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Meta-Analysis
. 2012 Sep;122(9):2105-14.
doi: 10.1002/lary.23465. Epub 2012 Aug 9.

Clinical assessment of pediatric obstructive sleep apnea: a systematic review and meta-analysis

Affiliations
Meta-Analysis

Clinical assessment of pediatric obstructive sleep apnea: a systematic review and meta-analysis

Victor Certal et al. Laryngoscope. 2012 Sep.

Abstract

Objectives/hypothesis: Clinical symptoms and signs are routinely used to investigate pediatric obstructive sleep apnea (OSA). This study aimed to systematically assess the evidence for the diagnostic accuracy of individual or combined clinical symptoms and signs in predicting pediatric OSA.

Study design: A systematic review of the literature and diagnostic meta-analysis.

Methods: Four medical databases were searched (from inception to August 2011). Studies were included that compared the clinical assessment with the current gold standard (full polysomnography). The study quality was assessed using the quality assessment tool for diagnostic accuracy studies. Summary estimates of diagnostic accuracy were determined using the sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and hierarchical summary receiver operating characteristic (HSROC) model for meta-analyses.

Results: Ten diagnostic studies with 1,525 patients were included in the review. There was substantial variation in the sensitivity and specificity among different symptoms and signs, as well as across studies. Tonsillar size and snoring reported by parents or caregivers had high sensitivity but low specificity. In contrast, excessive daytime somnolence, observed apnea, and difficulty in breathing during sleep had high specificity but low sensitivity. Seven models of a combination of symptoms and signs presented moderate sensitivity (range, 0.04-0.94) and specificity (range, 0.28-0.99). The HSROC indicates poor diagnostic performance of the symptoms and signs in predicting pediatric OSA.

Conclusions: Neither single nor combined symptoms and signs have satisfactory performance in predicting pediatric OSA. Alternative diagnostic models are necessary to improve the accuracy.

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