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Evaluation of OSA severity

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prevalent hypertension in the general population and in patient populations, and health-related quality of life. Moreover, the AHI is a poor predictor of the response to CPAP therapy. As a result, the AHI fails to capture the real disease state of OSA.

As a clinical test, the sensitivity, speciﬁcity and predictive values of the AHI are unknown. Based on the huge prevalence ﬁgures of an increased AHI in general populations, it can be assumed that the AHI is very sensitive but not at all speciﬁc. As a consequence, using the AHI as a primary predictor for OSA probably results in overdiagnosis. This fact obviously contributes to confounding of research results and therapeutic outcomes alike.

Other sleep study variables

The mere counting of respiratory events (and computing their index in relation to the total sleep time or total recording time) has produced disappointing results so far. New markers that gauge the systemic eects of OSA and that correlate better with relevant clinical outcomes have recently been put forward.

Intermittent hypoxia markers

The oxygen saturation dips associated with respiratory events can be easily measured and processed for quantitative analysis. Innovative analysis techniques have produced new markers of exposure to hypoxaemia. The total area under the oxygen desaturation curve has been named the “hypoxic burden”. Integration of the duration of the respiratory events, the corresponding desaturation areas and the total analysed time generates a composite marker, called the “adjusted AHI”. Preliminary evidence suggests that these new indices have a better predictive value for cardiovascular outcome than conventional AHI. However, cut-o criteria are yet to be proposed for severity classiﬁcation of OSA.

Markers of sympathetic activation

Arousal-related cardiovascular responses, involving heart rate, peripheral arterial tone and pulse waveform are suitable markers of sympathetic activity.

Markers of sleep fragmentation

Early studies have demonstrated a lack of correlation between the frequency of cortical arousals and symptom severity in OSA. However, renewed interest in sleep continuity, sleep depth, arousal threshold and arousal intensity may reveal as yet undisclosed information that is useful for severity grading of OSA.

All of these novel “high-potential” indicators require further exploration. Multicentre prospective studies will be needed to test proof-of-principle regarding clinical relevance. Ultimately, formal validation studies will have to be conducted before these new markers can be embraced for clinical use.

Assessing OSA severity based on an integrative disease model

It is clear that factors other than the frequency of respiratory events must be included when rating OSA severity. The severity of the systemic eects of these respiratory events as well as the susceptibility of the individual to the end-organ impact of these eects should be considered. The three-dimensional model and multicomponent grading system for OSA severity described below emanated from a meeting of experts at a conference held in Baveno (Italy) in 2016.

Respiratory events, systemic eects and end-organ impact

The disease state of OSA can be conceived using a three-dimensional model that integrates all relevant pathogenic factors (ﬁgure 1). The horizontal axis represents

ERS Handbook: Respiratory Sleep Medicine

Evaluation of OSA severity

Acute systemic eect

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Chronic end-organ impact

Respiratory eventsA

Figure 1. Graphic representation of a three-dimensional model of OSA disease severity. A full explanation of the ﬁgure is provided in the main text. Reproduced and modiﬁed from Randerath

et al. (2018) with permission.

the count of respiratory events (A). The vertical axis represents a systemic eect (E) induced by respiratory events (e.g. a certain degree of hypoxaemia). The area A*E represents the integration of all events with their associated systemic eects, which is the nightly exposure to adverse eects of OSA (e.g. the hypoxic burden). Repetitive exposure causes a strain on the end-organ systems. The sagittal axis represents an end-organ impact (O) of OSA, e.g. cognitive impairment, arterial hypertension, cardiovascular damage, insulin resistance, etc. The volume A*E*O represents the relationship between the three dimensions. Based on postulated inter-individual dierences in susceptibility to exposure, the disease spectrum may vary from low exposure–high impact to high exposure–low impact. The dashed aspect of the boundaries indicates that the end-organ impact of OSA is as yet dicult to assess. This is due to uncertainty regarding susceptibility of the individual on the one hand and to possible confounding eects of other disease processes on the other.

