Validated Model for Prediction of Adverse Cardiac Outcome in Patients With Fabry Disease

Christopher Orsborne1, Joshua Bradley2, Laura J Bonnett3

  • 1Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom; Manchester University NHS Foundation Trust, Wythenshawe, Manchester, United Kingdom; Salford Royal NHS Foundation Trust, Salford, United Kingdom.

Insights

A new risk model for Fabry disease accurately predicts adverse cardiac events using cardiac MRI biomarkers. This tool aids in identifying high-risk patients for better treatment guidance.

Area of Science:

  • Cardiology
  • Genetics
  • Biomarkers

Background:

  • Cardiac manifestations of Fabry disease are a leading cause of mortality.
  • Current risk stratification for adverse cardiac outcomes is inadequate.
  • Prognostic value of cardiac magnetic resonance (CMR) biomarkers needs clarification.

Purpose of the Study:

  • Develop and validate a prognostic model for predicting adverse cardiac outcomes in Fabry disease patients.
  • Incorporate deep phenotyping and CMR biomarkers for precise risk estimation.
  • Create a risk calculator for individual 5-year risk assessment.

Main Methods:

  • Longitudinal prospective cohort study of 200 Fabry disease patients.
  • Utilized cardiac magnetic resonance (CMR) imaging.
  • Developed prognostic models using Cox proportional hazards modeling with a median follow-up of 4.5 years.

Main Results:

  • The best model included age, myocardial T1 dispersion, and left ventricular mass.
  • Achieved an optimism-adjusted c-statistic of 0.77 (95% CI: 0.70-0.84).
  • Demonstrated excellent model calibration across the risk spectrum.

Conclusions:

  • A validated risk prediction model for 5-year adverse cardiac outcome in Fabry disease was developed.
  • The model accurately predicts risk for both men and women.
  • This tool can be integrated into clinical practice, with external validation recommended.
Abstract

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