BACKGROUND: To address the growing burden of disease and long waiting lists for sleep services, a simplified two-stage model was developed and validated for identifying obstructive sleep apnoea (OSA) in primary care using a screening questionnaire followed by home sleep monitoring. METHODS: 157 patients aged 25-70 years attending their primary care physician for any reason at six primary care clinics in rural and metropolitan regions of South Australia participated. The first 79 patients formed the development group and the next 78 patients the validation group. A screening questionnaire was developed from factors identified from sleep surveys, demographic and anthropometric data to be predictive of moderate to severe OSA. Receiver operating characteristic (ROC) curve analysis was used to validate the two-channel ApneaLink device against full polysomnography. The diagnostic accuracy of the overall two-stage model was then evaluated. RESULTS: Snoring, waist circumference, witnessed apnoeas and age were predictive of OSA and incorporated into a screening questionnaire (ROC area under curve (AUC) 0.84, 95% CI 0.75 to 0.94, p<0.001). ApneaLink oximetry with a 3% dip rate was highly predictive of OSA (AUC 0.96, 95% CI 0.91 to 1.0, p<0.001). The two-stage diagnostic model showed a sensitivity of 0.97 (95% CI 0.81 to 1.00) and specificity of 0.87 (95% CI 0.74 to 0.95) in the development group, and a sensitivity of 0.88 (95% CI 0.60 to 0.98) and specificity of 0.82 (95% CI 0.70 to 0.90) in the validation group. CONCLUSION: A two-stage model of screening questionnaire followed by home oximetry can accurately identify patients with OSA in primary care and has the potential to expedite care for patients with this common sleep disorder.
BACKGROUND: To address the growing burden of disease and long waiting lists for sleep services, a simplified two-stage model was developed and validated for identifying obstructive sleep apnoea (OSA) in primary care using a screening questionnaire followed by home sleep monitoring. METHODS: 157 patients aged 25-70&emsp14;years attending their primary care physician for any reason at six primary care clinics in rural and metropolitan regions of South Australia participated. The first 79 patients formed the development group and the next 78 patients the validation group. A screening questionnaire was developed from factors identified from sleep surveys, demographic and anthropometric data to be predictive of moderate to severe OSA. Receiver operating characteristic (ROC) curve analysis was used to validate the two-channel ApneaLink device against full polysomnography. The diagnostic accuracy of the overall two-stage model was then evaluated. RESULTS: Snoring, waist circumference, witnessed apnoeas and age were predictive of OSA and incorporated into a screening questionnaire (ROC area under curve (AUC) 0.84, 95% CI 0.75 to 0.94, p<0.001). ApneaLink oximetry with a 3% dip rate was highly predictive of OSA (AUC 0.96, 95% CI 0.91 to 1.0, p<0.001). The two-stage diagnostic model showed a sensitivity of 0.97 (95% CI 0.81 to 1.00) and specificity of 0.87 (95% CI 0.74 to 0.95) in the development group, and a sensitivity of 0.88 (95% CI 0.60 to 0.98) and specificity of 0.82 (95% CI 0.70 to 0.90) in the validation group. CONCLUSION: A two-stage model of screening questionnaire followed by home oximetry can accurately identify patients with OSA in primary care and has the potential to expedite care for patients with this common sleep disorder.
Authors: Kirk Kee; John Dixon; Jonathan Shaw; Elena Vulikh; Markus Schlaich; David M Kaye; Paul Zimmet; Matthew T Naughton Journal: J Clin Sleep Med Date: 2018-12-15 Impact factor: 4.062
Authors: Lien Desteghe; Jeroen M L Hendriks; R Doug McEvoy; Ching Li Chai-Coetzer; Paul Dendale; Prashanthan Sanders; Hein Heidbuchel; Dominik Linz Journal: Clin Res Cardiol Date: 2018-04-12 Impact factor: 5.460
Authors: Joule J Li; Sarah L Appleton; Gary A Wittert; Andrew Vakulin; R Douglas McEvoy; Nick A Antic; Robert J Adams Journal: Sleep Date: 2014-03-01 Impact factor: 5.849