A multicomponent grading system for OSA severity

A novel multicomponent system for grading OSA severity combines the presence of symptoms on the one hand and cardiovascular or metabolic comorbidities on the other. These comorbidities are considered manifestations of end-organ impact. The model is suitable in subjects with an AHI above threshold (e.g. ≥15 events·h−1) in whom a causal relationship between the increased AHI and the symptoms and comorbidities is likely. Both symptoms and comorbidities are dichotomised into the categories “mild” and “severe”. The combination of these features yields four categories: A, B, C and D (ﬁgure 2). The model has been tested in a cohort of OSA patients and has been shown to allow better stratiﬁcation of the OSA population in terms of responsiveness to therapy.

35ERS Handbook: Respiratory Sleep Medicine

Evaluation of OSA severity

End-organ impact/

Symptoms

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Mild symptoms

Major end-organ

impact

Mild symptoms

Minor end-organ

impact

ESS <9

Dozing episodes–

No hypersomnia

Normal vigilance test

Insomnia–

Pathological vigilance test

Severe symptoms

Major end-organ

impact

Severe symptoms

Minor end-organ

impact

ESS ≥9

Dozing episodes+

Hypersomnia

Insomnia+

Recurrent/ poorly controlled

comorbidities

Not detectable/ well controlled

Figure 2. A proposed multicomponent grading system for OSA severity: the Baveno classiﬁcation. A full explanation of the ﬁgure is provided in the main text. Reproduced and modiﬁed from Randerath et al. (2018) with permission.

Future trends

Proper validation of new indicators that identify and quantify the disease state of OSA will be paramount to improving both clinical outcome and the results from clinical research. Part of this exercise is to question the assumptions that underpin the current OSA model. The use of the AHI as a predictor of disease is arguably an error in formal logic, whereby the precedent (having disease manifestations) and consequences (testing positive for a marker) have been inverted. Patients with symptomatic OSA usually have an increased AHI, but the opposite is not necessarily true. New markers that characterise the disease state of OSA should have sucient speciﬁcity. This will translate into better “targeted” selection of treatment options and into improved prediction of response to treatment.

Another limitation of the current OSA model is the assumption that the severity of the disease is uniquely deﬁned by the degree of exposure to adverse eects. One must also consider individual dierences in the susceptibility to the systemic eects of OSA. The quest for new markers should not only envisage the identiﬁcation of indicators of exposure, but also markers of vulnerability to OSA, which may prove a major component of disease severity.

Last but not least, OSA and OSAS are complex and heterogeneous conditions. Several pathophysiological pathways may play a role and clinical manifestations are quite diverse. It is likely that no single marker will be sucient to capture all aspects of OSA. One-size-ﬁts-all solutions for diagnosis and treatment are now outdated; personalised medicine has become the new standard. In this context, treatable traits are important attributes. Finding severity markers for the most relevant traits that deﬁne an individual’s OSA phenotype is a major challenge for future research.

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Evaluation of OSA severity

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Further reading

• American Academy of Sleep Medicine (1999). Sleep-related breathing disorders in adults:

recommendations for syndrome deﬁnition and measurement techniques in clinical research. The Report of an American Academy of Sleep Medicine Task Force. Sleep; 22: 667–689.

• American Academy of Sleep Medicine (2014). Obstructive sleep apnea, adult. In: The

International Classiﬁcation of Sleep Disorders - Diagnostic and Coding Manual. 3rd Edn. Westchester, American Academy of Sleep Medicine; pp. 53–62.

• Heinzer R, et al. (2015). Prevalence of sleep-disordered breathing in the general population:

the HypnoLaus study. Lancet Respir Med; 3: 310–318.

• Lim DC, et al. (2020). Reinventing polysomnography in the age of precision medicine. Sleep

Med Rev; 52: 101313.

• Malhotra A, et al. (2021). Metrics of sleep apnea severity: beyond the apnea-hypopnea index.

Sleep; 44: zsab030.

• McNicholas WT, et al. (2022). Obstructive sleep apnea: transition from pathophysiology to an

integrative disease model. J Sleep Res; 31: e13616.

• Pevernagie D (2021). Future treatment of sleep disorders: syndromic approach versus

management of treatable traits? Sleep Med Clin; 16: 465–473.

• Pevernagie D, et al. (2021). The role of patient-reported outcomes in sleep measurements.

Sleep Med Clin; 16: 595–606.

