The Laryngoscope C 2012 The American Laryngological, V
Rhinological and Otological Society, Inc.
Clinical Assessment of Pediatric Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis Victor Certal, MD; Emanuel Catumbela, MD; Joa˜o C. Winck, MD, PhD; Ineˆs Azevedo, MD, PhD; Armando Teixeira-Pinto, PhD; Altamiro Costa-Pereira, MD, PhD 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. Key Words: Pediatric sleep apnea, systematic review, meta-analysis, diagnostic accuracy. Laryngoscope, 000:000–000, 2012
INTRODUCTION Pediatric obstructive sleep apnea (OSA) is characterized by recurring episodes of complete and/or partial obstruction of the upper airway during sleep, resulting in intermittent hypoxemia and hypercapnia, frequent arousals, and sleep fragmentation.1,2 OSA is a severe condition in children and differs from its adult counterpart in its physiology, clinical presentation, polysomnographic characteristics, and outcomes. The estimated prevalence in children is 1% to 3%3; however, the prevalence is difficult to measure because of subdiagnosis.2,4 Lack of community awareness about the
From the Department of Otorhinolaryngology (V.C.), Hospital Sao Sebastiao, Santa Maria da Feira; Center for Research in Health Technologies and Information Systems ( V. C ., E . C ., A . T.- P., A . C .- P.), University of Porto, Porto; Department of Pulmonology ( J . C . W.), Department of Pediatrics (I.A.), and Department of Biostatistics and Medical Informatics (A.T.-P.), Faculty of Medicine, University of Porto, Porto, Portugal. Editor’s Note: This Manuscript was accepted for publication May 4, 2012. The authors have no funding, financial relationships, or conflicts of interest to disclose. Send correspondence to Victor Certal, MD, Departamento de Cieˆncias da Informac¸a˜o e da Decisa˜o em Sa ude, Faculdade de Medicina, Universidade do Porto, Al. Prof. Hernaˆni Monteiro, 4200-319 Porto, Portugal. E-mail:
[email protected] DOI: 10.1002/lary.23465
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negative sleep-related effects on the daily functioning of children, together with the parents’ underestimation of the problem when they talk to the physician, are factors that contribute to this underestimation.5,6 Polysomnography (PSG) is the gold standard for diagnosing and quantifying OSA.6–9 Nocturnal PSG recordings provide unbiased and objective information on various sleep-related characteristics such as sleep architecture, cardiac and respiratory patterns, and gas exchange. However, several factors have hampered a more extensive implementation of such diagnostic procedures, including the inconvenience for both parents and child spending the night in the laboratory, the rather onerous and labor-intensive nature of this diagnostic procedure, the relative scarcity of laboratories with expertise in children’s sleep disorders, and as a corollary to this, the extended waiting period between referral and actual testing. Thus, the diagnosis of pediatric OSA is still often made on a clinical basis.10 Numerous studies have assessed the accuracy of clinical symptoms and signs in detecting pediatric OSA. The most commonly assessed symptoms and signs include snoring, observed apnea, mouth breathing, excessive daytime somnolence (EDS), and large tonsil size, but the diagnostic accuracy varies significantly for different symptoms and signs as well as across studies. Certal et al.: Clinical Assessment of Pediatric OSA
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Fig. 1. Flow diagram of study identification and selection. PSG ¼ polysomnography.
Two previous reviews addressed this variability.11,12 However, both used a poor methodological approach and presented conflicting results. We conducted a systematic review and meta-analysis to assess the accuracy of clinical symptoms and signs in predicting pediatric OSA. The secondary aim was to assess possible sources of heterogeneity.
MATERIALS AND METHODS Study Design A systematic review and meta-analysis of studies focusing on the clinical evaluation of pediatric OSA was undertaken. The methodological approach included the development of selection criteria, definition of search strategies, assessment of the quality of the studies, data abstraction, and statistical data analysis.
