Assessing cardiac function in obstructive sleep apnea using a novel metric: integrating the respiratory event frequency and desaturation duration
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVES: Obstructive sleep apnea (OSA) is associated with impaired cardiac function, evidenced by a decreased left ventricular ejection fraction (LVEF). Traditional measures, including the apnea-hypopnea index (AHI) and lung-to-finger circulation time (LFCT), do not simultaneously consider the hypoxemic duration, frequency, and severity. This study introduces the respiratory event response time area (RERTA), integrating duration- and frequency-based OSA indices, and examined its associations with the LVEF. METHODS: ). The RERTA, a joint metric that reflects the event frequency and desaturation duration, was determined as the square root of the product of the AHI and mean LFCT. PSG parameters and related metrics were then examined for associations with echocardiographic measures. RESULTS: Among 34 participants (10 with mild-to-moderate and 24 with severe OSA), the severe group exhibited a lower median LVEF than the mild-to-moderate group (64.00% [61.00%-68.50%] vs. 70.00% [66.50%-72.75%], p < 0.05). After adjusting for age, gender, and the body-mass index (BMI), each 1-event/h increase in the AHI was linked to a borderline reduction in the LVEF (-0.71%, 95% CI: -1.42 to 0.00; p = 0.05). A 1-unit increase in the RERTA corresponded to a 0.33% decrease in the LVEF (95% CI: -0.62% to -0.04%, p < 0.05). CONCLUSIONS: The RERTA combines the AHI and mean LFCT, providing a joint assessment of circulatory stress in patients with OSA. This study highlights its potential utility in evaluating cardiac function associated with OSA, which can serve as a basis for future prospective research.
Source: BMC Pulmonary Medicine
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
Awards: Flipping Medical and Healthcare for Chronic Disease Management with Artificial Intelligence and Big Health Data Analytics( I ), Developing a Multimodal Time-Series Model to Predict Aberrant Driving Behavior in Scooter Couriers: Assessing the Impact of Sleep Disorders, Heart Rate Variability, and Environmental Exposure, G7630662126
OpenAlex ID: W4416145487
