Prevalence and Diagnostic Approach to Sleep Apnea in Hemodialysis Patients: A Population Study. (1st July 2015)

• Record Type: Journal Article
• Authors: Forni Ogna, Valentina; Ogna, Adam; Pruijm, Menno; Bassi, Isabelle; Zuercher, Emilie; Halabi, Georges; Phan, Olivier; Bullani, Roberto; Teta, Daniel; Gauthier, Thierry; Cherpillod, Anne; Mathieu, Claudine; Mihalache, Alexandra; Cornette, Francoise; Haba-Rubio, José; Burnier, Michel; Heinzer, Raphaël
• Other Names: Siafakas Nikolaos Academic Editor.
• Is Part Of: BioMed research international. Volume 2015(2015)
• Publication Date: 2015-07-01
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2015/103686
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Background . Previous observations found a high prevalence of obstructive sleep apnea (OSA) in the hemodialysis population, but the best diagnostic approach remains undefined. We assessed OSA prevalence and performance of available screening tools to propose a specific diagnostic algorithm. Methods . 104 patients from 6 Swiss hemodialysis centers underwent polygraphy and completed 3 OSA screening scores: STOP-BANG, Berlin's Questionnaire, and Adjusted Neck Circumference. The OSA predictors were identified on a derivation population and used to develop the diagnostic algorithm, which was validated on an independent population. Results . We found 56% OSA prevalence (AHI ≥ 15/h), which was largely underdiagnosed. Screening scores showed poor performance for OSA screening (ROC areas 0.538 [SE 0.093] to 0.655 [SE 0.083]). Age, neck circumference, and time on renal replacement therapy were the best predictors of OSA and were used to develop a screening algorithm, with higher discriminatory performance than classical screening tools (ROC area 0.831 [0.066]). Conclusions . Our study confirms the high OSA prevalence and highlights the low diagnosis rate of this treatable cardiovascular risk factor in the hemodialysis population. Considering the poor performance of OSA screening tools, we propose and validate a specific algorithm to identify hemodialysis patients at risk for OSA for whom further sleep investigations should be considered.