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Zhang Y, Han Y, Gao P, Mo Y, Hao S, Huang J, Ye F, Li Z, Zheng L, Yao X, Li Z, Li X, Wang X, Huang CJ, Jin B, Zhang Y, Yang G, Alfreds ST, Kanov L, Sylvester KG, Widen E, Li L, Ling X. Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study. JMIR Med Inform 2021; 9:e23606. [PMID: 33595452 PMCID: PMC7929752 DOI: 10.2196/23606] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 12/10/2020] [Accepted: 01/11/2021] [Indexed: 12/18/2022] Open
Abstract
Background Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. Objective The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. Methods Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. Conclusions Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.
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Affiliation(s)
- Yaqi Zhang
- School of Electrical Power Engineering, South China University of Technology, Guangzhou, China.,Department of Surgery, Stanford University, Stanford, CA, United States
| | - Yongxia Han
- School of Electrical Power Engineering, South China University of Technology, Guangzhou, China.,Department of Surgery, Stanford University, Stanford, CA, United States
| | - Peng Gao
- Department of Surgery, Stanford University, Stanford, CA, United States.,College of Pharmacy, Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Yifu Mo
- School of Electrical Power Engineering, South China University of Technology, Guangzhou, China.,China Southern Power Grid Company Limited, Guangzhou, China
| | - Shiying Hao
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States.,Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States
| | - Jia Huang
- Department of Surgery, Stanford University, Stanford, CA, United States.,Department of Critical Care Medicine, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Fangfan Ye
- Department of Surgery, Stanford University, Stanford, CA, United States.,Department of Anesthesiology, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Zhen Li
- Department of Surgery, Stanford University, Stanford, CA, United States
| | - Le Zheng
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States.,Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States
| | - Xiaoming Yao
- Translational Medicine Laboratory, Queen Mary Hospital, Hong Kong University, Hong Kong, China
| | - Zhen Li
- Department of Surgery, Stanford University, Stanford, CA, United States.,School of Electrical Engineering, Southeast University, Nanjing, China
| | - Xiaodong Li
- Department of Surgery, Stanford University, Stanford, CA, United States.,School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Xiaofang Wang
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Chao-Jung Huang
- National Taiwan University-Stanford Joint Program Office of Artificial Intelligence in Biotechnology, Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare, Taipei, China
| | - Bo Jin
- HBI Solutions Inc, Palo Alto, CA, United States
| | - Yani Zhang
- Tianjin Yunjian Medical Laboratory Institute Co Ltd, Tianjing, China
| | | | | | - Laura Kanov
- HBI Solutions Inc, Palo Alto, CA, United States
| | - Karl G Sylvester
- Department of Surgery, Stanford University, Stanford, CA, United States
| | - Eric Widen
- HBI Solutions Inc, Palo Alto, CA, United States
| | - Licheng Li
- School of Electrical Power Engineering, South China University of Technology, Guangzhou, China
| | - Xuefeng Ling
- Department of Surgery, Stanford University, Stanford, CA, United States.,Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States
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