The Beginning of the AI-Enabled Preventative PAP Therapy Era
Abstract
Positive Airway Pressure (PAP) therapy is the most common and efficacious treatment
for Obstructive Sleep Apnea (OSA). However, it suffers from poor patient
adherence due to discomfort and may not fully alleviate all adverse consequences of
OSA. Identifying abnormal respiratory events before they have occurred may allow
for improved management of PAP levels, leading to improved adherence and better
patient outcomes. Our previous work has resulted in the successful development of
an Artificial Intelligence (AI) algorithm for the prediction of future apneic events
using existing airflow and air pressure sensors available internally to PAP devices.
Although researchers have studied the use of AI for the prediction of apneas, research
to date has focused primarily on using external polysomnography sensors that add
to patient discomfort and has not investigated the use of internal-to-PAP sensors
such as air pressure and airflow to predict and prevent respiratory events. We hypothesized
that by using our predictive software, OSA events could be proactively
prevented while maintaining patients’ sleep quality. An intervention protocol was
developed and applied to all patients to prevent OSA events. Although the protocol’s
cool-down period limited the number of prevention attempts, analysis of 11
participants revealed that our system improved many sleep parameters, which included
a statistically significant 31.6% reduction in Apnea-Hypopnea Index, while
maintaining sleep quality. Most importantly, our findings indicate the feasibility of
unobtrusive identification and unique prevention of each respiratory event as well as
paving the path to future truly personalized PAP therapy by further training of AI
models on individual patients.