Obstructive sleep apnea (OSA) contributes a major health burden to society due to its high prevalence and substantial neurocognitive and cardiovascular consequences. Estimates suggest that at least 10% of adults in North America are afflicted with OSA, making it probably the most common respiratory disease in the developed world (Peppard et al. Am J Epidemiol. 2013;177:1006). Nasal CPAP is a highly efficacious therapy that has been shown to improve neurocognitive and cardiovascular outcomes. However, CPAP is not always well tolerated. Alternative therapies, such as oral appliances and upper airway surgery, have highly variable efficacy, and evidence of important clinical benefits are uncertain. Therefore, efforts are ongoing to determine optimal alternative strategies for therapy.
In order to treat any condition optimally, one needs to be able to predict who is at highest risk of developing the condition, then to assess the consequences if left untreated, and finally to be able to predict response to various treatment options. Currently, the OSA field is still in its early stages of our understanding. Clinically, we are often faced with patients who have varying presentations and manifestations, but, for reasons that are unclear. For instance, two individuals with the same body mass index may have very different clinical manifestations, one with severe OSA and one without any OSA. Similarly, two individuals with an apnea hypopnea index of 40 events per hour (ie, severe OSA) may have very different symptoms attributable to OSA, eg, one could be asymptomatic and the other could be debilitated from sleepiness. We and others have been making efforts to determine why these phenomenon occur. At present, the techniques to define mechanisms underlying OSA are labor-intensive, requiring one or two overnight experiments to gather meaningful data. Although we are gathering new insights based on these techniques, efforts are ongoing to simplify these approaches and to make assessment of pathophysiologic characteristics more accessible to the clinician (Orr et al. Am J Respir Crit Care Med. 2017 Nov 30. doi: 10.1164/rccm.201707-1357LE. [Epub ahead of print]).
We ultimately believe that a thorough analysis of a sleep recording combined with demographic data and other readily available clinical data (perhaps plasma biomarkers) may yield sufficient information for us to know why OSA is occurring and what interventions might be helpful for an individual patient. Currently, our use of the polysomnogram to derive only an apnea hypopnea index does not take full advantage of the available data. An apnea hypopnea index can be readily obtained from home sleep testing and does not truly provide much insight into why a given individual has OSA, what symptoms are attributable to OSA, and what interventions might be considered for the afflicted individual. By analogy, if the only useful data derived from an ECG were a heart rate, the test would rapidly become obsolete. Along these lines, if the only role for the sleep clinician was to prescribe CPAP to everyone with an AHI greater than 5/h, there would be little need or interest in specialized training. In contrast, we suggest that rich insights regarding pathophysiology and mechanisms should be gathered and may influence clinical management of patients afflicted with OSA. Thus, we encourage more thorough analyses of available data to maximize information gleaned and, ultimately, to optimize clinical outcomes.