Is AHI the Best OSA Metric?

By Snorri Helgason

is ahi the best metric


Sleep apnea has been identified as a serious health issue for most countries. It exerts a substantial influence on individuals’ physical and mental health, impairing daily functioning and overall quality of life if left untreated. Moreover, sleep apnea imposes a substantial burden on society, leading to extensive healthcare costs, absenteeism, presenteeism, reduced productivity, heightened personnel risks, and an increased likelihood of motor vehicle accidents and injuries.

Since sleep apnea was defined in the 1970s as a disease, the definition of the disease and how to measure its severity has been categorized through the application of the apnea index, then later the apnea-hypopnea index (AHI).1 Over the years, sleep medicine has established its diagnostic approach for sleep apnea predominantly using the AHI.1,2 This metric holds significant influence as regulatory bodies, payors, and providers rely on it to make crucial patient diagnostic decisions. The AHI plays a pivotal role in categorizing patients according to the severity of their condition and determining whether they receive treatment for obstructive sleep apnea (OSA).

However, throughout the years, the question has been raised by many in the field: Is the AHI truly the optimal metric for these purposes?

Understanding the AHI

Per the AASM manual, obstructive apneas involve complete upper airway collapse with a 90% or more drop in airflow for at least 10 seconds, accompanied by continued respiratory effort. Central apneas lack respiratory effort throughout the event, while mixed apneas start with no respiratory effort but resume despite absent airflow. The American Academy of Sleep Medicine (AASM) recommends that hypopneas be identified using a definition that is based on a ≥ 30% decrease in airflow associated with a ≥ 3% reduction in the oxygen saturation or an arousal (H3A) for diagnosis of obstructive sleep apnea (OSA) in adults.3

The severity of sleep apnea is defined by the AHI, a ratio of the sum of all respiratory events divided by the total hours of sleep. Currently, AHI is the sole indicator of OSA severity recognized by scientific societies. By this convention, an AHI of 5 to 15 per hour defines mild sleep apnea, an AHI of 15 to 30 per hour is moderate sleep apnea, and an AHI of 30 or more per hour is severe sleep apnea.4,5  Historically, the AHI has been favored for its simplicity in calculation and objective measurement of events like apnea and hypopnea,6 helping physicians to classify the severity of the disease.

Limitations of the AHI

The correlation between the AHI and clinical outcomes has been found to be inadequate.1,2,6,7 Since the AHI threshold is based on population averages, it fails to account for the unique characteristics of individual patients. This means that symptomatic patients may exhibit a low AHI, while asymptomatic individuals may be diagnosed with a high AHI. In such cases, healthcare providers face a challenge in determining the appropriate course of action.

Furthermore, the usefulness of the AHI as a metric in clinical settings is called into question when it is not strongly linked to symptoms or the risk of developing comorbid chronic diseases, such as cardiovascular disease, and does not provide information on the severity of the single events as they occur,8 presence of significant oxygen desaturation, ECG abnormalities, and sympathetic activation, that may imply more significant pathology than the AHI alone.6,7

Over time, there have been changes in the definition of the AHI, including notable modifications in how oxygen saturation and breathing cessation are defined, as well as the inclusion of arousals with hypopnea events.

The AHI alone cannot help evaluate symptoms like cognitive impairment, daytime sleepiness, or cardiovascular complications. Additionally, the methods employed in deriving the AHI can vary significantly in clinical practice. The measurement and scoring techniques used to calculate the AHI differ among sleep laboratories and devices, resulting in potential inconsistencies and inter-laboratory variability. These variations can compromise the accuracy and reliability of the AHI as a metric, making it challenging to draw direct comparisons between studies and treatment outcomes.8,9

Alternative Metrics

To truly understand individual risks and predict treatment outcomes, it is crucial to delve deeper into the intricacies of this condition. Achieving a comprehensive understanding of the complexity of OSA is imperative for advancing precision medicine and personalized care within this field.7, 10-14

As a response to this enlightened understanding, new, advanced polysomnographic metrics are starting to be developed, like hypoxic burden (HB),14 pulse wave amplitude drop (PWAD),15 adjusted AHI (a-AHI),16 and others, to characterize the full impact of the disease.

