Machine Learning–Based Risk Assessment for Cancer Therapy–Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients

Author:

Zhou Yadi1,Hou Yuan1,Hussain Muzna23,Brown Sherry‐Ann4ORCID,Budd Thomas5,Tang W. H. Wilson26ORCID,Abraham Jame5,Xu Bo2ORCID,Shah Chirag7,Moudgil Rohit2,Popovic Zoran2ORCID,Cho Leslie2,Kanj Mohamed2,Watson Chris3,Griffin Brian2,Chung Mina K.26ORCID,Kapadia Samir2ORCID,Svensson Lars8ORCID,Collier Patrick26ORCID,Cheng Feixiong159ORCID

Affiliation:

1. Genomic Medicine Institute Lerner Research InstituteCleveland Clinic Cleveland OH

2. Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH

3. School of Medicine Dentistry and Biomedical Sciences Wellcome‐Wolfson Institute of Experimental MedicineQueen’s University Belfast United Kingdom

4. Cardio‐Oncology Program Division of Cardiovascular Medicine Medical College of Wisconsin Milwaukee WI

5. Department of Hematology/Medical Oncology Taussig Cancer InstituteCleveland Clinic Cleveland OH

6. Department of Molecular Medicine Cleveland Clinic Lerner College of MedicineCase Western Reserve University Cleveland OH

7. Department of Radiation Oncology Taussig Cancer InstituteCleveland Clinic Cleveland OH

8. Department of Cardiovascular Surgery Cleveland Clinic Cleveland OH

9. Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland OH

Abstract

Background 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. Methods and Results 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. Conclusions 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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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