Selection Criteria For proper identification of studies eligible for the analysis, the study selection criteria were defined before data collection. Only articles whose primary objective was to evaluate the ability of clinical evaluation (i.e., the clinical history and/or physical examination) to accurately diagnose pediatric OSA were selected. Articles that predominantly evaluated other diagnostic modalities (e.g., radiographs, pulse oximetry, electrocardiography, laboratory testing) were excluded. Only articles using full multichannel PSG (with electroencephalography) as the gold standard reference test for comparison with clinical evaluation were selected. The included studies provided information such as sensitivity and specificity or sufficient information to allow the construction of the diagnostic 2 2 table with four cells for true positives, false negatives, false positives, and true negatives.
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Search Strategy Our primary method to locate potentially eligible studies was a computerized literature search in the MEDLINE database (through PubMed), from inception to August 2011, without any restriction on the language of publication, using the following search key words and MeSH terms: ‘‘sleep apnea OR snoring OR sleep disordered breathing OR obstructive sleep apnea syndrome OR sleep apnea/diagnosis’’ AND ‘‘polysomnography OR sleep monitoring’’ AND ‘‘children OR child.’’ Literature searches were also undertaken, using the same search key words, in the following databases: the Cochrane collaboration, SCOPUS, and ProQuest Dissertations and Thesis Database. In defining the search strategies, we prioritized formats with higher sensitivity to increase the probability of identifying all relevant articles. Conference proceedings of the American Thoracic Society, Associated Professional Sleep Societies, European Sleep Research Society, and European Respiratory Society for the years 2002 to 2007 were hand-searched for eligible studies. Reference lists from eligible studies and review articles were crosschecked to identify additional trials. A grey literature research was also performed using OpenSIGLE Database. Both English and non-English studies were considered.
Study Quality Assessment and Data Abstraction The titles and abstracts of the retrieved studies were independently screened for relevance by two reviewers (V.C. and E.C.). As currently recommended for systematic reviews of diagnostic accuracy studies, the reviewers evaluated the methodology of the selected studies using the 14-item Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS). All disagreements were resolved by consensus. The data were used to construct 2 2 contingency tables, from which sensitivity and specificity were calculated for each
Certal et al.: Clinical Assessment of Pediatric OSA
AHI >5
AHI >1
AHI >1
NR
AHI >5
AHI >1
AHI >15
AHI >1
AHI >1
AHI >1
Polysomnographic Criteria for OSA
study. When the raw data were presented in 3 3 or 4 4 tables (e.g., when the reference apnea-hypopnea index [AHI] was defined by >2 categories), we reconstructed 2 2 contingency tables by considering AHI >1 as the positive state of pediatric OSA.
4–18 years (76) 50 OSA ¼ obstructive sleep apnea; AHI ¼ apnea/hypopnea index; NR ¼ not reported.
Community hospital
67
3–8 years (60)
Individual symptoms and combination of symptoms Based on the Pediatric Sleep Questionnaire Individual symptoms and combination of symptoms 1–12 years (56) 326 Children’s sleep laboratory
Combination of symptoms 5–15 years (65)
18 months–12 years (59) 93 Sleep disorders center
229
Individual symptoms and combination of symptoms Individual symptoms 6–8 years (50) 480 General population
Combination of symptoms 1–14 years (60) 30 Pediatric otolaryngology outpatient clinic
Based on the Pediatric Sleep Questionnaire 5–12.9 years (57)
2–13 years (58) 62
105
Sleep clinic China
UK Sproson et al., 200942
Xu et al., 200643
USA Rosen et al., 199934
Sleep clinic
USA
China Li et al., 200641
USA Goodwin et al., 200539
Leach et al., 199140
USA Goldstein et al., 199438
Certal et al.: Clinical Assessment of Pediatric OSA
USA
Singapore Chau et al., 200336
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Chervin et al., 200738
USA Carroll et al., 199535
University-based sleep disorders laboratory
Individual symptoms and combination of symptoms Individual symptoms 5.4 months–14.8 years (58) 83
Country
Pediatric sleep and breathing disorders clinic Sleep clinic
Age, Male (%) Sample Size (n)
All database searches were performed in August 2011. A flow chart of the process of study identification and inclusion/exclusion is shown in Figure 1. In total, 810 articles were identified using the search strategy and sources listed. After the titles and abstracts were screened for relevance, 780 articles were excluded (the reasons for exclusion are presented in Fig. 1). The remaining 30 articles were retrieved for more detailed full-text evaluation, and 21 were excluded for the following reasons: six studies14–19 used a resumed PSG as the reference gold standard and thus lacked electroencephalography in their reference PSG; two studies included pediatric and adult populations in their results,8,20 four studies did not provide sufficient information to build 2 2 tables;21–24 and nine studies evaluated other diagnostic modalities.25–33 One study34 was included after a hand-search of references of the included studies. Finally, 10 studies were included in the review.34–43
Author, Year, Reference
Search and Study Selection
Setting
RESULTS
TABLE I. Characteristics of Included Studies.
