Ricardo L M Duarte1,2, Marcelo F Rabahi3, Flavio J Magalhães-da-Silveira1, Tiago S de Oliveira-E-Sá4,5, Fernanda C Q Mello2, David Gozal6. 1. Sleep - Laboratório de Estudo dos Distúrbios do Sono, Centro Médico BarraShopping, Rio de Janeiro, Brazil. 2. Instituto de Doenças do Tórax - Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. 3. Faculdade de Medicina, Universidade Federal de Goiás, Goiás, Brazil. 4. Hospital de Santa Marta - Centro Hospitalar Lisboa Central, Portugal. 5. NOVA Medical School - Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Portugal. 6. Department of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, The University of Chicago, Chicago, Illinois.
Abstract
STUDY OBJECTIVES: To develop and validate a practical model for obstructive sleep apnea (OSA) screening in adults based on objectively assessed criteria, and then compare it with two widely used tools, namely STOP-BANG and NoSAS. METHODS: This is a retrospective study of an existing database of consecutive outpatients who were referred for polysomnography for suspected sleep-disordered breathing by their primary care physicians. Area under the curve (AUC) and 2 × 2 contingency tables were employed to obtain the performance of the new instrument. RESULTS: A total of 4,072 subjects were randomly allocated into two independent cohorts: one for derivation (n = 2,037) and one for validation (n = 2,035). A mnemonic model, named No-Apnea, with two variables (neck circumference and age) was developed (total score: 0-9 points). We used the cutoff ≥ 3 to classify patients at high risk of having OSA. OSA severity was categorized by apnea-hypopnea index (AHI): any OSA (AHI 5 ≥ events/h; OSA-5), moderate/ severe OSA (AHI 15 ≥ events/h; OSA-15); and severe OSA (AHI 30 ≥ events/h; OSA-30). In the derivation cohort, the AUCs for screening of OSA-5, OSA-15, and OSA-30 were: 0.784, 0.758, and 0.754; respectively. The rate of subjects correctly screened was 78.1%, 68.8%, and 54.4%, respectively for OSA-5, OSA-15, and OSA-30. Subsequently, the model was validated confirming its reproducibility. In both cohorts, No-Apnea discrimination was similar to STOP-BANG or NoSAS. CONCLUSIONS: The No-Apnea, a 2-item model, appears to be a useful and practical tool for OSA screening, mainly when limited resources constrain referral evaluation. Despite its simplicity when compared to previously validated tools (STOP-BANG and NoSAS), the instrument exhibits similar performance characteristics.
STUDY OBJECTIVES: To develop and validate a practical model for obstructive sleep apnea (OSA) screening in adults based on objectively assessed criteria, and then compare it with two widely used tools, namely STOP-BANG and NoSAS. METHODS: This is a retrospective study of an existing database of consecutive outpatients who were referred for polysomnography for suspected sleep-disordered breathing by their primary care physicians. Area under the curve (AUC) and 2 × 2 contingency tables were employed to obtain the performance of the new instrument. RESULTS: A total of 4,072 subjects were randomly allocated into two independent cohorts: one for derivation (n = 2,037) and one for validation (n = 2,035). A mnemonic model, named No-Apnea, with two variables (neck circumference and age) was developed (total score: 0-9 points). We used the cutoff ≥ 3 to classify patients at high risk of having OSA. OSA severity was categorized by apnea-hypopnea index (AHI): any OSA (AHI 5 ≥ events/h; OSA-5), moderate/ severe OSA (AHI 15 ≥ events/h; OSA-15); and severe OSA (AHI 30 ≥ events/h; OSA-30). In the derivation cohort, the AUCs for screening of OSA-5, OSA-15, and OSA-30 were: 0.784, 0.758, and 0.754; respectively. The rate of subjects correctly screened was 78.1%, 68.8%, and 54.4%, respectively for OSA-5, OSA-15, and OSA-30. Subsequently, the model was validated confirming its reproducibility. In both cohorts, No-Apnea discrimination was similar to STOP-BANG or NoSAS. CONCLUSIONS: The No-Apnea, a 2-item model, appears to be a useful and practical tool for OSA screening, mainly when limited resources constrain referral evaluation. Despite its simplicity when compared to previously validated tools (STOP-BANG and NoSAS), the instrument exhibits similar performance characteristics.
Authors: N S Marshall; L Delling; R R Grunstein; M Peltonen; C D Sjöström; K Karason; L M S Carlsson; J Hedner; K Stenlöf; L Sjöström Journal: Eur Respir J Date: 2011-05-26 Impact factor: 16.671
Authors: Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan Journal: Epidemiology Date: 2010-01 Impact factor: 4.822
Authors: Ricardo L M Duarte; Fernanda C Q Mello; Flavio J Magalhães-da-Silveira; Tiago S Oliveira-E-Sá; Marcelo F Rabahi; David Gozal Journal: Sleep Breath Date: 2019-02-08 Impact factor: 2.816
Authors: Ricardo L M Duarte; Flavio J Magalhães-da-Silveira; Tiago S Oliveira-E-Sá; Marcelo F Rabahi; Fernanda C Q Mello; David Gozal Journal: Lung Date: 2019-05-10 Impact factor: 2.584
Authors: Ricardo L M Duarte; Marcelo F Rabahi; Tiago S Oliveira-E-Sá; Flavio J Magalhães-da-Silveira; Fernanda C Q Mello; David Gozal Journal: Lung Date: 2019-01-02 Impact factor: 2.584
Authors: Kimberly Y Kreitinger; Macy M S Lui; Robert L Owens; Christopher N Schmickl; Eduardo Grunvald; Santiago Horgan; Janna R Raphelson; Atul Malhotra Journal: Obesity (Silver Spring) Date: 2020-11 Impact factor: 5.002