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  • Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China

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Medical management
Protocol
Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China
  1. Fanqi Meng1,2,
  2. Zhihua Zhang1,3,
  3. Xiaofeng Hou1,
  4. Zhiyong Qian1,
  5. Yao Wang1,
  6. Yanhong Chen4,
  7. Yilian Wang5,
  8. Ye Zhou6,
  9. Zhen Chen7,
  10. Xiwen Zhang8,
  11. Jing Yang8,
  12. Jinlong Zhang9,
  13. Jianghong Guo10,
  14. Kebei Li11,
  15. Lu Chen12,
  16. Ruijuan Zhuang13,
  17. Hai Jiang14,
  18. Weihua Zhou15,
  19. Shaowen Tang16,
  20. Yongyue Wei17,
  21. Jiangang Zou1,18
  1. 1 Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  2. 2 Department of Cardiology, Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, Fujian, China
  3. 3 Department of Cardiology, Jiangning Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
  4. 4 Department of Cardiology, Wuhan Asia Heart Hospital, Wuhan, Hubei, China
  5. 5 Department of Cardiology, The Second People’s Hospital of Lianyungang, Lianyungang, Jiangsu, China
  6. 6 Department of Cardiology, The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China
  7. 7 Department of Cardiology, Taixing People’s Hospital, Taixing, Jiangsu, China
  8. 8 Department of Cardiology, The First People’s Hospital of Huaian, Huaian, Jiangsu, China
  9. 9 Department of Cardiology, The First People’s Hospital of Yancheng, Yancheng, Jiangsu, China
  10. 10 Department of Cardiology, Rugao People’s Hospital, Rugao, Jiangsu, China
  11. 11 Department of Cardiology, The First People’s Hospital of Zhangjiagang, Zhangjiagang, Jiangsu, China
  12. 12 Department of Cardiology, The Third People’s Hospital of Suzhou, Suzhou, Jiangsu, China
  13. 13 Department of Cardiology, The Third People’s Hospital of Wuxi, Wuxi, Jiangsu, China
  14. 14 Department of Cardiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  15. 15 School of Computing, University of Southern Mississippi, Hattiesburg, Mississippi, USA
  16. 16 Department of Epidemiology, Nanjing Medical University, Nanjing, Jiangsu, China
  17. 17 Department of Biostatistics, Nanjing Medical University, Nanjing, Jiangsu, China
  18. 18 Key Laboratory of Targeted Intervention of Cardiovascular Disease, Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
  1. Correspondence to Dr Jiangang Zou; jgzou{at}njmu.edu.cn

Abstract

Introduction Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF.

Methods and analysis We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study.

Ethics and dissemination The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences.

Trial registration number ChiCTR-POC-17011842; Pre-results.

  • Heart Failure
  • Sudden Cardiac Death
  • Machine Learning
  • Risk Model

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2018-023724

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    Footnotes

    • Contributors JGZ and FM conceived and designed the study. ZZ, XH, ZQ, YW, YC, YLW, YZ, ZC, XZ, JY, JLZ, JG, KL, LC, RZ and HJ participated in different phases of the protocol design. WZ provided expertise in data processing and machine learning. ST and YW provided their expertise for traditional statistical analysis. JGZ obtained funding. FM drafted the final manuscript. All authors have read the manuscript and provided feedback. JGZ approved the final version of the manuscript before submission. FM took responsibility for the submission process.

    • Funding This study was sponsored partly by the grant of clinical frontier technology from Jiangsu Science and Technology Agency (BE2016764).

    • Competing interests None declared.

    • Ethics approval The study protocol has been approved by the Ethics Committee of The First Affiliated Hospital of Nanjing Medical University (2017-SR-06).

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Patient consent for publication Obtained.