Creating an Optimal Approach for Diagnosing Sleep Apnea

Sleep apnea is nowadays recognized as a treatable chronic disease and awareness of it has increased, leading to an upsurge in demand for diagnostic testing. Conventionally, diagnosis depends on overnight polysomnography in a sleep clinic, which is highly human-resource intensive and ignores the night-to-night variability in classical sleep apnea markers, such as the apnea-hypopnea index. In this review, the authors summarize the main improvements that could be made in the sleep apnea diagnosis strategy; how technological innovations and multi-night home testing could be used to simplify, increase access, and reduce costs of diagnostic testing while avoiding misclassification of severity.

Key points

- Home multi-night sleep testing reduces misclassification of sleep apnea level of severity.
- Scoring of abnormal respiratory events and sleep disturbances could be assisted by artificial intelligence to reduce burden of manual scoring and inter-scorer variability.
- Sleep testing methods should be low-cost, simple to install, and easy to use at home.
- Robust clinical trials are needed to validate new sensors, algorithms, and digital solutions.