Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.
Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.
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DOI：
10.1161/jaha.120.019628
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发表时间：
2020-12
影响因子：
5.4
通讯作者：
Cheng F
中科院分区：
文献类型：
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作者：
Zhou Y;Hou Y;Hussain M;Brown SA;Budd T;Tang WHW;Abraham J;Xu B;Shah C;Moudgil R;Popovic Z;Cho L;Kanj M;Watson C;Griffin B;Chung MK;Kapadia S;Svensson L;Collier P;Cheng F
对癌症治疗的心血管毒性的认识日益提高，导致了新兴的心脏肿瘤学领域，该领域的中心是在癌症治疗之前，期间或之后预防，检测和治疗心功能不全患者。癌症治疗相关心功能不全（CTRCD）的早期检测和预防在精准心脏肿瘤学中发挥着重要作用。这项回顾性研究包括1997年至2018年期间的4309名癌症患者，其实验室检查和心血管超声心动图变量从克利夫兰诊所机构电子病历数据库（Epic Systems）中收集。在这些患者中，1560例（36%）被诊断为至少1种类型的CTRCD，838例（19%）在癌症治疗后（新发）发生CTRCD。我们假设可以实施机器学习算法来根据临床相关变量预测癌症患者的CTRCD。针对6种类型的心血管结局（包括冠状动脉疾病）训练和评估分类模型（受试者工作特征曲线下面积[AUROC]，0.821; 95% CI，0.815-0.826），房颤（AUROC，0.787; 95% CI，0.782-0.792），心力衰竭（AUROC，0.882; 95% CI，0.878-0.887），卒中（AUROC，0.660; 95% CI，0.650-0.670）、心肌梗死（AUROC，0.807; 95% CI，0.799-0.816）和新发CTRCD（AUROC，0.802; 95% CI，0.797-0.807）。使用时间分割数据进一步证实了模型的可推广性。模型检查显示了几个与CTRCD显著相关的临床相关变量，包括年龄、高血压、血糖水平、左心室射血分数、肌酐和天冬氨酸转氨酶水平。这项研究表明，机器学习方法通过利用来自医疗保健系统的大规模纵向患者数据，为肿瘤患者的心脏风险分层提供了强大的工具。
The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio‐oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer treatment. Early detection and prevention of cancer therapy–related cardiac dysfunction (CTRCD) play important roles in precision cardio‐oncology. This retrospective study included 4309 cancer patients between 1997 and 2018 whose laboratory tests and cardiovascular echocardiographic variables were collected from the Cleveland Clinic institutional electronic medical record database (Epic Systems). Among these patients, 1560 (36%) were diagnosed with at least 1 type of CTRCD, and 838 (19%) developed CTRCD after cancer therapy (de novo). We posited that machine learning algorithms can be implemented to predict CTRCDs in cancer patients according to clinically relevant variables. Classification models were trained and evaluated for 6 types of cardiovascular outcomes, including coronary artery disease (area under the receiver operating characteristic curve [AUROC], 0.821; 95% CI, 0.815–0.826), atrial fibrillation (AUROC, 0.787; 95% CI, 0.782–0.792), heart failure (AUROC, 0.882; 95% CI, 0.878–0.887), stroke (AUROC, 0.660; 95% CI, 0.650–0.670), myocardial infarction (AUROC, 0.807; 95% CI, 0.799–0.816), and de novo CTRCD (AUROC, 0.802; 95% CI, 0.797–0.807). Model generalizability was further confirmed using time‐split data. Model inspection revealed several clinically relevant variables significantly associated with CTRCDs, including age, hypertension, glucose levels, left ventricular ejection fraction, creatinine, and aspartate aminotransferase levels. This study suggests that machine learning approaches offer powerful tools for cardiac risk stratification in oncology patients by utilizing large‐scale, longitudinal patient data from healthcare systems.