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

Authors :
Xander Jacquemyn
Bhargava K. Chinni
Benjamin T. Barnes
Sruti Rao
Shelby Kutty
Cedric Manlhiot
Source :
Cardio-Oncology, Vol 10, Iss 1, Pp 1-11 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

Abstract Background Anthracyclines are essential in pediatric cancer treatment, but patients are at risk cancer therapy-related cardiac dysfunction (CTRCD). Standardized definitions by the International Cardio-Oncology Society (IC-OS) aim to enhance precision in risk assessment. Objectives Categorize distinct phenotypes among pediatric patients undergoing anthracycline chemotherapy using unsupervised machine learning. Methods Pediatric cancer patients undergoing anthracycline chemotherapy at our institution were retrospectively included. Clinical and echocardiographic data at baseline, along with follow-up data, were collected from patient records. Unsupervised machine learning was performed, involving dimensionality reduction using principal component analysis and K-means clustering to identify different phenotypic clusters. Identified phenogroups were analyzed for associations with CTRCD, defined following contemporary IC-OS definitions, and hypertensive response. Results A total of 187 patients (63.1% male, median age 15.5 years [10.4–18.7]) were included and received anthracycline chemotherapy with a median treatment duration of 0.66 years [0.35–1.92]. Median follow-up duration was 2.78 years [1.31–4.21]. Four phenogroups were identified with following distribution: Cluster 0 (32.6%, n = 61), Cluster 1 (13.9%, n = 26), Cluster 2 (24.6%, n = 46), and Cluster 3 (28.9%, n = 54). Cluster 0 showed the highest risk of moderate CTRCD (HR: 3.10 [95% CI: 1.18–8.16], P = 0.022) compared to other clusters. Cluster 3 demonstrated a protective effect against hypertensive response (HR: 0.30 [95% CI: 0.13– 0.67], P = 0.003) after excluding baseline hypertensive patients. Longitudinal assessments revealed differences in global longitudinal strain and systolic blood pressure among phenogroups. Conclusions Unsupervised machine learning identified distinct phenogroups among pediatric cancer patients undergoing anthracycline chemotherapy, offering potential for personalized risk assessment.

Details

Language :
English
ISSN :
20573804
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Cardio-Oncology
Publication Type :
Academic Journal
Accession number :
edsdoj.5bef8372931f4332846735c7f9980dbd
Document Type :
article
Full Text :
https://doi.org/10.1186/s40959-024-00276-4