Using clinical data to predict obstructive sleep apnea.

ABSTRACT:

Background

Obstructive sleep apnea (OSA) is a common disorder and associated with motor vehicle accidents, reduced quality of life and various comorbidities. It is necessary to identify clinical parameters that may predict the presence and severity of OSA.

Methods

Subjects with suspected OSA were consecutively recruited for development and validation of the models. Clinical data collected from participants included general information, OSA-related symptoms, questionnaire responses, and physical examination. Logistic and linear regressions were used to develop models to determine the presence and severity of OSA.

Results

All 202 subjects (157 men, 45 women; age range, 18-68 years) underwent polysomnography (PSG) and clinical assessment, of whom 62.3% were diagnosed wit

SUBMITTER: He S 

PROVIDER: S-EPMC8902098 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature