Christien Kh Li1, Zhongzhi Xu2, Jeffery Ho3, Ishan Lakhani4, Ying Zhi Liu3, George Bazoukis5, Tong Liu6, Wing Tak Wong7, Shuk Han Cheng8, Matthew Tv Chan3, Lin Zhang9, Tony Gin3, Martin Cs Wong10, Ian Chi Kei Wong11, William Ka Kei Wu12, Qingpeng Zhang13, Gary Tse14. 1. Faculty of Medicine, Newcastle University, United Kingdom. 2. School of Data Science, City University of Hong Kong, Hong Kong, PR China. 3. Department of Anesthesia and Intensive Care, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, PR China. 4. Laboratory of Cardiovascular Physiology, Li Ka Shing Institute of Health Sciences, Hong Kong, PR China. 5. Second Department of Cardiology, Laboratory of Cardiac Electrophysiology, Evangelismos General Hospital of Athens, Athens, Greece. 6. Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Department of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211, PR China. 7. School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, PR China. 8. Department of Biomedical Sciences, College of Veterinary Medicine and Life Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, PR China. 9. Department of Anesthesia and Intensive Care, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, PR China; State Key Laboratory of Digestive Disease, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences, And Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong, PR China. 10. The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, PR China. 11. Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, PR China; University College London, United Kingdom. 12. Department of Anesthesia and Intensive Care, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, PR China; State Key Laboratory of Digestive Disease, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences, And Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong, PR China. Electronic address: wukakei@cuhk.edu.hk. 13. School of Data Science, City University of Hong Kong, Hong Kong, PR China. Electronic address: qingpeng.zhang@cityu.edu.hk. 14. Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Department of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211, PR China; School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, PR China. Electronic address: gary.tse@doctors.org.uk.
Abstract
BACKGROUND AND AIMS: Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to improve short-term mortality classification after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort. METHODS: Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n = 1747) and an independent tertiary care center (validation cohort, n = 1276), with index acute myocardial infarction between January 2013 and December 2017, as confirmed by principal diagnosis and laboratory findings, were identified retrospectively. RESULTS: Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant for 90-day mortality (Hosmer- Lemeshow test, p = 0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic = 0.63). Judicious application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81. CONCLUSIONS: The NPAC score comprises four items from routine laboratory parameters to basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gatekeeper to facilitate clinical decision-making.
BACKGROUND AND AIMS: Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to improve short-term mortality classification after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort. METHODS: Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n = 1747) and an independent tertiary care center (validation cohort, n = 1276), with index acute myocardial infarction between January 2013 and December 2017, as confirmed by principal diagnosis and laboratory findings, were identified retrospectively. RESULTS: Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant for 90-day mortality (Hosmer- Lemeshow test, p = 0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic = 0.63). Judicious application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81. CONCLUSIONS: The NPAC score comprises four items from routine laboratory parameters to basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gatekeeper to facilitate clinical decision-making.
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Authors: Chengsheng Ju; Jiandong Zhou; Sharen Lee; Martin Sebastian Tan; Tong Liu; George Bazoukis; Kamalan Jeevaratnam; Esther W Y Chan; Ian Chi Kei Wong; Li Wei; Qingpeng Zhang; Gary Tse Journal: ESC Heart Fail Date: 2021-06-03