The Diagnostic Evolution in Obstructive Sleep Apnea : From ODI to AHI to Hypoxic Burden [Part I (i) : Oxygen Desaturation Index (ODI)]

A Paradigm Shift in How We Measure Sleep-Disordered Breathing

Introduction

For decades, clinicians treating obstructive sleep apnea (OSA) have relied on metrics that count events—drops in oxygen, pauses in breathing—as proxies for disease severity. Yet as our understanding of OSA’s pathophysiology has deepened and AI empowers diving deeper for data analyses, a fundamental question has emerged: Are we measuring what truly matters?

The journey from the Oxygen Desaturation Index (ODI) to the Apnea-Hypopnea Index (AHI) to the emerging concept of Hypoxic Burden represents more than technical refinement. It reflects a philosophical evolution in sleep medicine—from simply counting respiratory events to quantifying their physiological consequences.

This blog traces that evolution, examining the clinical implications of each metric, identifying which patient populations benefit most from each measure, and exploring how hypoxic burden may revolutionize long-term treatment monitoring. In Part I, we will discuss the rationale for the evolution and overview of the difference in definition of the parameters (i) ODI, (ii) AHI, (iii) Hypoxic Burden. In Part II, we will further discuss which disease entity, demographics of the patients we should take special attention to these parameters for better patient care.

Part I (i): The Oxygen Desaturation Index (ODI) — Where It All Began

Historical Context and Definition

The Oxygen Desaturation Index emerged in the 1980s as pulse oximetry became widely available and affordable. ODI measures the number of times per hour of sleep that blood oxygen saturation falls by a specified threshold—typically ≥3% or ≥4%—from baseline(Rashid et al., 2021)

The practical appeal was immediate: a simple finger probe could measure intermittent hypoxemia, capturing the core physiological danger of sleep apnea. The resulting Oxygen Desaturation Index (ODI) has proven to be a highly relevant clinical metric. For example, the large-scale, community-based Japanese CIRCS study of 4,398 middle-aged residents demonstrated a strong correlation between ODI and the three-year incidence of type 2 diabetes(Muraki et al., 2010). Furthermore, ODI serves as a reliable predictor for adverse cardiovascular outcomes (Cao et al., 2020). 

Clinical Implications of ODI

The ODI provides a direct window into the hypoxemic consequences of sleep-disordered breathing. This has several important clinical implications:

Cardiovascular Risk Stratification:

Intermittent hypoxemia drives oxidative stress, sympathetic activation, and endothelial dysfunction—key mechanisms linking OSA to hypertension, arrhythmias, and atherosclerosis. Studies have shown that ODI independently associated with incident hypertension(Tkacova et al., 2014) and cardiovascular outcomes (He et al., 2023).

Screening Utility: 

The simplicity of overnight oximetry makes the Oxygen Desaturation Index (ODI) a valuable tool for home-based preoperative screening, allowing clinicians to develop appropriate perioperative management plans. For instance, an ODI greater than 10 events per hour demonstrates a sensitivity of 93% and a specificity of 75% in detecting moderate-to-severe OSA (Chung et al., 2012). Moreover, when standard polysomnography is unavailable, ODI serves as an effective alternative for identifying moderate-to-severe OSA in snoring children within resource-limited settings (Polytarchou et al., 2023)

Monitoring Oxygen-Sensitive Conditions: 

While comorbid chronic obstructive pulmonary disease (COPD) elevates the risks of mortality and hospitalization, treatment with CPAP resulted in a non-significant increase in these risks compared to remaining untreated (Marin et al., 2010). Furthermore, the Oxygen Desaturation Index (ODI) has been shown to correlate positively with pulmonary function metrics (FEV₁, FEV₁/FVC, and FEV%) and inversely with airflow limitation. Consequently, ODI serves as a strong predictive tool, particularly for patients with milder COPD and suspected OSA (Tamai et al., 2016).

Limitations That Prompted Evolution

Despite its utility, ODI’s limitations became increasingly apparent. The metric ignores respiratory events that do not produce measurable desaturation—common in younger patients with robust ventilatory reserves. It also fails to capture the arousal-related sleep fragmentation that drives daytime symptoms. Furthermore, nocturnal pulse oximeter cannot distinguish between central apnea from obstructive events, limiting its diagnostic specificity (Rashid et al., 2021). 

In Part II, we will discuss why ii) Apnea-Hypopnea Index (AHI) has been evolved and what are the limitations of AHI that are still unable to resolve. 

