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Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated

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Machine learning identified distinct patient groups in pediatric cancer treatment, revealing varied risks for heart dysfunction and high blood pressure during anthracycline chemotherapy for personalized care.

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Area of Science:

  • Cardio-oncology
  • Pediatric oncology
  • Machine learning in medicine

Background:

  • Anthracyclines are vital for pediatric cancer treatment but pose risks of cancer therapy-related cardiac dysfunction (CTRCD).
  • Standardized definitions by the International Cardio-Oncology Society (IC-OS) aim to improve CTRCD risk assessment precision.

Purpose of the Study:

  • To categorize distinct phenotypes in pediatric patients receiving anthracycline chemotherapy using unsupervised machine learning.
  • To identify patient subgroups with varying risks of CTRCD and hypertensive response.

Main Methods:

  • Retrospective analysis of pediatric cancer patients undergoing anthracycline chemotherapy.
  • Utilized unsupervised machine learning (principal component analysis, K-means clustering) to identify phenotypic clusters.
  • Analyzed phenogroups for associations with CTRCD and hypertensive response based on IC-OS definitions.

Main Results:

  • Four distinct phenogroups were identified among 187 pediatric patients.
  • Cluster 0 exhibited a significantly higher risk of moderate CTRCD (HR: 3.10).
  • Cluster 3 showed a protective effect against hypertensive response (HR: 0.30).
  • Longitudinal data revealed differences in global longitudinal strain and blood pressure among phenogroups.

Conclusions:

  • Unsupervised machine learning successfully identified distinct phenogroups in pediatric cancer patients treated with anthracyclines.
  • These phenogroups offer potential for personalized risk assessment and management of cardiotoxicity.