Predicting the 10-year risk of cardiomyopathy in long-term survivors of childhood cancer

  • 0St. Jude Children's Research Hospital, Memphis, USA.

Summary

This summary is machine-generated.

This study developed accurate models to predict the 10-year risk of cardiomyopathy in childhood cancer survivors. These models improve identification of survivors needing closer cardiac monitoring beyond current guidelines.

Area Of Science

  • Cardiovascular Medicine
  • Oncology
  • Genetics

Background

  • Childhood cancer survivors face a heightened risk of treatment-related cardiomyopathy and cardiac death.
  • Early identification of at-risk survivors is crucial for timely intervention and improved long-term outcomes.

Purpose Of The Study

  • To develop and validate a clinically applicable risk prediction model for cardiomyopathy in long-term childhood cancer survivors.
  • To enhance the identification of survivors at high risk for developing cardiomyopathy.

Main Methods

  • Utilized data from the St. Jude Lifetime Cohort (SJLIFE) for model development and the Childhood Cancer Survivor Study (CCSS) for validation.
  • Assessed demographic factors, cardiovascular risks, treatment exposures, and polygenic risk scores (PRSs) for cardiomyopathy.
  • Employed multivariable Poisson regression to predict 10-year cardiomyopathy risk (CTCAE grade ≥3) and assessed model performance using area under the receiver operating characteristic curve (AUC).

Main Results

  • Clinical models incorporating sex, age at diagnosis, anthracycline dose, and heart radiation achieved high AUC (0.833 in SJLIFE, 0.812 in CCSS).
  • Adding PRSs for hypertrophic cardiomyopathy and left ventricular end-systolic volume improved AUC in the CCSS cohort (0.822).
  • The PRS model identified fewer survivors as high-risk compared to existing guidelines but indicated a 1.5-times greater risk for those identified.

Conclusions

  • Developed and validated high-performance models for estimating 10-year cardiomyopathy risk in childhood cancer survivors.
  • These models offer improved risk stratification compared to current survivorship care guidelines.
  • Results can guide enhanced surveillance strategies for survivors at risk of cardiac complications.

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