Dataset Information

The Use of Computer Decision Support for Pediatric Obstructive Sleep Apnea Detection in Primary Care.

ABSTRACT: STUDY OBJECTIVES:To (1) describe outcomes from a computer decision support system (CDSS) for pediatric obstructive sleep apnea (OSA) detection in primary care; and (2) identity the prevalence of children meeting criteria for an OSA referral. METHODS:A CDSS for OSA was implemented in two urban primary care clinics. Parents of children (age 2 to 11 years) presenting to the clinic were asked if their child snored regularly, with a positive response resulting in six additional OSA screening items. Primary care providers (PCPs) received a prompt for all snoring children, listing applicable OSA signs and symptoms and recommending further evaluation and referral for OSA. RESULTS:A total of 2,535 children were screened for snoring, identifying 475 snoring children (18.7%). Among snoring children, PCPs referred 40 (15.4%) for further evaluation. The prevalence of additional OSA signs and symptoms ranged from 3.5% for underweight to 43.7% for overweight. A total of 74.7% of snoring children had at least one additional sign or symptom and thus met American Academy of Pediatrics guidelines criteria for an OSA referral. CONCLUSIONS:A CDSS can be used to support PCPs in identifying children at risk for OSA. Most snoring children met criteria for further evaluation. It will be important to further evaluate this referral threshold as well as the readiness of the sleep medicine field to meet this need. CLINICAL TRIALS REGISTRATION:Registry: ClinicalTrials.gov, Title: Evidence-based Diagnosis and Management of Pediatric Obstructive Sleep Apnea in Primary Care, Identifier: NCT02781376, URL: https://clinicaltrials.gov/ct2/show/NCT02781376.

SUBMITTER: Honaker SM

PROVIDER: S-EPMC6411194 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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The Use of Computer Decision Support for Pediatric Obstructive Sleep Apnea Detection in Primary Care.

Honaker Sarah M SM Street Ashley A Daftary Ameet S AS Downs Stephen M SM

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 20190315 3

<h4>Study objectives</h4>To (1) describe outcomes from a computer decision support system (CDSS) for pediatric obstructive sleep apnea (OSA) detection in primary care; and (2) identity the prevalence of children meeting criteria for an OSA referral.<h4>Methods</h4>A CDSS for OSA was implemented in two urban primary care clinics. Parents of children (age 2 to 11 years) presenting to the clinic were asked if their child snored regularly, with a positive response resulting in six additional OSA scr ...[more]

PMID: 30853049

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Project description:INTRODUCTION:Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. MATERIALS AND METHODS:Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. RESULTS:A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. DISCUSSION:We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. CONCLUSIONS:Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. TRIAL REGISTRATION:ClinicalTrials.gov NCT01161381.

Project description:IntroductionDiagnosis 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.MethodWe 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.ResultsFrom 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.ConclusionTonsil 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.

Dataset Information

The Use of Computer Decision Support for Pediatric Obstructive Sleep Apnea Detection in Primary Care.

Publications

The Use of Computer Decision Support for Pediatric Obstructive Sleep Apnea Detection in Primary Care.

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