Research pick: Sleep breathing - "A review of the relationship between flow-volume curve and obstructive sleep apnoea"

There is increasing evidence that routine lung-function tests could play a role in diagnosing obstructive sleep apnoea (OSA), a common but frequently undetected sleep disorder. OSA occurs when the upper airway repeatedly collapses during sleep, interrupting breathing, lowering blood oxygen levels, and fragmenting sleep. Left untreated, OSA has been linked to raised blood pressure, metabolic problems, and in some cases, sudden death during sleep.

A review of existing research in the International Journal of Medical Engineering and Informatics highlights how flow-volume curves, a standard output from pulmonary function tests (PFTs), may provide a simpler and more accessible route to identifying the condition. The current diagnostic standard, polysomnography, involves overnight monitoring in a specialist laboratory and requires sophisticated equipment and trained personnel, limiting access and creating long waiting lists. Whereas the more modern approach is much simpler and far more accessible.

Indeed, lung-function tests are non-invasive, widely available, and quick to perform. They measure how much air a person can inhale and exhale, and at what speed, with the flow-volume curve providing a graphical representation of airflow against lung volume during forced breathing. Research indicates that patients with OSA often show distinctive features on these curves, such as reduced lung volumes, increased airway resistance, and altered ratios of forced expiratory volume (FEV1) to forced vital capacity (FVC). These changes are thought to reflect underlying airway collapse and compromised respiratory mechanics.

Despite the potential, relatively few studies have examined the diagnostic value of flow-volume curves systematically. Most focus on conventional spirometric measures or the “saw-tooth” pattern sometimes observed in OSA, employing standard statistical methods rather than more sophisticated computational analyses. There has been limited exploration of whether more detailed waveform features or derived biomarkers could more reliably distinguish OSA from normal respiratory function.

The current review highlights an opportunity to harness modern data science in this area. By applying machine learning to detailed flow-volume data, researchers could identify subtle respiratory signatures that are indicative of OSA. Automated, point-of-care screening, might then be possible by embedding this algorithm into a PFT device. This would allow faster, lower-cost diagnosis and early intervention for at-risk patients.

Eris, S.B., Bilgin, C., Eris, Ö. and Bozkurt, M.R. (2025) ‘A review of the relationship between flow-volume curve and obstructive sleep apnoea‘, Int. J. Medical Engineering and Informatics, Vol. 17, No. 5, pp.476-486.