Evidence map›Paper›PMID 39468669›Full record

ArticleCardio-oncology (London, England)2024

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

Xander Jacquemyn, Bhargava K Chinni, Benjamin T Barnes, Sruti Rao, Shelby Kutty, Cedric Manlhiot

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Article in Cardio-oncology (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xander JacquemynDepartment of Pediatrics, The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Johns Hopkins School of Medicine, Johns Hopkins University, Johns Hopkins Hospital, 600 N. Wolfe Street, 1389 Blalock, Baltimore, MD, 21287, USA.
Bhargava K ChinniDepartment of Pediatrics, The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Johns Hopkins School of Medicine, Johns Hopkins University, Johns Hopkins Hospital, 600 N. Wolfe Street, 1389 Blalock, Baltimore, MD, 21287, USA.
Benjamin T BarnesDepartment of Pediatrics, The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Johns Hopkins School of Medicine, Johns Hopkins University, Johns Hopkins Hospital, 600 N. Wolfe Street, 1389 Blalock, Baltimore, MD, 21287, USA.
Sruti RaoDepartment of Pediatrics, The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Johns Hopkins School of Medicine, Johns Hopkins University, Johns Hopkins Hospital, 600 N. Wolfe Street, 1389 Blalock, Baltimore, MD, 21287, USA.
Shelby KuttyDepartment of Pediatrics, The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Johns Hopkins School of Medicine, Johns Hopkins University, Johns Hopkins Hospital, 600 N. Wolfe Street, 1389 Blalock, Baltimore, MD, 21287, USA.
Cedric ManlhiotDepartment of Pediatrics, The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Johns Hopkins School of Medicine, Johns Hopkins University, Johns Hopkins Hospital, 600 N. Wolfe Street, 1389 Blalock, Baltimore, MD, 21287, USA. cmanlhi1@jhmi.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAnthracyclines 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.

objectivesCategorize distinct phenotypes among pediatric patients undergoing anthracycline chemotherapy using unsupervised machine learning.

methodsPediatric 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.

resultsA 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.

conclusionsUnsupervised machine learning identified distinct phenogroups among pediatric cancer patients undergoing anthracycline chemotherapy, offering potential for personalized risk assessment.

Indexed as

AnthracyclineCancer therapy–related cardiac dysfunctionCardiotoxicityEchocardiographyMachine learning

Identifiers

PMID39468669
PMCPMC11514752

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