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

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 re...

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Autores principales: Kong, Vungsovanreach, Somakhamixay, Oui, Cho, Wan-Sup, Kang, Gilwon, Won, Heesun, Rah, HyungChul, Bang, Heui Je
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9673763/
https://www.ncbi.nlm.nih.gov/pubmed/36405028
http://dx.doi.org/10.7717/peerj.14348
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author Kong, Vungsovanreach
Somakhamixay, Oui
Cho, Wan-Sup
Kang, Gilwon
Won, Heesun
Rah, HyungChul
Bang, Heui Je
author_facet Kong, Vungsovanreach
Somakhamixay, Oui
Cho, Wan-Sup
Kang, Gilwon
Won, Heesun
Rah, HyungChul
Bang, Heui Je
author_sort Kong, Vungsovanreach
collection PubMed
description 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.
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spelling pubmed-96737632022-11-19 Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study Kong, Vungsovanreach Somakhamixay, Oui Cho, Wan-Sup Kang, Gilwon Won, Heesun Rah, HyungChul Bang, Heui Je PeerJ Cardiology 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. PeerJ Inc. 2022-11-15 /pmc/articles/PMC9673763/ /pubmed/36405028 http://dx.doi.org/10.7717/peerj.14348 Text en ©2022 Kong et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Cardiology
Kong, Vungsovanreach
Somakhamixay, Oui
Cho, Wan-Sup
Kang, Gilwon
Won, Heesun
Rah, HyungChul
Bang, Heui Je
Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study
title Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study
title_full Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study
title_fullStr Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study
title_full_unstemmed Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study
title_short Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study
title_sort recurrence risk prediction of acute coronary syndrome per patient as a personalized acs recurrence risk: a retrospective study
topic Cardiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9673763/
https://www.ncbi.nlm.nih.gov/pubmed/36405028
http://dx.doi.org/10.7717/peerj.14348
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