Bayesian network model to evaluate the effectiveness of continuous positive airway pressure treatment of sleep apnea

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2018-10-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
13
346-358
Series
Healthcare Informatics Research, Volume 24, issue 4
Abstract
Objectives: The association between obstructive sleep apnea (OSA) and mortality or serious cardiovascular events over a long period of time is not clearly understood. The aim of this observational study was to estimate the clinical effectiveness of continuous positive airway pressure (CPAP) treatment on an outcome variable combining mortality, acute myocardial infarction (AMI), and cerebrovascular insult (CVI) during a follow-up period of 15.5 years (186 ± 58 months). Methods: The data set consisted of 978 patients with an apnea-hypopnea index (AHI) ≥5.0. One-third had used CPAP treatment. For the first time, a data-driven causal Bayesian network (DDBN) and a hypothesis-driven causal Bayesian network (HDBN) were used to investigate the effectiveness of CPAP. Results: In the DDBN, coronary heart disease (CHD), congestive heart failure (CHF), and diuretic use were directly associated with the outcome variable. Sleep apnea parameters and CPAP treatment had no direct association with the outcome variable. In the HDBN, CPAP treatment showed an average improvement of 5.3 percentage points in the outcome. The greatest improvement was seen in patients aged ≤55 years. The effect of CPAP treatment was weaker in older patients (>55 years) and in patients with CHD. In CHF patients, CPAP treatment was associated with an increased risk of mortality, AMI, or CVI. Conclusions: The effectiveness of CPAP is modest in younger patients. Long-term effectiveness is limited in older patients and in patients with heart disease (CHD or CHF).
Description
Keywords
Bayesian analysis, Continuous positive airway pressure, Outcome assessment (health care), Patient-specific modeling, Sleep apnea syndromes
Other note
Citation
Ryynänen , O P , Leppänen , T , Kekolahti , P , Mervaala , E & Töyräs , J 2018 , ' Bayesian network model to evaluate the effectiveness of continuous positive airway pressure treatment of sleep apnea ' , Healthcare Informatics Research , vol. 24 , no. 4 , pp. 346-358 . https://doi.org/10.4258/hir.2018.24.4.346