We used the sensitivity, specificity, positive (LRþ) and negative likelihood ratios (LR), and diagnostic odds ratio (DOR) as measures of diagnostic accuracy. Individual diagnostic studies usually use different cutoff criteria (explicitly or implicitly) to define an abnormal result. This may be particularly true for diagnostic studies that evaluate clinical manifestations, where detection of the same symptom or sign can vary significantly among observers. For this reason, we used a hierarchical summary receiver operating characteristic (HSROC) model for meta-analyses of diagnostic accuracy that model the accuracy, threshold effect, and dependence of accuracy on threshold.13 The random effects model was applied to account for the within-study and between-study variation. The heterogeneity in results across studies was investigated through a visual examination of forest plots of sensitivity and specificity, as well as receiver operating characteristic (ROC) plots. We did not use statistical tests of heterogeneity because of expected variations arising from the interdependence of sensitivity and specificity. We anticipated a substantial heterogeneity between studies and used a random-effects model for meta-analyses that allowed the heterogeneity to be taken into account. We identified outliers (studies with data points falling outside the 95% confidence region of the summary operating point) in the ROC plots, and the characteristics of these studies were investigated. We also performed subgroup analyses to explore the possible causes of heterogeneity, such as AHI criteria for OSA (1 or >1 episode/hour) and study setting (sleep centers or non-sleep centers). The data processing and statistical analysis were performed using the Cochrane Collaboration’s Review Manager software version 4.2, RevMan Analyses software version 1.0, and SAS software (SAS 9.2 procNlmixed, SAS Institute, Cary, NC).
Clinical Criteria for OSA
Statistical Analysis
3
TABLE II. Assessment of the Methodological Quality of the Studies Included According to the 14-Item QUADAS Checklist* Question Author, Year, Reference
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Carroll et al., 199535 Chau et al., 200336
Yes Yes
Yes Yes
Yes Yes
Unclear Unclear
Yes Yes
Yes Yes
Yes Yes
No No
Yes Yes
Yes Unclear
Yes No
Yes Yes
Unclear Unclear
Yes Yes
Chervin et al., 200737
Yes
Yes
Yes
Unclear
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Unclear
Yes
Yes Unclear
Yes Yes
Yes Unclear
Unclear Unclear
Yes Yes
Yes Yes
Yes Yes
No Yes
Yes Yes
Unclear Yes
Unclear Yes
Yes Yes
Unclear Unclear
Yes Yes
Leach et al., 199240
Yes
Yes
Unclear
Unclear
Yes
Yes
Yes
No
Yes
Unclear
Unclear
Yes
Unclear
Yes
Li et al., 200641 Rosen et al., 199934
Yes Yes
Yes Yes
Yes Yes
Unclear Unclear
Yes Yes
Yes Yes
Yes Yes
Yes No
Yes Yes
Unclear Unclear
Unclear No
Yes Yes
Unclear Unclear
Yes Yes
Goldstein et al., 199438 Goodwin et al., 200539
Sproson et al., 200942 Xu et al., 200643
Unclear
Yes
Yes
Unclear
Yes
Yes
Yes
Yes
Yes
Unclear
Unclear
Yes
Unclear
Yes
Yes
Yes
Yes
Unclear
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Unclear
Yes
*Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist: 1) Was the spectrum of patients representative of the patients who will receive the test in practice? 2) Were selection criteria clearly described? 3) Is the reference standard likely to classify the target condition correctly? 4) Is the period between reference standard and index test short enough to be reasonably sure that the target condition did not change between the two tests? 5) Did a whole sample or random selection of the sample receive verification using a reference standard? 6) Did patients receive the same reference standard regardless of the index test result? 7) Was the reference standard independent of the index test (i.e., the index test did not form part of the reference standard)? 8) Was the execution of the index test described in sufficient detail to permit replication of the test? 9) Was the execution of the reference standard described in sufficient detail to permit its replication? 10) Were the index test results interpreted without knowledge of the results of the reference standard? 11) Were the reference standard results interpreted without knowledge of the results of the index test? 12) Were the same clinical data available when test results were interpreted as would be available when the test is used in practice? 13) Were uninterpretable/intermediate test results reported? 14) Were withdrawals from the study explained?
