Can a prediction model combining self-reported symptoms, sociodemographic and clinical features serve as a reliable first screening method for sleep apnea syndrome in patients with stroke?

Field Value
Model ID14-028-01
Model NameAaronson 2014
Pubmed ID24378806
First AuthorAaronson
JournalArchives of Physical Medicine and Rehabilitation
Year2014
TitleCan a prediction model combining self-reported symptoms, sociodemographic and clinical features serve as a reliable first screening method for sleep apnea syndrome in patients with stroke?
Primary Index ConditionStroke
Secondary Index ConditionNOS
OutcomeSleep Apnea
Model Sample Size438
Cohort Sample Size438
Number of Events135
Follow-Up DurationDiagnosis
AUROC0.76
Calibration Reported1
CovariatesTo be updated
Mesh TermsTo be updated
Number of Validations0

External Validations