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Updated: May 27, 2025

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Reporting of Fairness Metrics in Clinical Risk Prediction Models Used for Precision Health: Scoping Review.

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Fairness metrics are rarely used in clinical risk prediction models, and study populations are often not diverse. Integrating fairness metrics can improve health equity.

Keywords:
COVID-19biascardiovascular diseaseclinical risk predictionequityrisk stratificationsensitive features

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Area of Science:

  • Health Informatics
  • Precision Health
  • Clinical Prediction Modeling

Background:

  • Clinical risk prediction models are key for personalized health care.
  • Fairness metrics are crucial for evaluating disparities in these models.
  • Current use of fairness metrics in clinical prediction is infrequent and lacks empirical evaluation.

Purpose of the Study:

  • To assess the adoption of fairness metrics in clinical risk prediction models.
  • To empirically evaluate popular models for cardiovascular disease and COVID-19.

Main Methods:

  • A scoping literature review was conducted in November 2023.
  • Searched Google Scholar for high-impact publications on clinical risk prediction models.
  • Focused on models for cardiovascular disease (CVD) and COVID-19.

Main Results:

  • No reviewed articles evaluated fairness metrics.
  • Sex-stratified models were used in 26% of CVD and 9% of COVID-19 studies.
  • Study populations were largely homogeneous for race/ethnicity in both disease areas.

Conclusions:

  • The use of fairness metrics in clinical risk prediction is rare.
  • There is an urgent need for more diverse study cohorts and data collection.
  • An implementation framework is proposed to integrate fairness metrics for more equitable prediction.