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

Olli Pekka Ryynänen*, Timo Leppänen, Pekka Kekolahti, Esa Mervaala, Juha Töyräs

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
133 Downloads (Pure)


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).

Original languageEnglish
Pages (from-to)346-358
Number of pages13
JournalHealthcare Informatics Research
Issue number4
Publication statusPublished - 1 Oct 2018
MoE publication typeA1 Journal article-refereed


  • Bayesian analysis
  • Continuous positive airway pressure
  • Outcome assessment (health care)
  • Patient-specific modeling
  • Sleep apnea syndromes


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