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Central sleep apnea (CSA) is a sleep disorder characterised by a lack of respiratory effort during sleep due to a failure of the brain to send appropriate signals to the muscles that control breathing. CSA can lead to disrupted sleep and low oxygen levels in the blood, and is commonly associated with other medical conditions, such as heart failure (1). There has been a noticeable increasing interest in CSA over the last few years by key opinion leaders and it's overall impact on healthcare outcomes in people with co-existent obstructive sleep apnoea (OSA).
The reported prevalence of CSA varies widely depending on the population being studied, the various diagnostic methods employed, and the specific definition used. There is a clear need for standardised diagnostic criteria and more robust epidemiological data.
Latest Overall Prevalence Estimates:
CSA is generally reported to range from 5-10% of individuals with sleep-related breathing disorders (SRBDs) in the general population (2).
Idiopathic CSA represents 4-7% within sleep centre populations however this may underestimate the true prevalence due to limitations in current diagnostic approaches and verification biases (1).
Prevalence of CSA in Specific Medical Conditions:
The following represent prevalence estimates, with notable variations reported across studies:
CSA in Chronic Heart Failure (CHF):
Significant variation exists: Prevalence ranges from 7% to 69% depending on the left ventricular ejection fraction (LVEF) and the clinical presentation of the CHF. Studies show that it is highly prevalent in patients with symptomatic left ventricular systolic dysfunction (55%) and moderate to severe HF with reduced EF (34%-69%). Studies with a more stable CHF population report much lower figures (7-15%). Decompensation can dramatically increase prevalence and severity (2-3).
CSA and Stroke: Estimates range from 8-12% in studies analysed.
CSA and Pulmonary Hypertension: 39% (note: This figure comes from a smaller, less generalizable study, hence the lack of a range).
CSA and Chronic Kidney Disease: 10% (also based on a smaller study).
Opioid-Induced CSA: 24% (average prevalence from studies on chronic opioid use).
Methadone-Induced CSA: 30% (average prevalence from studies on methadone use).
Treatment-Emergent CSA (TE-CSA): 10-25% (during PAP titration studies) (2-3).
Key Limitations and Considerations:
Diagnostic Variability: The considerable variation in reported prevalence of CSA highlights inconsistencies in diagnostic methods and criteria across studies. Polysomnography is considered the gold standard, but its use varies, and other methods such as cardiorespiratory polygraphy show less consistent results. Precise differentiation between central and obstructive hypopneas remains challenging (1-3).
Study Population Differences: Prevalence estimates strongly depend on the characteristics of the study population. Studies on hospitalised patients or those with specific comorbidities are less generalisable to the wider population.
Night-to-Night Variability: Studies confirm significant night-to-night variability in CSA severity and even type of apnoea (CSA vs. OSA) (2-3).
Data reported in recent published studies offer a range of prevalence figures, reflecting the heterogeneous nature of CSA and the challenges in precisely defining and measuring its occurrence across diverse patient populations. Clinicians need to consider multiple factors including symptoms, medical history, and the results of polysomnography in making a diagnosis.
What about Central Sleep Apnea (CSA) in the Elderly?
A recent publication by Joskin and Bruyneel 2024 report on the prevalence of CSA in the elderly which does shine a spotlight on the importance of proper diagnosis, reporting and documenting CSA in this population (4).
High Prevalence in Specific Conditions: CSA is particularly prevalent among elderly patients with congestive heart failure (CHF), with prevalence rates varying widely (7-69%) depending on the severity and type of CHF. It's also found in other conditions like stroke (8-12%) (2-4).
Diagnostic Challenges: Distinguishing CSA from obstructive sleep apnea (OSA) can be difficult, even with polysomnography. Accurate diagnosis requires careful assessment of respiratory effort and airflow (1).
Treatment: Adaptive servo-ventilation (ASV) is generally preferred for CSA over CPAP. However, the efficacy and safety of ASV in elderly patients, particularly those with severely reduced ejection fraction (LVEF <45%), remains a subject of ongoing research and debate. Some studies found ASV to be harmful in specific populations (2).
Medications: While certain medications can induce CSA, others, such as acetazolamide, have shown some promise in managing CSA. However, more research is needed to confirm the long-term effects and safety (2-4).
There is a pressing need for more research to better define the different phenotypes of CSA in elderly patients, to develop improved diagnostic tools, and to assess the long-term efficacy and safety of different treatment strategies across various subgroups. Standardised outcome measures and better methods for assessing compliance are also crucial.
The landscape of sleep medicine is intricate, particularly when we unpack the nuances of Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) in older populations. There exists a significant variation in CSA prevalence, heavily influenced by demographic factors, diagnostic methodologies, and definitions employed across studies. It is apparent that the scope of epidemiological research needs expansion and standardisation to yield clearer prevalence metrics.
Another important consideration is the CPAP device used to treat patients needs to be able to accurately measure and report all central apnoeas and hypopnoeas and in a recent study by Richards et al 2025 – only 1 CPAP device of 5 was able to accurately report all obstructive and central apnoea and hypopnea events consistently under certain mask leak conditions. The CPAP algorithm that performed the best in this study was the Sefam S Box highlighting the need for standardisation of CPAP algorithms 5.
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