Prediction of sleep-disordered breathing after stroke.

ABSTRACT:

Objective/background

Sleep-disordered breathing (SDB) is highly prevalent after stroke and is associated with poor outcomes. Currently, after stroke, objective testing must be used to differentiate patients with and without SDB. Within a large, population-based study, we evaluated the usefulness of a flexible statistical model based on baseline characteristics to predict post-stroke SDB.

Patients/methods

Within a population-based study, participants (2010-2018) underwent SDB screening, shortly after ischemic stroke, with a home sleep apnea test. The respiratory event index (REI) was calculated as the number of apneas and hypopneas per hour of recording; values ≥10 defined SDB. The distributed random forest classifier (a machine learning technique) was applied to predict SDB

SUBMITTER: Brown DL 

PROVIDER: S-EPMC7666648 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature