Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study

Author:

Kong Vungsovanreach1,Somakhamixay Oui2,Cho Wan-Sup2,Kang Gilwon3,Won Heesun4,Rah HyungChul5,Bang Heui Je6

Affiliation:

1. Department of Big Data, Chungbuk National University, Cheongju, South Korea

2. Department of Management Information Systems, Chungbuk National University, Cheongju, South Korea

3. Department of Health Informatics and Management, College of Medicine, Chungbuk National University, Cheongju, South Korea

4. Cybrebain Research Section, Electronics and Telecommunications Research Institute, Daejeon, South Korea

5. Research Institute of Veterinary Medicine, Chungbuk National University, Cheongju, South Korea

6. Department of Rehabilitation Medicine, College of Medicine, Chungbuk National University, Cheongju, South Korea

Abstract

Acute coronary syndrome (ACS) has been one of the most important issues in global public health. The high recurrence risk of patients with coronary heart disease (CHD) has led to the importance of post-discharge care and secondary prevention of CHD. Previous studies provided binary results of ACS recurrence risk; however, studies providing the recurrence risk of an individual patient are rare. In this study, we conducted a model which provides the recurrence risk probability for each patient, along with the binary result, with two datasets from the Korea Health Insurance Review and Assessment Service and Chungbuk National University Hospital. The total data of 6,535 patients who had been diagnosed with ACS were used to build a machine learning model by using logistic regression. Data including age, gender, procedure codes, procedure reason, prescription drug codes, and condition codes were used as the model predictors. The model performance showed 0.893, 0.894, 0.851, 0.869, and 0.921 for accuracy, precision, recall, F1-score, and AUC, respectively. Our model provides the ACS recurrence probability of each patient as a personalized ACS recurrence risk, which may help motivate the patient to reduce their own ACS recurrence risk. The model also shows that acute transmural myocardial infarction of an unspecified site, and other sites and acute transmural myocardial infarction of an unspecified site contributed most significantly to ACS recurrence with an odds ratio of 97.908 as a procedure reason code and with an odds ratio of 58.215 as a condition code, respectively.

Funder

Ministry of Trade, Industry and Energy

Korea Institute for Advancement of Technology

International Cooperative R&D Program

Basic Science Research Program, National Research Foundation of Korea, Ministry of Education

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference25 articles.

1. Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram;Al-Zaiti;Nature Communications,2020

2. Convergence study in development of severity adjustment method for death with acute myocardial infarction patients using machine learning;Baek;Journal of Digital Convergence,2019

3. Heart disease and stroke statistics—2019 update: a report from the American Heart Association;Benjamin;Circulation,2019

4. Diagnosis of acute coronary syndrome with a support vector machine;Berikol;Journal of Medical Systems,2016

5. ECG diagnosis and classification of acute coronary syndromes;Birnbaum;Annals of Noninvasive Electrocardiology,2014

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