only). To test the predictive ability of the score and identify score thresholds, the receiver-operating characteristics (ROC) and curve were calculated.

RESULTS

A score ≥8 for predicting severe OSA resulted in an area under the ROC curve (ROC-AUC) of 90% (95% confidence interval: 84%, 95%), test accuracy of 82% (75%, 88%), sensitivity of 82% (65%, 93%), specificity of 82% (74%, 88%), and positive likelihood ratio of 4.55 (3.00, 6.90). A score ≤5 for predicting the absence of OSA resulted in a ROC-AUC of 89% (83%, 94%), test accuracy of 80% (73%, 86%), sensitivity of 72% (55%, 85%), specificity of 83% (75%, 89%), and positive likelihood ratio of 4.20 (2.66, 6.61). Performance characteristics were comparable in the small validation sample.

CONCLUSION

We introduced a novel prescreening tool combining easily obtainable objective measures with predictive power and high general applicability. The proposed tool successfully predicted severe OSA (important due to its high risk of cardiovascular disease) and the exclusion of OSA (rarely a feature of previous screening instruments, but important for better differential diagnosis and treatment).

摘要

目的

对疑似阻塞性睡眠呼吸暂停(OSA)患者进行早期预筛查有助于加快诊断和治疗。然而,目前预筛查工具的准确性和适用性不足。我们开发并测试了一种仅基于客观变量的无偏评分系统,该系统专注于重度OSA的诊断及OSA的排除。

患者与方法

OSA预测评分在作为睡眠呼吸暂停全球跨学科联盟(SAGIC)一部分招募的德国睡眠中心患者中进行开发(n = 150)和验证(n = 50)。该评分选择了六个易于评估且与呼吸暂停低通气指数高度相关的客观变量,包括一些已知的OSA风险因素:体重指数、颈围、腰围、舌位、男性性别以及年龄(仅针对女性)。为测试该评分的预测能力并确定评分阈值,计算了受试者工作特征(ROC)及曲线。

结果

预测重度OSA的评分≥8时,ROC曲线下面积(ROC-AUC)为90%(95%置信区间:84%,95%),测试准确性为82%(75%,88%),敏感性为82%(65%,93%),特异性为82%(74%,88%),阳性似然比为4.55(3.00,6.90)。预测无OSA的评分≤5时,ROC-AUC为89%(83%,94%),测试准确性为80%(73%,86%),敏感性为72%(55%,85%),特异性为83%(75%,89%),阳性似然比为4.20(2.66,6.61)。在小样本验证中,性能特征具有可比性。

结论

我们引入了一种新型预筛查工具,它将易于获取的客观测量指标与预测能力及高通用性相结合。所提出的工具成功预测了重度OSA(因其心血管疾病风险高而重要)以及OSA的排除(这很少是以往筛查工具的特点,但对更好的鉴别诊断和治疗很重要)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d01/8590840/c17a0489c0f3/NSS-13-2039-g0001.jpg

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