Assessment of sleep‐disordered‐breathing: Quest for a metric or search for meaning?

Abstract

When Christian Guilleminault described, with Bill Dement, the concept of sleep apnea syndromes (Guilleminault, Eldridge, & Dement, 1973), it actually referred to previous observations of various forms of manifestation of cessation of breathing during sleep that had been reported since the 19th century (Lavie, 2008). However, all clinicians and researchers who have been working in this field since are greatly indebted to the late Christian Guilleminault, who was a passionate pioneer and spent his whole life discovering the many facets of sleep-disordered breathing and dissecting the complexity of the disease. In this issue, Pevernagie et al. report on an historical review and critical appraisal of the metrics currently used to measure sleep apnea (i.e., the apnea–hypopnea index [AHI]) (Pevernagie et al., 2020). This is a very timely and also comprehensive document. The journey starts in the seventies with a seminal paper included in the book edited by Guilleminault and Dement entitled ‘Sleep Apnea Syndromes’ (Guilleminault, Van Den Hoed, & Mitler, 1978). In this chapter, they for the first time proposed not only to characterize the clinical features but also to quantify the burden and severity of sleep apnea, introducing the concept of the apnea index (AI). This captured the number of breathing cessations per hour of sleep. A metric conceptualizing sleep apnea and aiming to anticipate its consequences was born. This subsequently gained wide acceptance, although, as suggested by Pevernagie et al. (2020), there have been persistent concerns with respect to validation and reproducibility. The concept of the AHI capturing not only apneas but also hypopneas emerged at the end of the 1980s but was only fully standardized in the 1990s. This was summarized in a consensus document reporting on a working group of the American Academy of Sleep Medicine (1999). This is one of the most cited papers in the sleep field (total citations 3,849 and about 175 per year! [source Web of Science, June 2020]), although, as indicated in the title, it is dedicated to clinical research and not clinical practice. It is of interest to note that some of the major scientific societies (i.e., the European Respiratory Society, the Australasian Sleep Association and the American Thoracic Society) involved in the management of sleep apnea syndrome (SAS) were officially part of this working group that met in Chicago for a couple of days in 1998. There are several reasons why these ‘Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research’ are still cited and considered important despite a number of more recent guidelines or consensus documents on measurements, classification and scoring, nicely reported by Pevernagie et al. (2020). The recognition of obstructive sleep apnea (OSA) began in the 1970s, using a simple assessment by thermistors or thermocouples, which essentially provided a binary response, flow or no flow. This created difficulties when dealing with hypopneas because these measurement techniques were found to be inadequate in measuring partial reduction in flow. They provide only qualitative information that is not well correlated with breath amplitude. Therefore, a relative reduction in amplitude (e.g., >50%) of this signal cannot be used to reliably indicate the presence or absence of a hypopnea (1999). On the other hand, it was also suggested that in routine clinical practice it is not necessary to differentiate apneas from hypopneas because both types of events have similar pathophysiology and consequences. There are no data to suggest different outcomes in patients with predominantly apneas as compared to hypopneas (1999). Because actual volume measurement requires continuous monitoring of total oronasal airflow with a pneumotachometer, the ‘‘big change’’ acknowledged in Chicago was nasal pressure. Nasal cannula connected to a pressure transducer provided a near-quantitative but simpler surrogate measure of airflow. It was therefore considered to be a promising method for the detection of hypopneas. It has since become standard clinical practice. Nevertheless, standardizing visual detection of flow limitation from a nasal cannula pressure signal has not yet been achieved. This would be critical in evaluating sleep-disordered breathing in situations where a low AHI fails to capture a suspected risk for health outcomes (Pamidi, Redline, & Rapoport, 2017). Interestingly enough, questions remain regarding the quantification of nocturnal hypoxia and the most appropriate desaturation criteria when scoring hypopneas (i.e., 3% or 4% reduction from baseline). The severity of oxygen desaturation is a major predictive factor for OSA morbidity, especially with regard to cardiovascular consequences. Recognizing the apparent equivalence of hypopnea definitions requiring ≥3% or ≥4% desaturation, it was recommended