Methodological Quality of the Included Studies The main characteristics of the included studies are presented in Table I. Overall, 1,525 patients were included, with a mean of 152.5 patients per study (range, 30–480 patients). Despite the American Association of Sleep Medicine (AASM) recommendations,44 only six studies34–37,39,42 defined AHI >1/hour as the diagnosis of pediatric OSA (range, 1–15/hour). One study40 did not report any AHI threshold. All studies satisfied seven of the 14 items in the QUADAS checklist for the assessment of methodological quality, namely, selection criteria described, complete verification using reference standard, same reference standard used, reference standard independent of the index test, details of execution of the reference standard, similar clinical data available for interpretation of the test as that in practice, and withdrawals explained (Table II). The main methodological limitations of the studies were related to poor reporting of items four (period between reference standard and index test), eight (details about the execution of the index test), 10 and 11 (blind interpretation of the reference and index tests), and 13 (reporting of uninterpretable/intermediate test results). In all studies, no explicit criterion for identifying symptoms and signs was presented, and interobserver agreement was not assessed.
Sensitivity and Specificity Sensitivity and specificity for different symptoms and signs varied substantially; this variation was also observed across the studies included (Fig. 2). We therefore did not report the pooled sensitivity and specificity of each symptom or sign. Tonsillar size and snoring reported by parents or caregivers had relatively high sensitivity but low specificity. In contrast, EDS, observed apnea, and difficulty Laryngoscope 000: Month 2012
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breathing during sleep had relatively high specificity but low sensitivity. Several models of combined symptoms and signs were assessed (Table III). Seven models/algorithms presented a moderate sensitivity (range, 0.04–0.94) and specificity (range, 0.28–0.99): the Pediatric Sleep Questionnaire, the OSA score initially proposed by Brouillette et al.14 (snoring, difficulty breathing, EDS and behavior, personality or school performance), the modified OSA score of Carroll et al.35 (observed apnea, difficulty breathing, and parents watching child during sleep), the model of Xu et al.43 (observed apnea, nocturnal enuresis, intrusive naps, mouth breathing, and moderate to severe tonsil hypertrophy), the model of Goldstein et al.38 (snoring, pauses, difficulty breathing, sleep with neck extended, EDS, and adenoid face), and Goodwin et al.39 presented two models of symptoms (snoring and learning problems/snoring and EDS).
Positive Likelihood Ratio, Negative Likelihood Ratio, and Diagnostic Odds Ratio Table IV shows the pooled LRþ, LR, and DOR. None of the symptoms or signs had a pooled LRþ >10, which indicates that the presence of these symptoms and signs may not provide convincing evidence to identify OSA among children. Moreover, none of the symptoms or signs had pooled LR 1); for all symptoms but one (difficulty in breathing), no statistical evidence is provided Laryngoscope 000: Month 2012
for a difference in the diagnostic accuracy of symptoms and signs according to the AHI threshold (Figure 4).
