Predicting Nondiagnostic Home Sleep Apnea Tests Using Machine Learning.

ABSTRACT:

Study objectives

Home sleep apnea testing (HSAT) is an efficient and cost-effective method of diagnosing obstructive sleep apnea (OSA). However, nondiagnostic HSAT necessitates additional tests that erode these benefits, delaying diagnoses and increasing costs. Our objective was to optimize this diagnostic pathway by using predictive modeling to identify patients who should be referred directly to polysomnography (PSG) due to their high probability of nondiagnostic HSAT.

Methods

HSAT performed as the initial test for suspected OSA within the Veterans Administration Greater Los Angeles Healthcare System was analyzed retrospectively. Data were extracted from pre-HSAT questionnaires and the medical record. Tests were diagnostic if there was a respiratory event index (REI) ≥ 5 e

SUBMITTER: Stretch R 

PROVIDER: S-EPMC6853403 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature