Optimal sampling in derivation studies was associated with improved discrimination in external validation for heart...

Area of Science:

• Cardiology
• Medical Informatics
• Biostatistics

Background:

• Prognostic models are essential tools in heart failure (HF) management.
• External validity, or generalizability, of these models is often limited.
• Identifying factors that ensure model performance in new populations is critical.

Purpose of the Study:

• To identify key determinants of external validity for prognostic models in heart failure.
• To assess the impact of study characteristics on the performance of HF prognostic models.

Main Methods:

• Systematic literature search for HF prognostic models predicting 30-day mortality.
• Performance evaluation in a cohort of 3,452 acute HF patients.
• Application of critical appraisal tools to assess bias and study characteristics.

Main Results:

• 224 models were identified from 6,354 studies; mean c-statistic was 0.64.
• Optimal sampling, reflecting the gap between study and target populations, was significantly associated with higher model performance (β=0.25, P<0.001).
• This association remained significant after adjusting for study characteristics (β=0.24, P=0.01).

Conclusions:

• Optimal sampling in derivation studies is a primary determinant of external validity for HF prognostic models.
• Improving how study populations represent the intended patient groups enhances model generalizability.
• These findings can guide the development and selection of more reliable prognostic tools for heart failure.