The duration (length of time) and depth (severity) of oxygen desaturations during respiratory events play a key role in determining the physiological impact of obstructive sleep apnea (OSA). These factors are closely linked to adverse health outcomes, such as cardiovascular complications and disease progression.

• Common measures like the apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) fail to capture the nuances of duration and depth. They provide only a frequency count of events, ignoring the full impact of each event’s characteristics.

It is suggested that duration and depth of desaturations be included into OSA severity metrics as it will offer following advantages-

Better Risk Stratification:

• Studies suggest that incorporating the duration and depth of desaturations into metrics can enhance the risk stratification of OSA patients.
• Longer and deeper desaturations are associated with worse outcomes, including greater cardiovascular responses and disease progression.

Progression and Precision Medicine:

• Detailed analysis of desaturations can predict the progression of mild OSA to more severe forms.
• This information supports precision medicine by enabling tailored treatment strategies based on the specific characteristics of a patient’s desaturation events.

Readily Available Data:

• Oxygen desaturation data are already recorded in standard sleep studies (e.g., polysomnography), making it feasible to integrate these dimensions into clinical practice without requiring additional equipment.

In summary, the duration and depth of oxygen desaturations provide a more comprehensive understanding of OSA’s physiological impacts and its associated risks, improving diagnosis, treatment, and management strategies. OSA specific Hypoxia burden is one such metric which captures the respiratory event frequency along with duration and depth of desaturations.

How do we define hypoxia Burden (HB)?

• Hypoxic burden (HB) measures the total area under the oxygen saturation curve for all respiratory events (apneas and hypopneas) during sleep.
• It captures the frequency, depth, and duration of oxygen desaturation events.

Calculation Process:

• For each respiratory event, the oxygen desaturation curve is synchronized and aligned to the event termination (time-zero).
• The area under the desaturation curve is calculated for each event based on the interval between pre-event and post-event maximum oxygen saturation values.
• The total HB is the sum of these individual areas, divided by the total sleep time, expressed as a percentage per minute per hour (%min/h).

Click on this link to understand the detailed methodology for calculating HB- https://apnimed.com/wp-content/uploads/2023/05/apnimed-white-paper-hypoxic-burden-sleep-apnea-050523.pdf.

HB accounts for the cumulative impact of oxygen desaturations, going beyond simple frequency-based metrics like the apnea-hypopnea index (AHI) or oxygen desaturation index (ODI). It provides a more detailed representation of OSA’s severity and its potential health consequences.

HB can be automatically calculated using specific commercial software (e.g., Sleepware G3 from Respironics and Cidelec). I am trying to get the details from Philips Respironics regarding the Sleepware G3 reporting on HB. So far I have not been able to generate HB reports with the current version of Sleepware G3 v4.2.1.0. HB can also be calculated by loading the PSG data on to the following website https://www.thesiestagroup.com/hypoxic-burden/. This website was showing that HB calculation is currently under maintenance while I was writing this blog. Another group of researchers have developed a freely available code that we can use on our computers to calculate HB. One need to transfer .edf files for analysis into this software. One can access this software at the following link- https://zenodo.org/records/6198838

Hypoxia Burden cut off values:

The cut-off values for hypoxic burden (HB) depend on its application, particularly for assessing cardiovascular risk and mortality in patients with obstructive sleep apnea (OSA).

Increased Risk Threshold:

• An HB value of >60%min/h (equivalent to approximately 15 minutes of 4% oxygen desaturations per hour) is associated with an increased risk of cardiovascular morbidity and mortality.

High-Risk Quintiles:

• Studies often categorize HB into quintiles, with higher quintiles (e.g., the 4th and 5th quintiles) showing significantly higher risks for adverse outcomes like cardiovascular events, heart failure, and all-cause mortality.
• Q1= <20%min/hr
• Q2= 20-34%min/hr
• Q3= 34-53%min/hr
• Q4= 53-88%min/hr
• Q5= >88%min/hr

Dose-Response Relationship:

• The risk of adverse health outcomes tends to increase progressively with higher HB values, reinforcing its utility as a continuous rather than binary measure. These thresholds help stratify patients and guide clinical decisions regarding OSA treatment and monitoring. For precise application, further standardization and clinical validation may be necessary.

Increased Hypoxic Burden predicts increased Cardiovascular Mortality

HB is strongly associated with cardiovascular mortality, outperforming traditional metrics like the apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) in predictive power. Unlike AHI, which shows inconsistent associations with mortality, HB consistently correlates with increased cardiovascular risk.

Study Evidence:

• Two large community-based cohort studies—Osteoporotic Fractures in Men Study (MrOS) and Sleep Heart Health Study (SHHS)—examined the link between HB and cardiovascular mortality: (https://doi.org/10.1093/eurheartj/ehy624)
  • MrOS: Men in the highest HB quintiles had a hazard ratio (HR) of 1.81–2.73 for cardiovascular mortality compared to the lowest quintile.
  • SHHS: Similar trends were observed, with those in the highest HB quintile having an HR of 1.95 for cardiovascular mortality.

