by Matthew Tarler, Ph.D., Sarah Weimer, Craig Frederick, Michael Papsidero M.D., Hani Kayyali as seen in Sleep Diagnosis and Therapy
Study Objective: to assess the feasibility and accuracy of a web-based portable sleep monitor and scoring technology for sleep apnea evaluation in the home.
Introduction: Sleep Disordered Breathing (SDB) affects more than 20 million patients with serious health and economic costs. Patient resistance to sleep outside of the home, long-term disease management, and reduced reimbursement emphasize the necessity for a simple and cost effective Home Sleep Testing (HST) solution.
Methods: The technology consists of a web portal that facilitates data management including manual over-read and report generation. The web portal interfaces to a seven (7) channel HST monitor (SleepView) which follows the AASM channel set recommendations. The monitor incorporated two automated scoring algorithms: respiratory event detection and sleep time estimation. To assess feasibility and accuracy, the system was tested on 13 patients admitted for routine Polysomnography at the Cleveland Clinic. The morning after the PSG study, each patient was instructed on the sensor set-up and sent home with the monitor. Once the monitor is returned to the lab, data and morning questionnaires were uploaded to the webportal, scored automatically and manually over-read by a Registered Polysomnographic Technologist (RPSGT).
Results: The patients found the device and sensor set-up easy to use with patients rating the “overall experience with SleepView use” either good or excellent. When compared to PSG, the monitor had AHI sensitivity of 100%, and specificity of 67%. These results did not change when the AHI calculation used Total Recording Time (TRT) instead of Total Sleep Time (TST). However, when compared to in-lab results, the at home AHI calculations that used TST generated a closer approximation (smaller bias) when compared to the calculations that used TRT (-3.9 vs. -5.6, p<0.01). This confirms the role of sleep time in improving disease severity assessment.
Conclusions: a new web-based HST solution with an easy-to-use monitor, effective workflow and scoring solution was developed and tested successfully. At-home results showed strong correlation with PSG especially when TST was used in the AHI calculation. The monitor’s accuracy is attributed to utilizing conventional in-lab signals and a scoring method that relies on event detection algorithms combined with manual over-read. The system lends itself for efficient and streamlined HST deployments that require seamless networking of multiple stakeholders such as sleep labs, family physicians, nurses and scorers…