Yen-Hung Lin1, Hui-Chun Huang2, Yi-Chung Chang3, Chen Lin4, Men-Tzung Lo5, Li-Yu Daisy Liu6, Pi-Ru Tsai7, Yih-Sharng Chen8, Wen-Je Ko9, Yi-Lwun Ho10,11, Ming-Fong Chen12, Chung-Kang Peng13,14, Timothy G Buchman15. 1. Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. austinr34@gmail.com. 2. Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. hchuangster@ntuh.gov.tw. 3. Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan. tako0215@hotmail.com. 4. Research Center for Adaptive Data Analysis, National Central University, No. 300, Jhongda Rd, Taoyuan County, 32001, Taiwan. dreamtheater.lin@gmail.com. 5. Research Center for Adaptive Data Analysis, National Central University, No. 300, Jhongda Rd, Taoyuan County, 32001, Taiwan. mzlo@ncu.edu.tw. 6. Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw. 7. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. pirutsai@yahoo.com.tw. 8. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. yschen1234@gmail.com. 9. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. kowj@ntu.edu.tw. 10. Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. ylho@ntu.edu.tw. 11. Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei, Taiwan. ylho@ntu.edu.tw. 12. Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. mfchen@ntuh.gov.tw. 13. Research Center for Adaptive Data Analysis, National Central University, No. 300, Jhongda Rd, Taoyuan County, 32001, Taiwan. ckpeng@comcast.net. 14. Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts, USA. ckpeng@comcast.net. 15. Department of Surgery, Emory University School of Medicine, Atlanta, Georgia, USA. tbuchma@emory.edu.
Abstract
INTRODUCTION: Extracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving ECLS. The primary outcome is death or urgent transplantation during the index admission. METHODS: Fifty-seven patients receiving ECLS less than 24 hours and 23 control subjects were enrolled. Digital 24-hour Holter electrocardiograms were recorded and three MSsE parameters (slope 5, Area 6-20, Area 6-40) associated with the multiscale correlation and complexity of heart beat fluctuation were calculated. RESULTS: Patients receiving ECLS had significantly lower value of slope 5, area 6 to 20, and area 6 to 40 than control subjects. During the follow-up period, 29 patients met primary outcome. Age, slope 5, Area 6 to 20, Area 6 to 40, acute physiology and chronic health evaluation II score, multiple organ dysfunction score (MODS), logistic organ dysfunction score (LODS), and myocardial infarction history were significantly associated with primary outcome. Slope 5 showed the greatest discriminatory power. In a net reclassification improvement model, slope 5 significantly improved the predictive power of LODS; Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in MODS. In an integrated discrimination improvement model, slope 5 added significantly to the prediction power of each clinical parameter. Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in sequential organ failure assessment. CONCLUSIONS: MSsE provides additional prognostic information in patients receiving ECLS.
INTRODUCTION: Extracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving ECLS. The primary outcome is death or urgent transplantation during the index admission. METHODS: Fifty-seven patients receiving ECLS less than 24 hours and 23 control subjects were enrolled. Digital 24-hour Holter electrocardiograms were recorded and three MSsE parameters (slope 5, Area 6-20, Area 6-40) associated with the multiscale correlation and complexity of heart beat fluctuation were calculated. RESULTS:Patients receiving ECLS had significantly lower value of slope 5, area 6 to 20, and area 6 to 40 than control subjects. During the follow-up period, 29 patients met primary outcome. Age, slope 5, Area 6 to 20, Area 6 to 40, acute physiology and chronic health evaluation II score, multiple organ dysfunction score (MODS), logistic organ dysfunction score (LODS), and myocardial infarction history were significantly associated with primary outcome. Slope 5 showed the greatest discriminatory power. In a net reclassification improvement model, slope 5 significantly improved the predictive power of LODS; Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in MODS. In an integrated discrimination improvement model, slope 5 added significantly to the prediction power of each clinical parameter. Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in sequential organ failure assessment. CONCLUSIONS: MSsE provides additional prognostic information in patients receiving ECLS.
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