• Pevernagie DA, et al. (2020). On the rise and fall of the apnea-hypopnea index: a historical

review and critical appraisal. J Sleep Res; 29: e13066.

• Randerath W, et al. (2018). Challenges and perspectives in obstructive sleep apnoea: report by

an ad hoc working group of the Sleep Disordered Breathing Group of the European Respiratory Society and the European Sleep Research Society. Eur Respir J; 52: 1702616.

• Randerath WJ, et al. (2021). Evaluation of a multicomponent grading system for obstructive

sleep apnoea: the Baveno classiﬁcation. ERJ Open Res; 7: 00928-2020.

• Riha RL (2021). Deﬁning obstructive sleep apnoea syndrome: a failure of semantic rules.

Breathe; 17: 210082

37ERS Handbook: Respiratory Sleep Medicine

Epidemiology of obstructive

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sleep apnoea, central sleep apnoea and hypoventilation syndromes

Johan Verbraecken

OSA

SDB is very common in adults. Evidence has shown that an AHI of ≥5 events·h−1 is present in 24% of middle-aged men and in 9% of middle-aged women (Young et al.,

1993); elsewhere, an elevated AHI in >30% of those aged ≥65 years was noted (Bixler et al., 1998). Recent studies, such as the HypnoLaus study (Heinzer et al., 2015), have shown even higher prevalences of OSA, which could be attributed to increasing obesity in the general population (Peppard et al., 2013). Estimated global prevalence numbers were recently published, which showed that almost 1 billion 30–69 year olds present with mild-to-moderately severe OSA, whereas 425 million adults have severe OSA (Benjaﬁeld et al., 2019). When symptoms are considered in addition to the AHI, the prevalence of OSA is close to just 2% in women and 4% in men. If symptoms are not taken into account, the AHI can be related to other conditions. For example, the risk of systemic hypertension rises proportionally with the AHI, and cardiovascular risk increases with an AHI of ≥15–30 events·h−1. For this reason, it is important to make a distinction between the presence or absence of complaints for deﬁnition purposes, to the degree that it is relevant.

The most important risk factor for developing OSA is obesity, as >70% of OSA patients are obese (Deegan et al., 1996). When BMI exceeds 25 kg·m−2, sensitivity is 93% and speciﬁcity is 74% for developing OSA (Grunstein et al., 1993). Moreover, a 10% weight gain causes a six-fold increase in the risk of developing an AHI of ≥15 events·h−1 (Peppard et al., 2000). A 10% weight gain also induces an approximate AHI increase

Key points

• Clinically relevant OSA has an estimated prevalence of at least 4% in males and 2% in females.

• Obesity has a very strong relationship with AHI.

• CSR has a male predominance.

• The prevalence of CSA due to a medical disorder without CSR is limited.

• OHS occurs in a minority of patients who are severely overweight.

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Epidemiology of OSA, CSA and hypoventilation syndromes

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of 32%; however, in contrast, a 10% weight loss can cause the AHI to drop by 26%. More recently, the association between central obesity and OSA was assessed and a strong correlation between visceral fat and OSA was noted (Vgontzas et al., 2003).

Interestingly, more men than women suer from OSA (AHI ≥5 events·h−1), at a ratio of 2:1. Recent studies have shown a less pronounced dierence in the male:female ratio, at approximately 1.5:1 (Heinzer et al., 2015, and Donovan et al., 2016). The higher prevalence among men could be explained by androgens that stimulate upper airway collapse, whereas progesterone would have a more protective eect. This also explains the increase in prevalence amongst women aer menopausal age (Jordan et al., 2003).

OSA prevalence also increases with age. An increase is observed until the seventh decade of life, followed by a plateau (Young et al., 2002). Daytime symptoms seem to be less frequent with more advanced age.

CSA

Most information regarding the epidemiology of CSA comes from clinical populations with a number of predisposing conditions, such as CHF, stroke and opioid use. Such categories are subject to referral pattern bias. In one large, non-clinic-based cohort, CSA has been found to be 53 times less common than OSA (0.9% versus 47.6%) (Donovan et al., 2016), with a prevalence of 1.8% in men and 0.2% in women. Approximately half of the CSA cases were associated with CSR (0.4% overall). In older studies, the prevalence of CSA was noted to be 0.4% overall, but a higher cut-o value of 10 had been employed (Bixler et al., 1998).

