Composite outcome measures in a pragmatic clinical trial of chronic heart failure management: A comparative

Sungwon Chang1, Patricia M Davidson2, Phillip J Newton1

  • 1University of Technology, Sydney, Australia.

Insights

Different heart failure outcome scores weigh mortality, hospitalization, and quality of life differently. Understanding these composite outcome components is crucial for accurate interpretation in clinical trials.

Area of Science:

  • Cardiology
  • Clinical Trials
  • Health Outcomes Research

Background:

  • Composite outcomes are vital for assessing heart failure, integrating patient, clinician, and objective health measures.
  • Limited research has explored the specific composition and influence of components within these composite outcomes.

Purpose of the Study:

  • To compare the comparability and interpretability of three common heart failure composite outcomes: Packer's composite, Patient Journey, and the African American Heart Failure Trial (A-HeFT) scores.
  • To examine the influence of individual components (mortality, hospitalization, quality of life) on the final outcome within each scoring system.

Main Methods:

  • The Which heart failure intervention is most cost-effective & consumer friendly in reducing hospital care (WHICH(?)) Trial data was used to compare Packer's composite, Patient Journey, and A-HeFT scores.
  • Analysis focused on the contribution of mortality, hospitalization, and quality of life to the overall outcome in each scoring system.

Main Results:

  • All three composite outcomes included mortality, hospitalization, and quality of life, but their relative impact varied.
  • Hospitalization most influenced Packer's composite (67.7%), quality of life most influenced Patient Journey (61.5%), and mortality most influenced A-HeFT (45.4%).

Conclusions:

  • The contribution of each component to composite heart failure outcomes differs significantly across scoring systems.
  • Understanding the weighting of individual components within composite outcomes is essential for accurate interpretation of clinical trial results.
Abstract

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