Background: Obstructive sleep apnea (OSA) is a common sleep disorder associated with increased cardiovascular morbidity. Continuous positive airway pressure (CPAP) therapy is the standard treatment; however, its long-term cardiovascular benefits remain uncertain. Aim: To evaluate 5-year associations between CPAP therapy and the risks of cardiovascular diseases in patients with OSA. Methods: This retrospective cohort study used the TriNetX global database, including adults diagnosed with OSA between 2014 and 2025. Patients with prior cardiovascular disease were excluded. After 1:1 propensity score matching, 54,635 CPAP users were compared to matched nonusers. Participants were followed from 1 day to 5 years after the index date. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated for incident cardiovascular outcomes. Sensitivity analyses were performed at 1- and 3-month follow-up thresholds. Results: CPAP therapy was associated with significantly lower risks of stroke (HR 0.861), transient ischemic attack (HR 0.699), atrial fibrillation/flutter (HR 0.781), bradycardia (HR 0.899), ventricular arrhythmias (HR 0.875), pericarditis (HR 0.768), angina (HR 0.619), major adverse cardiovascular events (HR 0.936), and cardiac conduction and functional disorders (HR 0.861). No significant risk reduction was observed for pulmonary embolisms, deep vein thromboses, myocardial infarction, or structural cardiac abnormalities. Sensitivity analyses confirmed the robustness of most findings. Conclusions: In patients with OSA, CPAP therapy was associated with reduced risks of several cardiovascular outcomes over 5 years, although benefits were not observed across all conditions. These findings highlight the potential but selective cardiovascular benefits of CPAP therapy, warranting further prospective and randomized studies.
V. L. Amelia, Septi Melisa, Jason C. Hsu et al.· Biological Research for Nurs...· 0 citations
BACKGROUND
Coronary artery disease (CAD) is the leading cause of death globally and a major contributor to hospital readmission. This study aimed to predict 30-day mortality in patients hospitalized with acute and chronic CAD using a structured machine learning approach with data from multiple centers.
METHODS
We conducted a retrospective cohort study using patient data from the Taipei Medical University Clinical Research Database (TMUCRD). Multiple machine learning algorithms were employed to develop predictive models for 30-day mortality. Model performance was evaluated using a stratified fivefold cross-validation approach. Key performance metrics included the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 score.
RESULTS
A total of 23,267 patients (mean age 64.9 years) were included, with 1215 deaths overall (5.2%): 570 (3.7%) in the internal cohort (n = 15,510) and 645 (8.3%) in the external validation cohort (n = 7757, Shuang Ho Hospital). XGBoost achieved the best performance for the overall and acute CAD cohorts (AUROC 0.845 and 0.820, respectively), while logistic regression performed best for chronic CAD (AUROC 0.766). Key predictive features included the Charlson Comorbidity Index, hemoglobin level, emergency room admission status, age, and creatinine level.
CONCLUSION
The use of a structured machine learning approach to predict 30-day mortality in patients with acute and chronic CAD demonstrated promising discriminative performance, providing valuable insights that could enhance personalized care and inform clinical decisions.
Septi Melisa, P. Phan, Sheng-Hsuan Chien et al.· International Journal of Car...· 0 citations