Obstructive sleep apnea (OSA) is a prevalent disease. Often limited clinical resources result in long patient waiting lists. Simpler validated methods of care are needed.
To demonstrate that a nurse-led model of care can produce health outcomes in symptomatic moderate-severe OSA not inferior to physician-led care.
A randomized controlled multicenter noninferiority clinical trial was performed. Of 1,427 potentially eligible patients at 3 centers, 882 consented to the trial. Of these, 263 were excluded on the basis of clinical criteria. Of the remaining 619, 195 met home oximetry criteria for high-probability moderate-severe OSA and were randomized to 2 models of care: model A, the simplified model, using home autoadjusting positive airway pressure to set therapeutic continuous positive airway pressure (CPAP), with all care supervised by an experienced nurse; and model B, involving two laboratory polysomnograms to diagnose and treat OSA, with clinical care supervised by a sleep physician. The primary end point was change in Epworth Sleepiness Scale (ESS) score after 3 months of CPAP. Other outcome measures were collected.
For the primary outcome change in ESS score, nurse-led management was no worse than physician-led management (4.02 vs. 4.15; difference, -0.13; 95% confidence interval: -1.52, 1.25) given a prespecified noninferiority margin of -2 for the lower 95% confidence interval. There were also no differences between both groups in CPAP adherence at 3 months or other outcome measures. Within-trial costs were significantly less in model A.
A simplified nurse-led model of care has demonstrated noninferior results to physician-directed care in the management of symptomatic moderate-severe OSA, while being less costly. Clinical trial registered with http://www.anzctr.org.au (ACTRN012605000064606).
"According to Thalhofer and Dorow (1997) CSA is characterized by repeated apnoeas during sleep resulting from loss of respiratory effort. OSA has been shown to increase the risk of motor vehicle accidents, hypertension and possibly stroke and heart failure (Antic et al 2009) and is prevalent around the world (table 2). "
[Show abstract][Hide abstract] ABSTRACT: This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.
"These repeated arousals cause sleep fragmentation which leads to daytime sleepiness (Collop 2007). OSA has been shown to increase the risk of motor vehicle accidents, hypertension, stroke, heart disease and diabetes (Antic et al 2009, Collop 2007) and is prevalent around the world. The prevalence of OSA ranges from 2% to 7.5% depending on gender and race or location (Bearpark et al 1995, Bixler et al 2001, Ip et al 2001, 2004, Kim et al 2004, Lam et al 2007, Sharma et al 2006, Udwadia et al 2004, Young et al 1993). "
[Show abstract][Hide abstract] ABSTRACT: Sleep disorders are a common problem and contribute to a wide range of healthcare issues. The societal and financial costs of sleep disorders are enormous. Sleep-related disorders are often diagnosed with an overnight sleep test called a polysomnogram, or sleep study involving the measurement of brain activity through the electroencephalogram. Other parameters monitored include oxygen saturation, respiratory effort, cardiac activity (through the electrocardiogram), as well as video recording, sound and movement activity. Monitoring can be costly and removes the patients from their normal sleeping environment, preventing repeated unbiased studies. The recent increase in adoption of smartphones, with high quality on-board sensors has led to the proliferation of many sleep screening applications running on the phone. However, with the exception of simple questionnaires, no existing sleep-related application available for smartphones is based on scientific evidence. This paper reviews the existing smartphone applications landscape used in the field of sleep disorders and proposes possible advances to improve screening approaches.
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