STUDY OBJECTIVES: Rules for classifying apneas as obstructive, central, or mixed are well established. Although hypopneas are given equal weight when calculating the apnea-hypopnea index, classification is not standardized. Visual methods for classifying hypopneas have been proposed by the American Academy of Sleep Medicine and by Randerath et al (Sleep. 2013;36[3]:363-368) but never compared. We evaluated the clinical suitability of the 2 visual methods for classifying hypopneas as central or obstructive. METHODS: Fifty hypopnea-containing polysomnographic segments were selected from patients with clear obstructive or clear central physiology to serve as standard obstructive or central hypopneas. These 100 hypopnea-containing polysomnographic segments were deidentified, randomized, and scored by 2 groups. We assigned 1 group to use the American Academy of Sleep Medicine criteria and the other the Randerath algorithm. After a washout period, re-randomized hypopnea-containing polysomnographic segments were scored using the alternative method. We determined the accuracy (agreement with standard), interrater (Fleiss's κ), and intrarater agreement (Cohen's κ) for obtained scores. RESULTS: Accuracy of the 2 methods was similar: 67% vs 69.3% for Randerath et al and the American Academy of Sleep Medicine, respectively. Cohen's κ was 0.01-0.75, showing that some raters scored similarly using the 2 methods, while others scored them markedly differently. Fleiss's κ for the American Academy of Sleep Medicine algorithm was 0.32 (95% confidence interval, 0.29-0.36) and for the Randerath algorithm was 0.27 (95% confidence interval, 0.23-0.30). CONCLUSIONS: More work is needed to discover a noninvasive way to accurately characterize hypopneas. Studies like ours may lay the foundation for discovering the full spectrum of physiologic consequences of obstructive sleep apnea and central sleep apnea.
STUDY OBJECTIVES: Rules for classifying apneas as obstructive, central, or mixed are well established. Although hypopneas are given equal weight when calculating the apnea-hypopnea index, classification is not standardized. Visual methods for classifying hypopneas have been proposed by the American Academy of Sleep Medicine and by Randerath et al (Sleep. 2013;36[3]:363-368) but never compared. We evaluated the clinical suitability of the 2 visual methods for classifying hypopneas as central or obstructive. METHODS: Fifty hypopnea-containing polysomnographic segments were selected from patients with clear obstructive or clear central physiology to serve as standard obstructive or central hypopneas. These 100 hypopnea-containing polysomnographic segments were deidentified, randomized, and scored by 2 groups. We assigned 1 group to use the American Academy of Sleep Medicine criteria and the other the Randerath algorithm. After a washout period, re-randomized hypopnea-containing polysomnographic segments were scored using the alternative method. We determined the accuracy (agreement with standard), interrater (Fleiss's κ), and intrarater agreement (Cohen's κ) for obtained scores. RESULTS: Accuracy of the 2 methods was similar: 67% vs 69.3% for Randerath et al and the American Academy of Sleep Medicine, respectively. Cohen's κ was 0.01-0.75, showing that some raters scored similarly using the 2 methods, while others scored them markedly differently. Fleiss's κ for the American Academy of Sleep Medicine algorithm was 0.32 (95% confidence interval, 0.29-0.36) and for the Randerath algorithm was 0.27 (95% confidence interval, 0.23-0.30). CONCLUSIONS: More work is needed to discover a noninvasive way to accurately characterize hypopneas. Studies like ours may lay the foundation for discovering the full spectrum of physiologic consequences of obstructive sleep apnea and central sleep apnea.
Authors: Richard B Berry; Rita Brooks; Charlene Gamaldo; Susan M Harding; Robin M Lloyd; Stuart F Quan; Matthew T Troester; Bradley V Vaughn Journal: J Clin Sleep Med Date: 2017-05-15 Impact factor: 4.062
Authors: S Redline; V K Kapur; M H Sanders; S F Quan; D J Gottlieb; D M Rapoport; W H Bonekat; P L Smith; J P Kiley; C Iber Journal: Am J Respir Crit Care Med Date: 2000-02 Impact factor: 21.405
Authors: Warren R Ruehland; Peter D Rochford; Fergal J O'Donoghue; Robert J Pierce; Parmjit Singh; Andrew T Thornton Journal: Sleep Date: 2009-02 Impact factor: 5.849
Authors: Richard B Berry; Rohit Budhiraja; Daniel J Gottlieb; David Gozal; Conrad Iber; Vishesh K Kapur; Carole L Marcus; Reena Mehra; Sairam Parthasarathy; Stuart F Quan; Susan Redline; Kingman P Strohl; Sally L Davidson Ward; Michelle M Tangredi Journal: J Clin Sleep Med Date: 2012-10-15 Impact factor: 4.062
Authors: Ankit Parekh; Thomas M Tolbert; Anne M Mooney; Jaime Ramos-Cejudo; Ricardo S Osorio; Marcel Treml; Simon-Dominik Herkenrath; Winfried J Randerath; Indu Ayappa; David M Rapoport Journal: Am J Respir Crit Care Med Date: 2021-12-15 Impact factor: 21.405