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Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy

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Abstract

Cardiotoxicity is a severe side effect for colorectal cancer (CRC) patients undergoing fluoropyrimidine-based chemotherapy. To develop and compare machine learning algorithms to predict cardiotoxicity risk among nationally representative CRC patients receiving fluoropyrimidine, CRC Patients with at least one claim of fluoropyrimidine after their cancer diagnosis were included. The outcome was the 30-day cardiotoxicity from the first day of starting fluoropyrimidine. The machine learning models including extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) were developed using 2006–2011 SEER-Medicare data, and model performances were evaluated using 2012–2014 data. Precision, F1 score, and area under the receiver operating characteristics curve (AUC) were measured to evaluate model performances. Feature importance plots were obtained to quantify the predictor importance. Among 36,030 CRC patients, 18.74% of them developed cardiotoxicity within 30 days since the first fluoropyrimidine. The XGBoost approach had better prediction performance with higher precision (0.619) and F1 score (0.406) in predicting the 30-day cardiotoxicity, compared to the RF (precision, 0.607 and F1 score, 0.395) and LR (precision, 0.610 and F1 score, 0.398). According to the DeLong’s test for AUC difference, the XGBoost significantly outperformed the RF and LR (XGBoost, 0.816 vs. RF, 0.804, P < 0.001; XGBoost vs. LR, 0.812, P = 0.003, respectively). Feature importance plots identified pre-existing cardiac conditions, surgery, older age as top significant risk factors for cardiotoxicity events among CRC patients after receiving fluoropyrimidine. In summary, the developed machine learning models can accurately predict the occurrence of 30-day cardiotoxicity among CRC patients receiving fluoropyrimidine-based chemotherapy.

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Data Availability

For SEER-Medicare data, data access is available to investigators for research purposes and is required to obtain approval from NCI.

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Acknowledgements

This study was supported by the PhRMA Foundation Predoctoral Fellowship awarded to Dr. Chao Li. This study used the linked SEER-Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the National Cancer Institute (NCI); the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database.

Funding

This study was supported by the PhRMA Foundation Predoctoral Fellowship awarded to Dr. Chao Li.

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Authors and Affiliations

  1. Department of Health Outcomes Research and Policy, Auburn University, Auburn University Harrison School of Pharmacy, 4306D Walker Building, Auburn, AL, 36849-5506, USA

    Chao Li, Chiahung Chou, Surachat Ngorsuraches & Jingjing Qian

  2. Department of Biostatistics and Health Data Science, Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, IN, USA

    Li Chen

  3. Department of Medical Research, China Medical University Hospital, Taichung City, Taiwan

    Chiahung Chou

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  1. Chao Li
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  2. Li Chen
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Contributions

CL: conceptualization, methodology, funding acquisition, investigation, formal analysis, project administration, writing-original draft, and writing-review and editing. CC: data curation, supervision, and writing-review and editing. SN: supervision, and writing-review and editing. LC: Supervision, and writing-review and editing. JQ: conceptualization, funding acquisition, data curation, supervision, project administration, writing-original draft, and writing-review and editing.

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Correspondence to Jingjing Qian.

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The study was approved by the Auburn University institutional review board (IRB).

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Li, C., Chen, L., Chou, C. et al. Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy. Cardiovasc Toxicol 22, 130–140 (2022). https://doi.org/10.1007/s12012-021-09708-4

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