Researchers led by Robert Stretch from the Division of Pulmonary, Critical Care & Sleep Medicine at UCLA have developed an algorithm that can predict whether a patient will have a non-diagnostic home sleep apnea test based upon data from the electronic health record and a brief questionnaire.
Obstructive sleep apnea (OSA) affects between 4-37% of the adult population depending on the diagnostic criteria applied and population studied. Diagnostic testing for OSA typically starts with an “unattended” home sleep apnea test (HSAT) using a portable device. Since this test has a 17% false-negative rate, it is recommended that all patients who have a non-diagnostic initial HSAT should undergo an “attended” in-laboratory polysomnogram (PSG). A non-diagnostic HSAT is one in which the recording is technically inadequate (i.e. due to signal loss) or appears normal (i.e. respiratory event index < 5/hr). In clinical practice the rate of non-diagnostic HSATs varies between 15-30% of all studies. The ability to predict a non-diagnostic HSAT result prior to the test being ordered allows clinicians to pre-emptively order a PSG instead, thereby minimizing harms in the form of delayed diagnoses, missed diagnoses, additional financial burden to the patient and healthcare system, and inefficient use of limited resources.
Researchers led by Robert Stretch from the Division of Pulmonary, Critical Care & Sleep Medicine at UCLA have developed an algorithm that predicts whether a patient will have a non-diagnostic home sleep apnea test. The algorithm was developed using machine learning techniques and uses data from the electronic health record (automatically sourced) and patient responses to a brief questionnaire to make predictions with a high degree of precision and accuracy. Assuming all patients with a non-diagnostic HSAT subsequently undergo PSG (as per American Academy of Sleep Medicine guidelines), implementing this model to guide testing would result in the following:
For every 1000 patients currently undergoing HSAT as their initial test for OSA…
The algorithm’s classification threshold can also be adjusted to meet the specific needs of an institution. For example, one such alternative threshold results in a 24.4% reduction in the number of patients needing both HSAT and PSG while only increasing total PSGs performed by 2.6%.
Initial derivation and validation in a split cohort of 613 patients.
obstructive sleep apnea, OSA, Sleep apnea, sleep, diagnosis, home sleep apnea test, polysomnography, HSAT, PSG, machine learning, clinical decision support, triage, sleep study