Cloud algorithm-driven oximetry-based diagnosis of obstructive sleep apnoea in symptomatic habitually snoring children

The ability of a cloud-driven Bluetooth oximetry-based algorithm to diagnose obstructive sleep apnoea syndrome (OSAS) was examined in habitually snoring children concurrently undergoing overnight polysomnography. Children clinically referred for overnight in-laboratory polysomnographic evaluation for suspected OSAS were simultaneously hooked to a Bluetooth oximeter linked to a smartphone. Polysomnography findings were scored and the apnoea/hypopnoea index (AHIPSG) was tabulated, while oximetry data yielded an estimated AHIOXI using a validated algorithm. The accuracy of the oximeter in identifying correctly patients with OSAS in general, or with mild (AHI 1–5 events·h–1), moderate (5–10 events·h–1) or severe (>10 events·h–1) OSAS was examined in 432 subjects (6.5±3.2 years), with 343 having AHIPSG >1 event·h–1. The accuracies of AHIOXI were consistently >79% for all levels of OSAS severity, and specificity was particularly favourable for AHI >10 events·h–1 (92.7%). Using the criterion of AHIPSG >1 event·h–1, only 4.7% of false-negative cases emerged, from which only 0.6% of cases showed moderate or severe OSAS. Overnight oximetry processed via Bluetooth technology by a cloud-based machine learning-derived algorithm can reliably diagnose OSAS in children with clinical symptoms suggestive of the disease. This approach provides virtua...
Source: European Respiratory Journal - Category: Respiratory Medicine Authors: Tags: Sleep medicine Original Articles: Sleep Source Type: research