Original article / research
Assessing the Utility of Oxygen Desaturation Index as a Diagnostic Tool for Obstructive Sleep Apnoea: A Cross-sectional Study
OC05-OC08
Correspondence
Dr. Subramanian Suriyan,
118, Krithika Nivas, Ananthanathan Nagar, Mannivakkam Extension, Chennai-600048, Tamil Nadu, India.
E-mail: drssmani@gmail.com
Introduction: Obstructive Sleep Apnoea (OSA) is a common yet underdiagnosed condition with serious medical consequences if not recognised early. Despite its limitations, the Apnoea-Hypopnoea Index (AHI) remains the most studied parameter for assessing the severity of OSA. This situation has created a need for a preliminary, alternative diagnostic tool for the early diagnosis and severity assessment of OSA in resource-limited settings.
Aim: To determine whether the Oxygen Desaturation Index (ODI) could serve as a simple yet equally effective diagnostic tool compared to the AHI in assessing the severity of OSA.
Materials and Methods: A cross-sectional study was conducted in SRM Medical College, Chennai, Tamil Nadu, India, involving 100 patients who underwent Level III polysomnography for suspected OSA. The study spanned one year, from May 2023 to May 2024. Ethical clearance for the study was obtained from the Institutional Review Board (IRB). Pearson’s correlation coefficient was applied to assess the relationship between AHI and ODI scores. Cohen’s weighted Kappa analysis Bland-Altman plot, ROC curve analysis, and Youden’s J Statistic were performed to determine the best ODI threshold for OSA severity. Statistical analysis was conducted using Statistical Package for Social Sciences (SPSS) version 22.0, ensuring robust and reliable results.
Results: Analysis of the obtained parameters suggested a strong correlation (r=0.94, p-value <0.001) between AHI and ODI scores. ROC curve analysis indicated that an ODI >32 had good effectiveness in detecting severe OSA.
Conclusion: The strong positive correlation and significant agreement between AHI values and ODI scores indicate that ODI could be reliable in diagnosing and assessing the severity of OSA, especially in resource-limited environments.