RELATIVE EFFECTIVENESS OF DRONEDARONE VS. OTHER TREATMENTS OF ATRIAL FIBRILLATION (EFFECT-AF)

Study type

• Study topic:
  Disease /health condition

  Human medicinal product
• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology

  Effectiveness study (incl. comparative)
• Data collection methods:
  Combined primary data collection and secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Anatomical Therapeutic Chemical (ATC) code:
  (C01BA) Antiarrhythmics, class Ia

  Antiarrhythmics, class Ia

  (C01BC) Antiarrhythmics, class Ic

  Antiarrhythmics, class Ic

  (C01BD01) amiodarone

  amiodarone

  (C01BD07) dronedarone

  dronedarone

  (C07AA07) sotalol

  sotalol
• Medical condition to be studied:
  Atrial fibrillation

Population studied

• Short description of the study population:
  Historic-prospective cohort of attrial fibrillation in Germany, Spain, Italy and USA who were exposed to dronedarone or alternative antiarrhythmic drugs of interest.
• Age groups:
  • Adults (18 to < 46 years)
  • Adults (46 to < 65 years)
  • Adults (65 to < 75 years)
  • Adults (75 to < 85 years)
  • Adults (85 years and over)
• Special population of interest:
  Other
• Special population of interest, other:
  Patients with atrial fibrillation
• Estimated number of subjects:
  1009

Study design details

• Main study objective:
  To evaluate the relative effectiveness of dronedarone in real world clinical practice versus other anti-arrhythmic agents.
• Outcomes:
  Recurrence of Atrial Fibrillation, Cardiovascular hospitalisation,AV node ablation and catheter ablation for Atrial Fibrillation (AF),Progression to permanent AF,Clinical progression to heart failure and left ventricular systolic dysfunction Congestive heart failure,Interstitial pulmonary disease,Liver injury/toxicity,Renal insufficiency/failure,Cerebrovascular accident/Stroke,Myocardial infarction,Torsade de pointes,Death
• Data analysis plan:
  The following principles of the analysis may be employed:• Dynamic population time for denominators.• Events of interest to be considered as discrete.• Same patient may contribute population-time to different exposures.• Autocorrelation between events/within patients’ denominators will be considered using GEE or mixed effects models. • Propensity score methods and inverse probability weighted estimators will be used to enhance comparative validity and account for underrepresented patient populations in the enrolled study sample.