Clinical endpoints in pragmatic heart failure trials: From data collection to clinical endpoint classification

Area of Science:

• Clinical trials methodology
• Cardiovascular research
• Health informatics

Background:

• Clinical endpoint classification (CEC) is crucial for consistent assessment of safety and efficacy in clinical trials.
• Heart failure (HF) trials face challenges due to subjective event evaluation and variable management of worsening HF.
• Pragmatic clinical trials require efficient CEC strategies to enhance generalizability and reduce participant burden.

Purpose of the Study:

• To review and summarize common clinical endpoint classification (CEC) strategies.
• To examine CEC approaches utilized in recent heart failure (HF) pragmatic trials.
• To identify challenges and considerations for implementing CEC in HF pragmatic trials.

Main Methods:

• Literature review of common CEC strategies.
• Analysis of CEC approaches in recent heart failure pragmatic trials.
• Discussion of challenges from endpoint selection to data collection and classification.

Main Results:

• Common CEC strategies include traditional methods, investigator-reported endpoints, real-world data (RWD), and large language models (LLMs).
• Recent HF pragmatic trials have adopted various CEC strategies to meet trial objectives.
• Key considerations involve endpoint selection, data collection methods, and the application of advanced technologies like LLMs.

Conclusions:

• Diverse CEC strategies exist, offering flexibility for pragmatic trials.
• The choice of CEC strategy impacts trial generalizability and efficiency.
• Addressing challenges in endpoint definition, data acquisition, and classification is vital for successful HF pragmatic trials.