REFERENCES

Cao, W., Luo, J., Xiao, Y., 2020. <p>A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea</p>. Nat. Sci. Sleep 12, 1023–1031. https://doi.org/10.2147/NSS.S275252

Chung, F., Liao, P., Elsaid, H., Islam, S., Shapiro, C.M., Sun, Y., 2012. Oxygen Desaturation Index from Nocturnal Oximetry: A Sensitive and Specific Tool to Detect Sleep-Disordered Breathing in Surgical Patients. Anesth. Analg. 114, 993–1000. https://doi.org/10.1213/ANE.0b013e318248f4f5

He, S., Cistulli, P.A., de Chazal, P., 2023. A Review of Novel Oximetry Parameters for the Prediction of Cardiovascular Disease in Obstructive Sleep Apnoea. Diagnostics 13, 3323. https://doi.org/10.3390/diagnostics13213323

Muraki, I., Tanigawa, T., Yamagishi, K., Sakurai, S., Ohira, T., Imano, H., Kitamura, A., Kiyama, M., Sato, S., Shimamoto, T., Konishi, M., Iso, H., for the CIRCS Investigators, 2010. Nocturnal intermittent hypoxia and the development of type 2 diabetes: the Circulatory Risk in Communities Study (CIRCS). Diabetologia 53, 481–488. https://doi.org/10.1007/s00125-009-1616-0

Polytarchou, A., Ohler, A., Moudaki, A., Koltsida, G., Kanaka-Gantenbein, C., Kheirandish-Gozal, L., Gozal, D., Kaditis, A.G., 2023. Nocturnal oximetry parameters as predictors of sleep apnea severity in resource-limited settings. J. Sleep Res. 32, e13638. https://doi.org/10.1111/jsr.13638

Rashid, N.H., Zaghi, S., Scapuccin, M., Camacho, M., Certal, V., Capasso, R., 2021. The Value of Oxygen Desaturation Index for Diagnosing Obstructive Sleep Apnea: A Systematic Review. The Laryngoscope 131, 440–447. https://doi.org/10.1002/lary.28663

Tamai, K., Matsuoka, H., Suzuki, Y., Yoshimatsu, H., Masuya, D., Nakashima, N., Okada, N., Oda, N., Inoue, S., Koma, Y., Otsuka, A., 2016. Nocturnal Oxygen Desaturation Index is Inversely Correlated with Airflow Limitation in Patients with Chronic Obstructive Pulmonary Disease. COPD J. Chronic Obstr. Pulm. Dis. 13, 235–240. https://doi.org/10.3109/15412555.2015.1074995

Tkacova, R., McNicholas, W.T., Javorsky, M., Fietze, I., Sliwinski, P., Parati, G., Grote, L., Hedner, J., 2014. Nocturnal intermittent hypoxia predicts prevalent hypertension in the European Sleep Apnoea Database cohort study. Eur. Respir. J. 44, 931–941. https://doi.org/10.1183/09031936.00225113

Publications by Belun :

1) “Belun® Ring Platform: a novel home sleep apnea testing system for assessment of obstructive sleep apnea” (https://jcsm.aasm.org/doi/10.5664/jcsm.8592),

2) “Detection of obstructive sleep apnea using Belun® Sleep Platform wearable with neural network based algorithm and its combined use with STOP-Bang questionnaire” (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0258040),

3) “Belun® Ring (Belun® Sleep System BLS-100): Deep Learning-Facilitated Wearable Enables OSA Detection, Apnea Severity Categorization, and Sleep Stage Classification in Patients Suspected of OSA”  (https://pubmed.ncbi.nlm.nih.gov/37380590/), 

4) “Correlation of Pulse Rate Variability(PRV) and Heart Rate Variability(HRV) Metrics During Sleep in Subjects Suspected of OSA” (https://academic.oup.com/sleep/article/46/Supplement_1/A420/7182347)

5) “The Belun sleep platform to diagnose obstructive sleep apnea in patients with hypertension and high cardiovascular risk” (https://pubmed.ncbi.nlm.nih.gov/37071415/)

6) “Integrating Body Sensor into a Wearable Platform to Enhance the Identification of Central and Mixed Apneas”. (https://academic.oup.com/sleep/article/47/Supplement_1/A222/7654683) 

7) “Using a Wearable’s Multi-Night Capability to Mitigate Night-to-Night Variability in a Dental Clinic Cohort”. J Dental Sleep Med. 2026; 13(1). (https://aadsm.org/journal/original_article_1_issue_131.php)