Individual patient data network meta-analysis of mortality effects of implantable cardiac devices

B Woods1, N Hawkins2, S Mealing3

  • 1Centre for Health Economics, University of York, York, UK Department of Health Economics, ICON Clinical Research, Oxford, UK.

Insights

Cardiac resynchronisation therapy-defibrillators (CRT-D) offer the greatest mortality reduction in heart failure patients. Benefits vary by patient characteristics like QRS duration, LBBB, age, and gender, informing personalized treatment decisions.

Area of Science:

  • Cardiology
  • Medical Devices
  • Clinical Trials

Background:

  • Heart failure with reduced ejection fraction (HFrEF) is a significant cause of mortality.
  • Implantable cardioverter defibrillators (ICD), cardiac resynchronisation therapy pacemakers (CRT-P), and combination therapy (CRT-D) are used to manage HFrEF.
  • Previous studies suggest these devices reduce all-cause mortality compared to medical therapy alone.

Purpose of the Study:

  • To synthesize data from major randomized controlled trials on device therapy for HFrEF.
  • To estimate the comparative mortality effects of ICD, CRT-P, and CRT-D.
  • To investigate how device benefits vary according to patient characteristics.

Main Methods:

  • Network meta-analysis of individual patient data from 13 randomized trials.
  • Inclusion of 12,638 patients with HFrEF.
  • Adjustment for patient characteristics as predictors of mortality benefit.

Main Results:

  • Unadjusted analyses showed CRT-D (42% mortality reduction) was most effective, followed by ICD (29%) and CRT-P (28%) versus medical therapy.
  • CRT-D demonstrated greater mortality reduction than CRT-P (19%) and ICD (18%).
  • QRS duration (≥150 ms), LBBB morphology, female gender, and younger age (<60) were significant predictors of differential device benefit.

Conclusions:

  • Device therapy offers significant mortality benefits in HFrEF patients.
  • Patient characteristics, including QRS duration, LBBB, age, and gender, modify these benefits.
  • These findings support personalized treatment decisions and shared decision-making in clinical practice.
Abstract