无监督机器学习识别出接受蒽环类药物治疗的儿科患者心脏并发症的不同表型。

IF 3.2 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Cardio-oncology Pub Date : 2024-10-28 DOI:10.1186/s40959-024-00276-4
Xander Jacquemyn, Bhargava K Chinni, Benjamin T Barnes, Sruti Rao, Shelby Kutty, Cedric Manlhiot
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引用次数: 0

摘要

背景:蒽环类药物在儿科癌症治疗中至关重要,但患者面临癌症治疗相关心功能障碍(CTRCD)的风险。国际心脏肿瘤学会(IC-OS)的标准化定义旨在提高风险评估的准确性:使用无监督机器学习对接受蒽环类化疗的儿科患者的不同表型进行分类:方法:回顾性纳入在我院接受蒽环类化疗的儿科癌症患者。从患者病历中收集基线时的临床和超声心动图数据以及随访数据。进行了无监督机器学习,包括使用主成分分析和 K-means 聚类进行降维,以识别不同的表型群。根据当代 IC-OS 的定义,对识别出的表型群与 CTRCD 和高血压反应的关联性进行了分析:共有 187 名患者(63.1% 为男性,中位年龄为 15.5 岁 [10.4-18.7])接受了蒽环类化疗,中位治疗时间为 0.66 年 [0.35-1.92]。中位随访时间为 2.78 年 [1.31-4.21]。确定了四个表型组,其分布情况如下:第 0 组(32.6%,n = 61)、第 1 组(13.9%,n = 26)、第 2 组(24.6%,n = 46)和第 3 组(28.9%,n = 54)。与其他群组相比,群组 0 患中度 CTRCD 的风险最高(HR:3.10 [95% CI:1.18-8.16],P = 0.022)。在排除基线高血压患者后,群组 3 对高血压反应具有保护作用(HR:0.30 [95% CI:0.13-0.67],P = 0.003)。纵向评估显示,不同表型组的总体纵向应变和收缩压存在差异:无监督机器学习在接受蒽环类化疗的儿科癌症患者中识别出了不同的表型组,为个性化风险评估提供了可能。
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Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines.

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.

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来源期刊
Cardio-oncology
Cardio-oncology Medicine-Cardiology and Cardiovascular Medicine
CiteScore
5.00
自引率
3.00%
发文量
17
审稿时长
7 weeks
期刊最新文献
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