DISCUSSION Methodological Limitations Some methodological limitations of this review should be considered. First, as expected, there was substantial heterogeneity in results across studies. Several factors could contribute to this situation. In the 2005 edition of the International Classification of Sleep Disorders,44 the AASM defined an AHI greater than 1/hour as abnormal in children, but three included studies38,41,43 had other thresholds in their polysomnograph analysis, and one study40 did not specify any threshold. None of the studies clearly described how to identify symptoms and signs, and it is therefore unclear whether there was a substantial variation across studies with regard to the Certal et al.: Clinical Assessment of Pediatric OSA
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TABLE III. Models of Combinations of Symptoms and Signs and Their Diagnostic Accuracy in Predicting Pediatric OSA. Models of Combinations
Author, Year, Reference
200942; 37
Sensitivity
Specificity
Pediatric sleep questionnaire
Sproson et al., Chervin et al., 2007
0.53; 0.78
0.67; 0.72
Modified OSA score: observed apnea, difficulty breathing and parents watching child during sleep
Carroll et al., 199535
0.40
0.92
OSA score: snoring, difficulty in breathing, EDS, and behavior, personality or school performance
Rosen et al., 199934
0.47
0.28
Night sweating, mouth breathing and snoring Observed apnea, nocturnal enuresis, intrusive naps, mouth breathing, and moderate to severe tonsillar hypertrophy Snoring and learning problems
Li et al., 200641 Xu et al., 200643
0.81 0.94
0.57 0.42
Goodwin et al., 200539
0.04
0.99
Snoring and EDS
Goodwin et al., 200539
0.09
0.97
Snoring, pauses, difficulty in breathing, sleep with neck extended, EDS, and adenoid face
Goldstein et al., 199438
0.92
0.29
OSA ¼ obstructive sleep apnea; EDS ¼ excessive daytime somnolence.
explicit criteria for the definition of these symptoms and signs. However, a substantial variation in the implicit threshold could be expected among studies, as the test results (presence or absence of symptoms and signs) depend on the perceptions, interpretation, and judgment of observers. In this review, we used the HSROC model for meta-analyses of diagnostic accuracy, which takes threshold effects into account. The subgroup analysis for the covariate AHI (AHI ¼ 1 vs. AHI >1) did not appear to influence the diagnostic accuracy. In addition, eight34–39,40,42,44 of the 10 studies were conducted in sleep clinics or sleep centers, where the prevalence of pediatric OSA is generally higher than in primary health services. A further decrease in the diagnostic yield may thus be expected when clinical symptoms and signs are used to predict pediatric OSA among children in primary care settings.
Diagnostic Accuracy of Symptoms and Signs for Predicting Pediatric OSA Despite the substantial variation in results among studies, this review demonstrates a poor overall diagnostic accuracy for single symptoms or signs in predicting pediatric OSA. Tonsillar size and snoring reported by parents or caregivers have relatively high sensitivity. Generally, when a test has high sensitivity, a negative
result can be used to rule out the target condition. Thus, the absence of these symptoms and signs may be useful for excluding a diagnosis of OSA. However, the low specificity of these symptoms and signs may lead to a large number of false diagnoses of OSA. In contrast, EDS, observed apnea, and difficulty breathing during sleep have relatively high specificity but low sensitivity. Because a positive result in a highspecificity test can be used to confirm a diagnosis of the target condition, the presence of the above-mentioned symptoms or signs may be useful for identifying OSA. Several models for the combination of symptoms and signs have been proposed; however, the diagnostic performance of these combinations has not yet been well assessed. Eight studies34,35,37–39,41–43 evaluated the diagnostic accuracy of combined symptoms and signs, and the wide variation in models makes it impossible to pool the results. None of the models present reasonable sensitivity and specificity, and there appears to be substantial variation in the diagnostic performance among these models.
Clinical Implications of the Findings This review demonstrates that single and combined symptoms and signs do not have satisfactory diagnostic performance in predicting pediatric OSA. These results,
TABLE IV. Summary Estimates of the Diagnostic Accuracy of Clinical Symptoms and Signs for Pediatric Obstructive Sleep Apnea. Clinical Criteria
Snoring Observed apnea
No. of Patients (Studies)
Sensitivity Range
Specificity Range
Pooled DOR (95% CI)
Pooled LRþ (95% CI)
Pooled LR (95% CI)
753 (5) 273 (4)
0.30–0.97 0.35–0.74
0.26–0.90 0.48–0.95
4.94 (1.93–12.64) 3.73 (1.21–11.54)
1.76 (1.09–2.82) 2.29 (0.94–5.60)
0.36 (0.17–0.76) 0.61 (0.45–0.84)
Difficulty breathing
272 (4)
0.12–0.89
0.42–0.95
3.62 (1.56–8.37)
2.37 (1.49–3.76)
0.65 (0.37–1.17)
EDS Tonsillar size
691 (4) 516 (4)
0.18–0.45 0.48–0.82
0.58–0.86 0.43–0.84
1.59 (1.02–2.47) 3.34 (1.97–5.66)
1.49 (1.01–2.06) 1.73 (1.22–2.45)
0.91 (0.82–1.00) 0.52 (0.40–0.67)
DOR ¼ diagnostic odds ratio; CI ¼ confidence interval; LRþ ¼ positive likelihood ratio; LR ¼ negative likelihood ratio; EDS ¼ excessive daytime somnolence.