The HB aims to capture the total amount of respiratory event-related hypoxemia over the sleep period. The HB is defined as the total area under the respiratory event-related desaturation curve.15 The HB has a better association with cardiovascular disease than the AHI.16

The PWADs are typically observed concomitantly with cortical arousals, which occur spontaneously or after nocturnal events such as sleep apneas/hypopneas and leg movements. The PWAD events reflect transient vasoconstriction followed by vasodilation that occurs in response to surges in sympathetic activity. This is then followed by a compensatory parasympathetic response.18 PWA drops associated with respiratory events were correlated to cortical activity, suggesting that PWA drops could be used to indicate the brain’s response to respiratory events.20, 21

The a-AHI adds an obstruction severity parameter that includes durations of each individual apnea and hypopnea and areas of related desaturation normalized for total time analyzed and provides valuable additional information to the AHI, potentially enhancing the identification of patients with obstructive sleep apnea (OSA) who are at the greatest risk of mortality or cardiovascular complications.14

Utilizing these newly derived metrics, extracted from extensive information obtained through polysomnography (PSG), will significantly bolster our ability to identify various obstructive sleep apnea subtypes. (OSA). Additionally, these metrics will facilitate the exploration of the underlying mechanisms of the disease in relation to specific comorbidities, leading to the discovery of improved treatments for OSA.


To comprehensively address the patient journey of individuals with sleep apnea and enhance their outcomes, it is imperative to identify more effective tools for personalizing the treatment pathway. It is crucial to reevaluate the approach of condensing an entire night’s sleep into a single numerical value, particularly when this value, such as the AHI, exhibits weak correlations with symptoms and clinical outcomes.

While the sleep field has made significant strides in making sleep diagnostic studies more accessible, relying solely on the AHI and even employing various approaches to calculate the AHI raises valid concerns (e.g., using derived, indirect signals that correlate with AHI but do not measure airflow or effort directly). In some cases, the ease and accessibility offered by simplified or oversimplified tests may come at the expense of more detailed and thorough diagnostic assessments.

Though these tests can provide initial screening or basic insights into sleep disorders, they may not capture the intricacies needed for accurate diagnosis and personalized treatment planning. A broader perspective is needed to ensure that sleep apnea care and diagnosis are approached holistically, considering a range of factors beyond a single metric to achieve better patient outcomes.


Snorri Helgason is the Director of Market Access at Nox Medical in Reykjavik, Iceland.

This article originally appeared in the Sleep Lab Magazine Jul/Aug 2023.

Interesting reading

historical review and critical appraisal. J Sleep Res. 2020;29(4): e13066. doi:10.1111/jsr.13066