that the 3% criterion should be adopted. However, it was also pointed out that using a ≥3% instead of ≥4% desaturation requirement for defining hypopnea does increase the AHI substantially, with a median AHI in a general community sample being almost twice as great using a 3% as a 4% criterion (Berry et al., 2012). This reflects the difficulty of the AHI being consistent across studies and also a single metric of disease severity. Whether or not to include arousals in the definition of the AHI and OSA has also been much discussed. This may be related to the lack of well-conducted trials demonstrating an association between arousals and adverse outcomes. Nevertheless, the arousals, apart from their role in upper airway opening after apneic events, contribute significantly to OSA morbidity (Levy et al., 2015) (i.e., excessive daytime sleepiness, cognitive defects and hypertension) (Sulit, Storfer-Isser, Kirchner, & Redline, 2006). More importantly, there are conceptual limitations in the AHI. In an elegant editorial, David Rapoport recently summarized the past, present and future of the physiological assessment of OSA (Rapoport, 2018). Despite multiple definitions of hypopnea, the AHI contains a conceptual limitation: it only describes the frequency with which abnormal respiratory events occur. It captures little or no aetiologic information on why obstruction occurs in a given patient. Correlations of the AHI with excessive daytime somnolence or cardiovascular, metabolic or cognitive outcomes have also been rather modest. Finally, there has been also limited correlation of treatment success with baseline AHI (Rapoport, 2018). This editorial accompanied a study looking at simple flow–time tracings derived from sleep studies. This enabled the capture of physiologic information that completes the definition of the ‘‘phenotype’’ of a specific patient's repetitive obstructive events (Sands et al., 2018). In summary, pharyngeal collapsibility determines the ventilation at ‘eupneic’ ventilatory drive during sleep, and pharyngeal compensation determines the rise in ventilation accompanying a rising ventilatory drive. Measuring ventilation and ventilatory drive during spontaneous cyclic events enables non-invasive phenotyping in the clinic (Sands et al., 2018). The flow signal (e.g., its surrogate using nasal pressure), thus provides much more information than simply assessing the AHI. More importantly, the procedure is simple and based on a standard diagnostic polysomnogram (Rapoport, 2018). Thus, there is growing appreciation that the underlying aetiology (i.e., endotype) and clinical manifestation (i.e., phenotype) of OSA in an individual are not well described by the AHI (Edwards, Redline, Sands, & Owens, 2019). As pointed out by Pevernagie et al. (2020), a diagnostic therapeutic trial (i.e., attributing the cause of any symptoms by observing improvement under therapy), instead of the traditional syndromic approach, may be an alternative to the use of the AHI. This could actually apply to symptoms or to biomarkers if sufficiently sensitive and specific (Edwards et al., 2019; Sanchez-de-la-Torre et al., 2015). In this latter paper, for instance, blood pressure response under continuous positive airway pressure (CPAP) in OSA with resistant hypertension was predicted by a pre-CPAP cardiovascular system-related micro-ribonucleic acid (miRNA) profile (Sanchez-de-la-Torre et al., 2015). A clear limitation remains that biological signatures of intermittent hypoxia and OSA have not been established and related to outcomes. There is an urgent need to discover and validate the companion ‘‘biomarkers’’ of every OSA endotype or phenotype. Biomarkers should not only include biological parameters, but also a combination of polysomnography metrics reflecting pathophysiology (Ryan et al., 2020). Lastly, cluster analysis may identify distinct OSA phenotypes. This type of analysis underscores the high degree of heterogeneity and complexity that exists within OSA patients regarding clinical presentation, risk factors and consequences, whatever their AHI (Bailly et al., 2016). Moreover, in an analysis of a subtype of the Sleep Heart and Health Study (Mazzotti et al., 2019), the excessively sleepy subtype was associated with more than threefold increased risk of prevalent heart failure compared with each of the other subtypes. Symptom subtype was also associated with incident cardiovascular disease, coronary heart disease and heart failure, with the excessively sleepy demonstrating increased risk compared with other subtypes. These results suggest that OSA symptom subtypes represent true underlying disease characteristics with clinical relevance (Mazzotti et al., 2019). Overall, it may well be that the rise and fall of the AHI reflects less a quest for a metric than a search for meaning. In the coming years, we may hypothesize that the AHI will move from a leading to a bench player position.

Topics & Concepts