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Certal et al.: Clinical Assessment of Pediatric OSA
Fig. 3. Hierarchical summary receiver operating characteristic (HSROC) curves for the diagnostic performance of each symptom and sign. EDS ¼ excessive daytime somnolence.
taken together with the findings from a previous review,9 suggest that the diagnosis of pediatric OSA should be based on more reliable noninvasive methods. In recent years, several diagnostic tools have been developed, which enable some degree of predictability in the detection of OSA or some of its Laryngoscope 000: Month 2012
consequences.9,25,26,33,45,46 However, we should emphasize that such tools have not been extensively evaluated beyond the program where they were developed, and their validity has not been assessed across different countries or settings (i.e., community vs. referral populations). Certal et al.: Clinical Assessment of Pediatric OSA
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Fig. 4. Hierarchical summary receiver operating characteristic (HSROC) curves for the diagnostic performance of each symptom and sign for covariate apnea/hypopnea index (AHI) (AHI ¼ 1 vs. AHI >1). EDS ¼ excessive daytime somnolence.
A recent study,47 based on the conceptual framework that OSAS will result in unique signatures in the expression of genes or proteins, analyzed morning urine samples from 120 children using differential in-gel electrophoresis (2D-DIGE) and found that unique sets of proteins were either increased or decreased in the urine of children with OSA. In addition, ROC analyses using Laryngoscope 000: Month 2012
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more than one of the putative biomarkers showed that if all four proteins were employed, the diagnostic accuracy yielded 100% sensitivity and 96.5% specificity to predict OSA. Further research is needed to explore the diagnostic value of this approach and its value to clinical decision making, but if this approach is validated, it should permit the delineation of specific criteria for Certal et al.: Clinical Assessment of Pediatric OSA
population screening (low cost, high sensitivity, and specificity) and enable priority assignments for treatment and outcome monitoring in children considered to be at increased risk. In the interim, most physicians have to rely on clinical symptoms and signs for identifying possible OSA patients who need further treatment. However, given the substantial variation in the models of combined symptoms and signs reported by the studies included in this analysis, as well as the limited number of studies with each model, further prospective studies are still necessary to compare the diagnostic performance of these models, as well as to develop and validate more effective models for the prediction of pediatric OSA. The new models should include the symptoms and signs with high specificity identified by this systematic review, such as EDS, observed apnea, and difficulty breathing during sleep, and symptoms with high sensitivity such as snoring and tonsillar size. This combination strategy may lead to an increase in sensitivity with acceptable specificity. Different models should be established and validated in various age groups, as many of the symptoms and signs are age dependent. Further studies should define explicit criteria for identifying symptoms and signs, interpret the index tests (symptoms and signs) and reference tests blindly, and include patients from primary care settings.
CONCLUSION This meta-analysis provides a comprehensive critical review of the literature to date and a statistical analysis of the current diagnostic accuracy of clinical symptoms and signs for investigating pediatric OSA. The major finding of this study is that both single and combinations of symptoms and signs have poor diagnostic accuracy in predicting pediatric OSA. It is important to critically evaluate the current evidence to appropriately guide future efforts in this research field. Highquality diagnostic studies that address alternative noninvasive methods in the evaluation of pediatric OSA are still necessary.
Acknowledgment The authors gratefully acknowledge the support provided by Helder Silva, MD, from the hospital Sao Sebastiao.
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