The most common cause of mortality from Fabry disease is cardiac symptoms, yet risk stratification was insufficient. More evidence-based therapy recommendations may be made possible by identifying individuals at risk of a negative cardiac outcome. Although modern cardiac magnetic resonance biomarkers are routinely used, their prognostic significance is unknown. For a study, researchers sought to create, internally validate, and assess the effectiveness of a predictive model that incorporates modern deep phenotyping and may be used to calculate individual risks for poor cardiac outcomes in Fabry disease patients.

The participants in the long-term prospective cohort research were 200 consecutive Fabry disease patients who underwent clinical cardiac magnetic resonance. Median follow-up lasted 4.5 years (IQR: 2.7-6.3 years). Cox, proportional hazards modeling was employed to create prognostic models. An unfavorable cardiac event composite constituted the outcome. The performance of the model was assessed. A risk calculator was created that estimates the patient’s 5-year risk of a poor cardiac outcome for both men and women.

Age, native myocardial T1 dispersion (SD of per voxel myocardial T1 relaxation times), and indexed left ventricular mass made up the best-performing, internally validated, parsimonious multivariable model. Across 5 imputed model development data sets, the median optimism-adjusted c-statistic was 0.77 (95% CI: 0.70-0.84). The complete risk profile has great model calibration.

The study created and internally validated a risk prediction model that can be readily incorporated into clinical treatment and reliably predicts a patient’s 5-year chance of a worse cardiac outcome, encompassing both men and women with Fabry disease. External verification was necessary.