  1. Pevernagie DA, Gnidovec-Strazisar B, Grote L, et al. On the rise and fall of the apnea−hypopnea index: A historical review and critical appraisal. J Sleep Res. 2020;29(4):e13066. doi:10.1111/jsr.13066
  2. Malhotra A, Ayappa I, Ayas N, et al. Metrics of sleep apnea severity: beyond the apnea-hypopnea index. Sleep. 2021;44(7): zsab030. doi:10.1093/sleep/zsab030
  3. Berry RB, Abreu AR, Krishnan V, Quan SF, Strollo PJ, Malhotra RK. A transition to the American Academy of Sleep Medicine–recommended hypopnea definition in adults: initiatives of the Hypopnea Scoring Rule Task Force. Journal of Clinical Sleep Medicine. 2022;18(5):1419-1425. doi:
  4. Sleep-Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research. Sleep. 1999;22(5):667-689. doi:10.1093/sleep/22.5.667
  5. Obstructive Sleep Apnea. Published online 2008.
  6. Soori R, Baikunje N, D’sa I, Bhushan N, Nagabhushana B, Hosmane GB. Pitfalls of AHI system of severity grading in obstructive sleep apnoea. Sleep Sci. 2022;15(Spec 1):285-288. doi:10.5935/1984-0063.20220001
  7. Lim DC, Mazzotti DR, Sutherland K, et al. Reinventing Polysomnography in the Age of Precision Medicine. Sleep Med Rev. 2020; 52:101313. doi: 10.1016/j.smrv.2020.101313
  8. Lee YJ, Lee JY, Cho JH, Choi JH. Interrater reliability of sleep stage scoring: a meta-analysis. Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine. 2022;18(1):193-202. doi:
  9. Iftikhar IH, Finch CE, Shah AS, Augunstein CA, Ioachimescu OC. A meta-analysis of diagnostic test performance of peripheral arterial tonometry studies. J Clin Sleep Med. 2022;18(4):1093-1102. doi:10.5664/jcsm.9808
  10. Malhotra A, Mesarwi O, Pepin JL, Owens RL. Endotypes and phenotypes in obstructive sleep apnea. Curr Opin Pulm Med. 2020;26(6):609-614. doi:10.1097/MCP.0000000000000724
  11. Solelhac G, Sánchez-de-la-Torre M, Blanchard M, et al. Pulse Wave Amplitude Drops Index: A Biomarker of Cardiovascular Risk in Obstructive Sleep Apnea. Am J Respir Crit Care Med. 207(12):1620-1632. doi:10.1164/rccm.202206-1223OC
  12. Liu Y, Ghafoor AA, Hajipour M, Ayas N. Role of precision medicine in obstructive sleep apnoea. BMJ Med. 2023;2(1). doi:10.1136/bmjmed-2022-000218
  13. Zinchuk A, Gentry M, Concato J, Yaggi K. Phenotypes in obstructive sleep apnea: a definition, examples and evolution of approaches. Sleep Med Rev. 2017; 35:113-123. doi: 10.1016/j.smrv.2016.10.002
  14. Eckert DJ, White DP, Jordan AS, Malhotra A, Wellman A. Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets. Am J Respir Crit Care Med. 2013;188(8):996-1004. doi:10.1164/rccm.201303-0448OC
  15. Azarbarzin A, Sands SA, Stone KL, et al. The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study. Eur Heart J. 2019;40(14):1149-1157. doi:10.1093/eurheartj/ehy624
  16. Solelhac G, Sánchez-de-la-Torre M, Blanchard M, et al. Pulse Wave Amplitude Drops Index: A Biomarker of Cardiovascular Risk in Obstructive Sleep Apnea. Am J Respir Crit Care Med. 2023;207(12):1620-1632. doi:10.1164/rccm.202206-1223OC
  17. Muraja-Murro A, Kulkas A, Hiltunen M, et al. Adjustment of apnea-hypopnea index with severity of obstruction events enhances detection of sleep apnea patients with the highest risk of severe health consequences. Sleep Breath. 2014;18(3):641-647. doi:10.1007/s11325-013-0927-z
  18. Guo J, Xiao Y. New Metrics from Polysomnography: Precision Medicine for OSA Interventions. Nat Sci Sleep. 2023; 15:69-77. doi:10.2147/NSS.S400048
  19. Grote L, Zou D, Kraiczi H, Hedner J. Finger plethysmography–a method for monitoring finger blood flow during sleep disordered breathing. Respir Physiol Neurobiol. 2003;136(2-3):141-152. doi:10.1016/s1569-9048(03)00090-9
  20. Delessert A, Espa F, Rossetti A, Lavigne G, Tafti M, Heinzer R. Pulse Wave Amplitude Drops during Sleep are Reliable Surrogate Markers of Changes in Cortical Activity. Sleep. 2010;33(12):1687-1692.
  21. Bosi M, Milioli G, Riccardi S, et al. Arousal responses to respiratory events during sleep: the role of pulse wave amplitude. J Sleep Res. 2018;27(2):261-269. doi:10.1111/jsr.12593

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