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Gao T, Ren H, He S, Liang D, Xu Y, Chen K, Wang Y, Zhu Y, Dong H, Xu Z, Chen W, Cheng W, Jing F, Tao X. Development of an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care: A study protocol. Front Cardiovasc Med 2023; 9:1091885. [PMID: 38106819 PMCID: PMC10722170 DOI: 10.3389/fcvm.2022.1091885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/12/2022] [Indexed: 12/19/2023] Open
Abstract
Background Cardiovascular disease (CVD) and cancer are the first and second causes of death in over 130 countries across the world. They are also among the top three causes in almost 180 countries worldwide. Cardiovascular complications are often noticed in cancer patients, with nearly 20% exhibiting cardiovascular comorbidities. Physical exercise may be helpful for cancer survivors and people living with cancer (PLWC), as it prevents relapses, CVD, and cardiotoxicity. Therefore, it is beneficial to recommend exercise as part of cardio-oncology preventive care. Objective With the progress of deep learning algorithms and the improvement of big data processing techniques, artificial intelligence (AI) has gradually become popular in the fields of medicine and healthcare. In the context of the shortage of medical resources in China, it is of great significance to adopt AI and machine learning methods for prescription recommendations. This study aims to develop an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care, and this paper presents the study protocol. Methods This will be a retrospective machine learning modeling cohort study with interventional methods (i.e., exercise prescription). We will recruit PLWC participants at baseline (from 1 January 2025 to 31 December 2026) and follow up over several years (from 1 January 2027 to 31 December 2028). Specifically, participants will be eligible if they are (1) PLWC in Stage I or cancer survivors from Stage I; (2) aged between 18 and 55 years; (3) interested in physical exercise for rehabilitation; (4) willing to wear smart sensors/watches; (5) assessed by doctors as suitable for exercise interventions. At baseline, clinical exercise physiologist certificated by the joint training program (from 1 January 2023 to 31 December 2024) of American College of Sports Medicine and Chinese Association of Sports Medicine will recommend exercise prescription to each participant. During the follow-up, effective exercise prescription will be determined by assessing the CVD status of the participants. Expected outcomes This study aims to develop not only an interpretable machine learning model to recommend exercise prescription but also an intelligent system of exercise prescription for precision cardio-oncology preventive care. Ethics This study is approved by Human Experimental Ethics Inspection of Guangzhou Sport University. Clinical trial registration http://www.chictr.org.cn, identifier ChiCTR2300077887.
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Affiliation(s)
- Tianyu Gao
- School of Physical Education, Jinan University, Guangzhou, China
| | - Hao Ren
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Faculty of Data Science, City University of Macau, Macao, Macao SAR, China
| | - Shan He
- Guangzhou Sport University, Guangzhou, China
| | - Deyi Liang
- Guangdong Women and Children Hospital, Guangzhou, China
| | - Yuming Xu
- Division of Physical Education, Guangdong University of Finance and Economics, Guangzhou, China
- School of Education, City University of Macau, Macao, Macao SAR, China
| | - Kecheng Chen
- School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yufan Wang
- Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxin Zhu
- Syns Institute of Educational Research, Hong Kong, Hong Kong SAR, China
| | - Heling Dong
- School of Physical Education, Jinan University, Guangzhou, China
| | - Zhongzhi Xu
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Weiming Chen
- Department of Health Medicine, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Weibin Cheng
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Fengshi Jing
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Faculty of Data Science, City University of Macau, Macao, Macao SAR, China
- UNC Project-China, UNC Global, School of Medicine, The University of North Carolina, Chapel Hill, NC, United States
| | - Xiaoyu Tao
- Zhuhai College of Science and Technology, Zhuhai, China
- ZCST Health and Medicine Industry Research Institute, Zhuhai, China
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Abstract
Cardiac rehabilitation is a complex intervention that seeks to improve the functional capacity, wellbeing and health-related quality of life of patients with heart disease. A substantive evidence base supports cardiac rehabilitation as a clinically effective and cost-effective intervention for patients with acute coronary syndrome or heart failure with reduced ejection fraction and after coronary revascularization. In this Review, we discuss the major contemporary challenges that face cardiac rehabilitation. Despite the strong recommendation in current clinical guidelines for the referral of these patient groups, global access to cardiac rehabilitation remains poor. The COVID-19 pandemic has contributed to a further reduction in access to cardiac rehabilitation. An increasing body of evidence supports home-based and technology-based models of cardiac rehabilitation as alternatives or adjuncts to traditional centre-based programmes, especially in low-income and middle-income countries, in which cardiac rehabilitation services are scarce, and scalable and affordable models are much needed. Future approaches to the delivery of cardiac rehabilitation need to align with the growing multimorbidity of an ageing population and cater to the needs of the increasing numbers of patients with cardiac disease who present with two or more chronic diseases. Future research priorities include strengthening the evidence base for cardiac rehabilitation in other indications, including heart failure with preserved ejection fraction, atrial fibrillation and congenital heart disease and after valve surgery or heart transplantation, and evaluation of the implementation of sustainable and affordable models of delivery that can improve access to cardiac rehabilitation in all income settings.
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Zimmerman A, Planek MIC, Chu C, Oyenusi O, Paner A, Reding K, Skeete J, Clark B, Okwuosa TM. Exercise, cancer and cardiovascular disease: what should clinicians advise? Cardiovasc Endocrinol Metab 2021; 10:62-71. [PMID: 34113793 PMCID: PMC8186519 DOI: 10.1097/xce.0000000000000228] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 07/05/2020] [Indexed: 12/18/2022]
Abstract
Cardiovascular disease is one of the leading causes of morbidity and mortality in persons with cancer. The elevated risk is thought to derive from the combination of cardiovascular risk factors and direct cardiotoxicity from cancer therapies. Exercise may be a potential strategy to counteract these toxicities and maintain cardiovascular reserve. In this article, we review the evidence for the potential cardioprotective effects of exercise training in cancer patients before, during, and following treatment. We also propose a patient-tailored approach for the development of targeted prescriptions based on individual exercise capacity and cardiovascular reserve.
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Affiliation(s)
| | | | - Catherine Chu
- Rush Medical College, Rush University Medical Center
| | | | - Agne Paner
- Division of Hematology/Oncology, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
| | - Kerryn Reding
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jamario Skeete
- Division of Cardiology, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Brian Clark
- Division of Cardiology, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Tochi M. Okwuosa
- Division of Cardiology, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA
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