Dataset Information

Development of a Sleep Apnea-Specific Health State Utility Algorithm.

ABSTRACT:

Importance

With the increasing emphasis on economic evaluations, there is a need for additional methods of measuring patient utility in the obstructive sleep apnea population.

Objective

To develop and validate a utility scoring algorithm for a sleep apnea-specific quality-of-life instrument.

Design, setting, and participants

Development and validation were conducted at 2 tertiary referral sleep centers and associated sleep clinics and included patients with newly diagnosed obstructive sleep apnea from a randomized clinical trial and an associated observational cohort study. Baseline participants were randomly divided into a model development group (60%) and a cross-validation group (40%).

Main outcomes and measures

Utility scoring of the Symptoms of Nocturnal Obstruction and Related Events (SNORE-25) was mapped from the SF-6D utility index through multiple linear regression in the development sample using the Akaike information criterion to determine the best model.

Results

A total of 500 participants (development, n?=?300; validation, n?=?200) were enrolled; the analyzed sample of 500 participants included 295 men (59%), and the mean (SD) age was 48.6 (12.8) years, with a range of 18 to 90 years. The mean (SD) SF-6D utility among participants with untreated sleep apnea was 0.61 (0.08; range, 0.40-0.85) with similar utility across sleep apnea severity groups. The best-fit model (the SNORE Utility Index) was the natural log conversion of the instrument subscales (r2?=?0.32 in the development sample). The SNORE Utility Index retained this association within the validation sample (r2?=?0.33).

Conclusions and relevance

The SNORE Utility Index provides a validated, disease-specific, preference-weighted utility instrument that can be used in future studies of patients with obstructive sleep apnea.

SUBMITTER: Skirko JR

PROVIDER: S-EPMC7042933 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Development of a Sleep Apnea-Specific Health State Utility Algorithm.

Skirko Jonathan R JR James Kathryn T KT Garrison Louis P LP Weaver Edward M EM

JAMA otolaryngology-- head & neck surgery 20200301 3

<h4>Importance</h4>With the increasing emphasis on economic evaluations, there is a need for additional methods of measuring patient utility in the obstructive sleep apnea population.<h4>Objective</h4>To develop and validate a utility scoring algorithm for a sleep apnea-specific quality-of-life instrument.<h4>Design, setting, and participants</h4>Development and validation were conducted at 2 tertiary referral sleep centers and associated sleep clinics and included patients with newly diagnosed ...[more]

PMID: 31999308

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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.

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: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.

Dataset Information

Development of a Sleep Apnea-Specific Health State Utility Algorithm.

Importance

Objective

Design, setting, and participants

Main outcomes and measures

Results

Conclusions and relevance

Publications

Development of a Sleep Apnea-Specific Health State Utility Algorithm.

Similar Datasets

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