The Adult Congenital Heart Disease Anatomic and Physiological Classification: Associations with Clinical Outcomes in Patients with Atrial Arrhythmias.
Anastasios KartasΑndreas S PapazoglouDiamantis KosmidisDimitrios V MoysidisAmalia BaroutidouIoannis DoundoulakisStefanos DespotopoulosElena VranaAthanasios KoutsakisGeorgios P RampidisDespoina NtiloudiSotiria LioriTereza MousiamaDimosthenis AvramidisSotiria ApostolopoulouAlexandra FrogoudakiAfrodite TzifaHaralambos KarvounisGeorge GiannakoulasPublished in: Diagnostics (Basel, Switzerland) (2022)
The implications of the adult congenital heart disease anatomic and physiological classification (AP-ACHD) for risk assessment have not been adequately studied. A retrospective cohort study was conducted using data from an ongoing national, multicentre registry of patients with ACHD and atrial arrhythmias (AA) receiving apixaban (PROTECT-AR study, NCT03854149). At enrollment, patients were stratified according to Anatomic class (AnatC, range I to III) and physiological stage (PhyS, range B to D). A follow-up was conducted between May 2019 and September 2021. The primary outcome was a composite of death from any cause, any major thromboembolic event, major or clinically relevant non-major bleeding, or hospitalization. Cox proportional-hazards regression modeling was used to evaluate the risks for the outcome among AP-ACHD classes. Over a median 20-month follow-up period, 47 of 157 (29.9%) ACHD patients with AA experienced the composite outcome. Adjusted hazard ratios (aHR) with 95% confidence intervals (CI) for the outcome in PhyS C and PhyS D were 1.79 (95% CI 0.69 to 4.67) and 8.15 (95% CI 1.52 to 43.59), respectively, as compared with PhyS B. The corresponding aHRs in AnatC II and AnatC III were 1.12 (95% CI 0.37 to 3.41) and 1.06 (95% CI 0.24 to 4.63), respectively, as compared with AnatC I. In conclusion, the PhyS component of the AP-ACHD classification was an independent predictor of net adverse clinical events among ACHD patients with AA.
Keyphrases
- congenital heart disease
- atrial fibrillation
- machine learning
- risk assessment
- deep learning
- transcription factor
- end stage renal disease
- newly diagnosed
- emergency department
- heart failure
- chronic kidney disease
- left atrial
- randomized controlled trial
- venous thromboembolism
- big data
- healthcare
- electronic health record
- peritoneal dialysis
- climate change
- patient reported outcomes
- left ventricular