Independent Risk Factor:

• HB remained a significant predictor of cardiovascular mortality even after adjusting for other factors like AHI, ODI, and comorbid conditions.

Mechanistic Insights:

• HB reflects the cumulative physiological stress of oxygen desaturation events, including their depth and duration, which are critical contributors to cardiovascular strain.
• This explains its stronger association with cardiovascular outcomes compared to frequency-based metrics.

Increased Hypoxic Burden increases Incidence of Major Cardiovascular Events

HB is significantly associated with the incidence of major adverse cardiovascular events (MACEs), such as myocardial infarction, stroke, and heart failure. HB demonstrates stronger predictive capacity for MACEs compared to traditional metrics like the apnea-hypopnea index (AHI) and oxygen desaturation index (ODI).

Study Evidence: (https://doi.org/10.1164/rccm.202105-1274OC)

• A large clinical study using the Pays de la Loire Sleep Cohort followed 5,358 OSA patients for a median of 6.5 years:
  • During this period, 592 MACEs were observed.
  • HB was independently associated with MACEs, with a hazard ratio (HR) of 1.21 per unit increase in HB, indicating a dose-response relationship.

Comparative Performance:

• HB outperformed traditional OSA metrics, including AHI and ODI, in predicting cardiovascular outcomes.
• T90 (percentage of sleep time with oxygen saturation below 90%) also showed predictive value but was weaker than HB.

Additional Insights:

• The relationship between HB and cardiovascular events was more pronounced in specific subgroups, such as older, non-obese patients.

High Hypoxic Burden is associated with high Blood Pressure

Association with Elevated Blood Pressure:

• Hypoxic burden (HB) is linked to higher blood pressure, particularly diastolic blood pressure (DBP).
• The cumulative oxygen desaturation during sleep (captured by HB) is a key driver of sleep-disordered breathing-induced hypertension.

Evidence from Studies:

• Data from the Multi-Ethnic Study of Atherosclerosis (MESA) showed: (https://doi.org/10.1136/thoraxjnl-2019-213533)
  • For every 1 standard deviation (SD) increase in log-transformed HB, there was a 0.9% increase in DBP.
  • Among participants not using antihypertensive medication, HB was associated with a 1.1% increase in systolic blood pressure (SBP) and a 1.9% increase in DBP.

REM vs. NREM Hypoxia:

• HB during REM sleep was associated with higher SBP in mild OSA patients.
• Both REM and NREM-related HB were linked to increased DBP, especially in individuals not on antihypertensive drugs.

Pathophysiological Mechanisms:

• Hypoxic burden contributes to sympathetic nervous system activation and vascular dysfunction, which elevate blood pressure, particularly during sleep.

High Hypoxic Burden increases risk of Stroke

Strong Association with Stroke Incidence: https://doi.org/10.1183/13993003.04022-2020

• Hypoxic burden (HB) is significantly linked to the risk of new cerebrovascular events, particularly ischemic strokes.
• HB provides a more robust prediction of stroke risk compared to traditional OSA metrics like apnea-hypopnea index (AHI) and oxygen desaturation index (ODI).

Key Study Evidence: https://doi.org/10.1183/13993003.04022-2020

• A study using the Pays de la Loire Sleep Cohort followed 3,597 patients for 5.9 years and observed 83 incident cerebrovascular events (70 ischemic strokes).
• Log-transformed HB showed a hazard ratio (HR) of 1.28 (95% CI: 1.05–1.57), outperforming AHI (HR: 1.20), ODI (HR: 1.13), and T90 (HR: 1.06).

Dose-Response Relationship:

• A higher HB is associated with an incremental increase in stroke risk, demonstrating a clear dose-response pattern.
• The association remained significant even when hemorrhagic strokes were excluded, further underscoring HB’s predictive value.

Specific Subgroup Insights:

• HB-stroke associations were stronger in non-obese individuals and those aged over 60 years.
• Stroke risk was not significantly influenced by continuous positive airway pressure (CPAP) adherence in this study.

High Hypoxic Burden increases risk of Heart Failure (HF)

Association with Incident Heart Failure:

• Hypoxic burden (HB) is significantly associated with an increased risk of developing heart failure (HF).
• It outperforms traditional metrics like the apnea-hypopnea index (AHI) in predicting HF incidence.

Key Study Evidence:

• Data from two large cohorts, the Sleep Heart Health Study (SHHS) and the Osteoporotic Fractures in Men Study (MrOS), showed:
  • In SHHS: Men with higher HB had a hazard ratio (HR) of 1.18 (95% CI: 1.02–1.37) for incident HF.
  • In MrOS: Men had an HR of 1.22 (95% CI: 1.02–1.45) for incident HF, independent of AHI.

Gender-Specific Effects:

• HB’s association with HF was more pronounced in men. In women, the relationship was weaker, potentially due to lower numbers of severe OSA cases in the female population.

Independent Predictive Value:

• HB remained a significant predictor even after adjusting for other factors like baseline coronary heart disease, central sleep apnea, and traditional OSA severity metrics.

