Dataset Information

Development of a pediatric obstructive sleep apnea triage algorithm.

ABSTRACT:

Introduction

Diagnosis and treatment of obstructive sleep apnea (OSA) in children is often delayed due to the high prevalence and limited physician and sleep testing resources. As a result, children may be referred to multiple specialties, such as pediatric sleep medicine and pediatric otolaryngology, resulting in long waitlists.

Method

We used data from our pediatric OSA clinic to identify predictors of tonsillectomy and/or adenoidectomy (AT). Before being seen in the clinic, parents completed the Pediatric Sleep Questionnaire (PSQ) and screening questionnaires for restless leg syndrome (RLS), nasal rhinitis, and gastroesophageal reflux disease (GERD). Tonsil size data were obtained from patient charts and graded using the Brodsky-five grade scale. Children completed an overnight oximetry study before being seen in the clinic, and a McGill oximetry score (MOS) was assigned based on the number and depth of oxygen desaturations. Logistic regression, controlling for otolaryngology physician, was used to identify significant predictors of AT. Three triage algorithms were subsequently generated based on the univariate and multivariate results to predict AT.

Results

From the OSA cohort, there were 469 eligible children (47% female, mean age = 8.19 years, SD = 3.59), with 89% of children reported snoring. Significant predictors of AT in univariate analysis included tonsil size and four PSQ questions, (1) struggles to breathe at night, (2) apneas, (3) daytime mouth breathing, and (4) AM dry mouth. The first triage algorithm, only using the four PSQ questions, had an odds ratio (OR) of 4.02 for predicting AT (sensitivity = 0.28, specificity = 0.91). Using only tonsil size, the second algorithm had an OR to predict AT of 9.11 (sensitivity = 0.72, specificity = 0.78). The third algorithm, where MOS was used to stratify risk for AT among those children with 2+ tonsils, had the same OR, sensitivity, and specificity as the tonsil-only algorithm.

Conclusion

Tonsil size was the strongest predictor of AT, while oximetry helped stratify individual risk for AT. We recommend that referral letters for snoring children include graded tonsil size to aid in the triage based on our findings. Children with 2+ tonsil sizes should be triaged to otolaryngology, while the remainder should be referred to a pediatric sleep specialist.

SUBMITTER: Heath DS

PROVIDER: S-EPMC8281470 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Rationale: Obstructive sleep apnea (OSA) is common in pregnancy and associated with maternal and fetal complications. Early detection of OSA may have important implications for maternal-fetal well-being. A screening tool combining several methods of assessment may better predict OSA among pregnant women compared with tools that rely solely on self-reported information.Objectives: To develop a screening tool combining subjective and objective measures to predict OSA in pregnant women.Methods: This study is a secondary analysis using data collected from a completed cohort of pregnant women (n = 121 during the first and n = 87 during the third trimester). Participants underwent full polysomnography and completed the Multivariable Apnea Prediction Questionnaire. The Obstructive Sleep Apnea/Hypopnea Syndrome Score and Facco apnea predictive model were obtained. Logistic regression analysis and area under the curve (AUC) were used to identify models predicting OSA risk.Results: Participants' mean age was 27.4 ± 7.0 years. The prevalence of OSA during the first and third trimester was 10.7% and 24.1%, respectively. The final model predicting OSA risk consisted of body mass index, age, and presence of tongue enlargement. During the first trimester, the AUC was 0.86 (95% confidence interval [CI], 0.76-0.96). During the third trimester, the AUC was 0.87 (95% CI, 0.77-0.96). When the first-trimester data were used to predict third-trimester OSA risk, the AUC was 0.87 (95% CI, 0.77-0.97). This model had high sensitivity and specificity when used during both trimesters. The negative posttest probabilities (probability of OSA given a negative test result) ranged from 0.03 to 0.07.Conclusions: A new model consisting of body mass index, age, and presence of tongue enlargement provided accurate screening of OSA in pregnant women, particularly African-Americans. This tool can be easily and rapidly administered in busy clinical practices without depending on patients' awareness of experiencing apnea symptoms.

Project description:BackgroundThe American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard.ObjectiveThis study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors.MethodsA total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B).ResultsA total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14).ConclusionsDefined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Project description:Study objectivesObstructive sleep apnea is associated with neurobehavioral dysfunction, but the relationship between disease severity as measured by the apnea-hypopnea index and neurobehavioral morbidity is unclear. The objective of our study is to compare the neurobehavioral morbidity of mild sleep-disordered breathing versus obstructive sleep apnea.MethodsChildren 3-12 years old recruited for mild sleep-disordered breathing (snoring with obstructive apnea-hypopnea index < 3) into the Pediatric Adenotonsillectomy Trial for Snoring were compared to children 5-9 years old recruited for obstructive sleep apnea (obstructive apnea-hypopnea 2-30) into the Childhood Adenotonsillectomy Trial. Baseline demographic, polysomnographic, and neurobehavioral outcomes were compared using univariable and multivariable analysis.ResultsThe sample included 453 participants with obstructive sleep apnea (median obstructive apnea-hypopnea index 5.7) and 459 participants with mild sleep-disordered breathing (median obstructive apnea-hypopnea index 0.5). By polysomnography, participants with obstructive sleep apnea had poorer sleep efficiency and more arousals. Children with mild sleep-disordered breathing had more abnormal executive function scores (adjusted odds ratio 1.96, 95% CI 1.30-2.94) compared to children with obstructive sleep apnea. There were also elevated Conners scores for inattention (adjusted odds ratio 3.16, CI 1.98-5.02) and hyperactivity (adjusted odds ratio 2.82, CI 1.83-4.34) in children recruited for mild sleep-disordered breathing.ConclusionsAbnormal executive function, inattention, and hyperactivity were more common in symptomatic children recruited into a trial for mild sleep-disordered breathing compared to children recruited into a trial for obstructive sleep apnea. Young, snoring children with only minimally elevated apnea-hypopnea levels may still be at risk for deficits in executive function and attention.Trial registrationPediatric Adenotonsillectomy for Snoring (PATS), NCT02562040; Childhood Adenotonsillectomy Trial (CHAT), NCT00560859.