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Published on: December 6, 2016
Feasibility study of determining a risk assessment model for obstructive sleep apnea in children based on local
Zlatko Kljajic1, Sandro Glumac2, Judith Adrianne Deutsch3
1Department of Otorhinolaryngology and Aesthetic Surgery, Polyclinic "Bagatin", Split, Croatia.
Insights
A new model using clinical factors effectively diagnoses obstructive sleep apnea (OSA) in children, showing strong correlation with polysomnography results. This offers a reliable diagnostic tool for pediatric OSA.
Area of Science:
- Pediatric Pulmonology
- Sleep Medicine
- Diagnostic Accuracy Studies
Background:
- Obstructive sleep apnea (OSA) is a common condition in children.
- Polysomnography is the gold standard for OSA diagnosis but can be resource-intensive.
- There is a need for feasible and reliable diagnostic models for pediatric OSA.
Purpose of the Study:
- To develop and validate a clinical model for diagnosing pediatric OSA.
- To compare the diagnostic accuracy of the clinical model against polysomnography.
Main Methods:
- Ninety-four children with suspected OSA were analyzed.
- Clinical parameters including modified Mallampati score, tonsil/adenoid size, age, gender, and BMI were assessed.
- Logistic regression and ROC curve analysis were used to evaluate the model's performance against the apnea-hypopnea index (AHI) from polysomnography.
Main Results:
- All assessed clinical parameters showed significant association with AHI (P < 0.001).
- Modified Mallampati score (r=0.723), tonsil size (r=0.673), and adenoid size (r=0.502) were most strongly correlated with AHI.
- The developed model demonstrated 84% sensitivity and 74% specificity.
Conclusions:
- A clinical model based on local findings provides a reliable method for diagnosing pediatric OSA.
- This model shows significant overlap with polysomnography results.
- The findings support the use of this clinical model as a feasible diagnostic tool for pediatric OSA.
Objective:
To test a feasible and reliable model for diagnosing obstructive sleep apnea (OSA) in children, based on the clinically relevant parameters, in comparison to a polysomnography.
Methods:
A total of 94 children with the suspected underlying OSA were included in the analyses. An association between clinical parameters (modified Mallampati score, tonsil size, adenoid size, age, gender, and body mass index) and apnea-hypopnea index (AHI) obtained following an overnight polysomnography was assessed, and significant variables were incorporated in the logistic regression model. Also, the sensitivity and specificity calculations of the model with the inclusion of ROC curve analysis were performed.
Results:
All tee local clinical parameters were significantly associated with AHI (P < 0.001). The most significant correlation with AHI was shown with the modified Mallampati score (r = 0.723), following with tonsil size (r = 0.673), and adenoid size (r = 0.502). The sensitivity of the tested model was 84%, and specificity was 74%.
Conclusion:
This study derived a model based on the local clinical findings that significantly overlapped with the results of an overnight polysomnography, in diagnosing OSA in children.
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