High Hypoxic Burden increases risk of Chronic Kidney Disease (CKD)

Association with CKD Prevalence: https://doi.org/10.1136/thoraxjnl-2020-214713

• Hypoxic burden (HB) is significantly associated with an increased prevalence of moderate-to-severe CKD.
• HB provides a stronger predictive link to CKD than traditional OSA metrics like the apnea-hypopnea index (AHI).

Key Study Evidence: https://doi.org/10.1136/thoraxjnl-2020-214713

• A study using data from the Multi-Ethnic Study of Atherosclerosis (MESA), which included 1,895 participants:
  • Higher HB was associated with a 20% increased prevalence of moderate-to-severe CKD.
  • Participants in the highest HB quintile had a 36% higher prevalence of CKD compared to those in the lowest quintile.

Dose-Response Relationship:

• The relationship between HB and CKD follows a dose-response pattern, with higher HB values linked to greater CKD prevalence.
• This association was consistent across subgroups defined by race and ethnicity.

Mechanistic Insights:

• The cumulative hypoxemia captured by HB likely contributes to kidney damage through mechanisms such as:
  • Chronic inflammation.
  • Increased oxidative stress.
  • Sympathetic nervous system activation.

Hypoxic Burden (HB) and Other Current and New Sleep Metrics:

Correlation with Traditional Metrics:

• Apnea-Hypopnea Index (AHI):
  • HB is moderately correlated with AHI (correlation coefficient ~0.8 for general populations).
  • In severe OSA cases, the correlation weakens (e.g., 0.51 for AHI > 30 events/hour), indicating that HB captures additional information beyond frequency-based metrics.
• Oxygen Desaturation Index (ODI):
  • HB shares some overlap with ODI but better accounts for the depth and duration of desaturations.
• T90 (percentage of sleep time with SpO2 < 90%):
  • HB provides complementary information, capturing events where desaturation may not fall below 90%, which T90 misses.

Additional Information Beyond Traditional Metrics:

• Unlike frequency-based metrics (e.g., AHI, ODI), HB incorporates the cumulative burden of hypoxemia, including frequency, depth, and duration of desaturation events.
• This makes HB more predictive of health outcomes like cardiovascular events and mortality.

Synergy with Novel Metrics:

• HB complements emerging metrics like:
  • Lung-to-Finger Circulation Time (LFCT): Measures the delay between respiratory events and nadir oxygen saturation, linked to cardiovascular risk. https://doi.org/10.1016/j.chest.2020.10.025
  • Pulse Rate Response (∆HR): Assesses heart rate changes following respiratory events, with synergistic predictive value when combined with HB for cardiovascular outcomes. https://doi.org/10.1164/rccm.202111-2608oc

Independent Predictive Value:

• HB often demonstrates independent predictive capacity for adverse outcomes (e.g., cardiovascular events) when combined with other sleep metrics, highlighting its distinct role.

Potential for Composite Indices:

• Combining HB with other metrics (e.g., ∆HR or LFCT) could improve risk stratification for conditions like cardiovascular disease and mortality. https://doi.org/10.1164/rccm.202010-3900OC

Possibilities for HB in future

Expand Beyond Oxygen Desaturation:

• Incorporate non-oximetric aspects of OSA, such as:
  • Cortical and autonomic arousals following respiratory events.
  • Metrics like heart rate response (∆HR) and arousal intensity to better capture the overall physiological burden of OSA.

Refine HB Definition:

• Systematically evaluate the impact of deep vs. shallow desaturations and short vs. long events on outcomes.
• Modify HB calculations to incorporate the relative contributions of these desaturation characteristics.

Integration with Advanced Technologies:

• Leverage advancements in:
  • Machine learning and deep learning to analyze polysomnography data.
  • Automated scoring and prediction models for more accurate and scalable HB calculations.

Broader Clinical Applications:

• Validate HB as a tool for predicting risks beyond cardiovascular outcomes, including:
  • Metabolic disorders.
  • Neurocognitive impairments.
  • Cancer progression.

Tailored Interventions (Precision Medicine):

• Use HB to guide personalized treatment plans:
  • Stratify patients based on HB levels to prioritize intensive interventions for high-risk individuals.
  • Monitor changes in HB to assess the effectiveness of therapies like CPAP.

Large-Scale Studies and Trials:

• Conduct prospective, randomized controlled trials to:
  • Evaluate HB’s utility in risk prediction.
  • Determine the clinical benefits of incorporating HB into routine care.

Standardization and Thresholds:

• Develop standardized protocols for calculating HB to ensure consistency across sleep laboratories and devices.
• Establish widely accepted thresholds for high-risk HB values to guide clinical decision-making.

Broader Accessibility:

• Facilitate the adoption of HB in both in-lab and at-home sleep studies by ensuring compatibility with commonly used devices and software.

Summary:

Future research and innovation should focus on refining the calculation, validation, and application of HB while exploring its potential in personalized care and broader clinical contexts. These advancements could significantly improve risk stratification, treatment outcomes, and overall patient care in OSA management.