The prevalence of CSA with CSR

CSR is generally seen in those >60 years of age. Its prevalence in a CHF setting has been reported to be 25–50%, with a male predominance. Aer stroke, ≤70% of patients presents with CSR. These patients oen present with OSA but CSA is also common, especially in the acute phase following stroke (Johnson et al., 2010). The risk factors for CSR (in CHF) are male sex, age >60 years, the presence of atrial ﬁbrillation (AF) and daytime hypocapnia (Javaheri et al., 2013). In general, a more pronounced lung congestion predicts a lower P oen in the supine position, and is related to alterations in the FRC.

. Some studies have reported that CSR occurs more

aCO

CSA due to a medical disorder without CSR

Due to the heterogeneous aetiology of this type of CSA, prevalence data vary according to the underlying aetiology (Randerath et al., 2017).

Acromegaly

The prevalence of CSA is usually low in patients with acromegaly and is related to disease activity. Some studies have shown a CSA prevalence of nearly 32%, but it is assumed that up to 10% of patients with acromegaly suer from relevant CSA. The central apnoea index (CAI) appears to be linked to the production of growth hormone (GH), whereas elevated ventilatory responses are associated with GH and insulin-like growth factor-1 levels. Medical and surgical treatment of acromegaly can reduce OSA; there is a lack of control studies examining the eects of treatment on CSA.

Type 2 diabetes mellitus

OSA is the dominant type of SDB in patients with type 2 diabetes mellitus (DM). However, the Sleep Heart Health Study reported a small but nonsigniﬁcant increase in the number of central apnoeas in patients with DM. Moreover, speciﬁc analysis

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Epidemiology of OSA, CSA and hypoventilation syndromes

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of periodic breathing (PB) showed a signiﬁcant rise in patients with obvious DM. Pharyngeal neuropathy can be a contributing factor but no obvious association has been established between CSA and autonomous dysfunction.

End-stage renal disease

OSA frequently occurs in end-stage renal disease (ESRD). Tada et al. (2007) reported 30 (38%) patients with sleep apnoea in a group of 78 patients being treated with haemodialysis. The average CAI was 4 events·h−1, and CSA made up 8% of all respiratory events. Eight of the 30 patients showed a CAI of ≥5 events·h−1 and were classiﬁed as having CSA. Interestingly, the prevalence of CSA depends on the dialysis procedure and the buers used: bicarbonate was associated with signiﬁcantly fewer CSAs compared with acetate buer, despite similar blood gases.

Fluid overload and shi over the course of the night is considered to be the underlying mechanism for a high prevalence of OSA in ESRD. A rise in ventilatory sensitivity and a destabilised respiratory control system also contribute to an increased prevalence of SDB in ESRD, as do comorbid AF and cardiac dysfunction, which points to synergistic eects on the development of sleep apnoeas.

Pulmonary hypertension

Pulmonary hypertension (PH) is a pathophysiological and haemodynamic condition that is related to multiple clinical aictions. CSR and CSA are common in CHF, although there are very limited data available on capillary PH. 0–45% of patients with PH have central respiratory events versus a prevalence of 0–56% for OSA. OSA is predominant in chronic thromboembolic and COPD-associated PH, whereas CSA is seen mainly in idiopathic or chronic thromboembolic PH.

Interstitial lung diseases

In interstitial lung diseases (ILDs), dyspnoea, nocturnal coughing, medication side-eects, periodic limb movements, hypoxaemia, OSA, depression and fatigue encumber sleep. The role of CSA has not been thoroughly investigated.

Neurodegenerative diseases

Several studies have reported on the prevalence of OSA in patients with dierent types of dementia. There are no convincing data regarding a higher prevalence of CSA with Alzheimer disease. The prevalence of sleep apnoea in Parkinson disease varies 21–67%, and most studies included low numbers of patients (15–100). OSA is the dominant type of sleep apnoea, while only a limited number of cases of CSA have been reported.

