Defining Extreme Phenotypes of OSA Across International Sleep Centers

Abstract

BACKGROUND: Extreme phenotypes of OSA have not been systematically defined. RESEARCH QUESTION: This study developed objective definitions of extreme phenotypes of OSA by using a multivariate approach. The utility of these definitions for identifying characteristics that confer predisposition toward or protection against OSA is shown in a new prospective sample. STUDY DESIGN AND METHODS: In a large international sample, race-specific liability scores were calculated from a weighted logistic regression that included age, sex, and BMI. Extreme cases were defined as individuals with an apnea-hypopnea index (AHI) $ 30 events/hour but low likelihood of OSA based on age, sex, and BMI (liability scores > 90th percentile). Similarly, extreme controls were individuals with an AHI < 5 events/hour but high likelihood of OSA (liability scores < 10th percentile). Definitions were applied to a prospective sample from the Sleep Apnea Global Interdisciplinary Consortium, and differences in photographybased craniofacial and intraoral phenotypes were evaluated. RESULTS: This study included retrospective data from 81,338 individuals. A total of 4,168 extreme cases and 1,432 extreme controls were identified by using liability scores. Extreme cases were younger (43.1 AE 14.7 years), overweight (28.6 AE 6.8 kg/m 2), and predominantly female (71.1%). Extreme controls were older (53.8 AE 14.1 years), obese (34.0 AE 8.1 kg/m 2), and predominantly male (65.8%). These objective definitions identified 29 extreme cases and 87 extreme controls among 1,424 Sleep Apnea Global Interdisciplinary Consortium participants with photography-based phenotyping. Comparisons suggest that a greater cervicomental angle increases risk for OSA in the absence of clinical risk factors, and smaller facial widths are protective in the presence of clinical risk factors. INTERPRETATION: This objective definition can be applied in sleep centers throughout the world to consistently define OSA extreme phenotypes for future studies on genetic, anatomic, and physiologic pathways to OSA.

Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples

“…40 Although age, sex, and BMI are traditional risk factors used toward OSA identification, some studies, including work from our group, have also observed that ethnicity/race and craniofacial features are potential predictive factors. 10,11,41,42 However, efficient identification of OSA remains problematic. Thus, there is a critical need for simple OSA prediction tools that could lead to widespread identification of the condition.…”

“…Although there were differences in the desaturation criteria, this large dataset (N ¼ 17,448) allowed for model development based on a generally uniform standard among all sites. 10,12,17 The incorporation of data from varying ethnicities also adds to the generalizability of the prediction models. In addition, the data used in these models represent readily available information from most clinical encounters.…”

“…Of the 17,731 individuals in the retrospective dataset, those with complete data for analysis (N ¼ 17,448) were randomly split into a training dataset with 60% of the subjects (n ¼ 10,469) and a validation dataset with the remaining 40% (n ¼ 6,979). 10…”

“…We sought to compare the diagnostic performance of the prediction tools vs a symptomless LOG model in a large retrospective dataset from the international Sleep Apnea Global Interdisciplinary Consortium (SAGIC). [10][11][12][13] We hypothesized that these machine learning methods would outperform LOG. Our second aim was to assess the diagnostic performance of the machine learning models and…”

Simple and Unbiased OSA Prescreening: Introduction of a New Morphologic OSA Prediction Score

“…Obstructive sleep apnea (OSA) is a chronic condition caused by recurrent partial (hypopnea) or complete (apnea) upper-airway collapse during sleep and presents with a wide range of symptoms, including snoring and daytime sleepiness. 1,2 The estimated global prevalence of moderate to severe OSA amounts to over 425 million adults. [3][4][5] OSA leads to significant performance impairment, increased risk for accidents and elevated risk for metabolic and cardiovascular disease, depression, dementia, and stroke.…”