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

Content Provider	Springer Nature : BioMed Central
Author	Jacquemyn, Xander Chinni, Bhargava K. Barnes, Benjamin T. Rao, Sruti Kutty, Shelby Manlhiot, Cedric
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.
Related Links	https://cardiooncologyjournal.biomedcentral.com/counter/pdf/10.1186/s40959-024-00276-4.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	20573804
DOI	10.1186/s40959-024-00276-4
Journal	Cardio-Oncology
Issue Number	1
Volume Number	10
Language	English
Publisher	BioMed Central
Publisher Date	2024-10-28
Access Restriction	Open
Subject Keyword	Oncology Cardiology Machine learning Anthracycline Cancer therapy–related cardiac dysfunction Cardiotoxicity Echocardiography
Content Type	Text
Resource Type	Article
Subject	Oncology Cardiology and Cardiovascular Medicine
Journal Impact Factor	3.2/2023

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