Central apnoea at high altitude

In healthy subjects, hypobaric hypoxaemia at an altitude of >2000 m can lead to CSA and PB. Generally, the percentage of individuals exhibiting PB during sleep increases progressively at higher altitudes. Usually, 25% exhibit PB at 2500 m, and almost 100% demonstrate PB at 4000 m. As men have higher chemoresponsiveness than women, high-altitude PB is more common in men than in women.

CSA due to medication or substance abuse

CSA induced by chronic opioid use is described in about 30% of subjects who use methadone to treat heroin addiction (Wang et al., 2005). So far, there are no known sex, race or ethnicity dierences. Use of potent long-acting opioids is the most important causal factor, but the eect is dose-dependent. This also indicates that if the drug is successfully tapered down, CSA may resolve or improve.

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Epidemiology of OSA, CSA and hypoventilation syndromes

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Primary CSA

The prevalence of primary CSA is much lower than that of OSA. Based on the Sleep Heart Health Study, 0.5% of the general population has primary CSA, 1% in men and

0.1% in women (Donovan et al., 2016). In a clinical cohort, it was estimated that ∼4% of patients who were referred to a sleep

clinic with suspicion of SDB predominantly displayed CSA (De Backer et al., 1995). CSA aects healthy individuals physiologically, especially old people and young children. Generally speaking, CSA occurs at an advanced age (Bixler et al., 1998). A high hypercapnic ventilatory response (HCVR) seems to be a major predisposing factor in the development of primary CSA.

Primary CSA of infancy and prematurity

For more information on these subcategories, the reader is referred to chapter 17, which discusses paediatric respiratory sleep medicine.

Treatment-emergent CSA

Treatment-emergent CSA (TECSA) aects 2–15% of patients with classic OSA. With ongoing CPAP therapy, a spontaneous decrease in the number of central apnoeas or even their disappearance is expected over time once PAP is well established (Cassel et al., 2011).

Hypoventilation syndromes

OHS

This syndrome occurs in a minority of patients who are severely overweight. Clinically relevant OSA aects 80–90% of patients with OHS. Conversely, OHS is described in 9–11% of patients with OSA. Prevalence estimates for OHS in the general population are unknown but may be estimated using obesity and OSA prevalence data (∼0.4%, or approximately one in 260 people in the US adult population, though this maybe lower in countries with a reduced prevalence of obesity) (Masa et al., 2019). The prevalence of OHS is higher in men than women, but the dierence is not as pronounced as in OSA. In one clinical cohort it was reported that the prevalence of OHS is similar in men and women (Palm et al., 2016). Higher levels of obesity are usually associated with more severe sleep-related hypoventilation but individual variations can also play a role (Verbraecken et al., 2013). Use of central nervous system (CNS) depressants may aggravate respiratory impairment further.

Congenital central hypoventilation syndrome

For further information on this topic, the interested reader is referred to chapter 17 on paediatric respiratory sleep medicine.

Late-onset central hypoventilation with hypothalamic dysfunction

For further information on this topic, the interested reader is referred to chapter 17 on paediatric respiratory sleep medicine.

Idiopathic central alveolar hypoventilation

Patients with this diagnosis may have an underlying functional defect aecting respiratory drive and mechanics, which remains undiagnosed. No data are available on its prevalence. The use of CNS depressants (anxiolytics, hypnotics, neuroleptics and alcohol) may further worsen hypercapnia.

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Epidemiology of OSA, CSA and hypoventilation syndromes

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Sleep-related hypoventilation due to medication or substance abuse

The prevalence of sleep-related hypoventilation due to the use of respiratory depressants is not known. It is obvious that baseline hypoventilation will worsen following initiation of the medication. The use of medication or a substance that impairs respiratory drive or mechanics is the primary risk factor responsible for hypoventilation, but signiﬁcant inter-individual dierences in sensitivity and tolerance to respiratory depressants do exist.

Sleep-related hypoventilation due to a medical disorder

The prevalence of sleep-related hypoventilation due to a medical disorder is a function of the underlying condition’s prevalence, degree of severity and clinical characteristics. No thresholds are set for pulmonary parenchymal or vascular disease severity to predict the risk of sleep-related hypoventilation in individual subjects. A reduced HCVR and the use of CNS depressants may further worsen respiratory impairment.