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Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction

(1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognosti...

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Autores principales: Xiao, Changhu, Guo, Yuan, Zhao, Kaixuan, Liu, Sha, He, Nongyue, He, Yi, Guo, Shuhong, Chen, Zhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880640/
https://www.ncbi.nlm.nih.gov/pubmed/35200709
http://dx.doi.org/10.3390/jcdd9020056
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author Xiao, Changhu
Guo, Yuan
Zhao, Kaixuan
Liu, Sha
He, Nongyue
He, Yi
Guo, Shuhong
Chen, Zhu
author_facet Xiao, Changhu
Guo, Yuan
Zhao, Kaixuan
Liu, Sha
He, Nongyue
He, Yi
Guo, Shuhong
Chen, Zhu
author_sort Xiao, Changhu
collection PubMed
description (1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognostic value of machine learning (ML) for the prediction of MACEs. (2) Methods: Five-hundred patients diagnosed with AMI and who had undergone successful percutaneous coronary intervention were included in the study. Logistic regression (LR) analysis was used to assess the relevance of MACEs and 24 selected clinical variables. Six ML models were developed with five-fold cross-validation in the training dataset and their ability to predict MACEs was compared to LR with the testing dataset. (3) Results: The MACE rate was calculated as 30.6% after a mean follow-up of 1.42 years. Killip classification (Killip IV vs. I class, odds ratio 4.386, 95% confidence interval 1.943–9.904), drug compliance (irregular vs. regular compliance, 3.06, 1.721–5.438), age (per year, 1.025, 1.006–1.044), and creatinine (1 µmol/L, 1.007, 1.002–1.012) and cholesterol levels (1 mmol/L, 0.708, 0.556–0.903) were independent predictors of MACEs. In the training dataset, the best performing model was the random forest (RDF) model with an area under the curve of (0.749, 0.644–0.853) and accuracy of (0.734, 0.647–0.820). In the testing dataset, the RDF showed the most significant survival difference (log-rank p = 0.017) in distinguishing patients with and without MACEs. (4) Conclusions: The RDF model has been identified as superior to other models for MACE prediction in this study. ML methods can be promising for improving optimal predictor selection and clinical outcomes in patients with AMI.
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spelling pubmed-88806402022-02-26 Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction Xiao, Changhu Guo, Yuan Zhao, Kaixuan Liu, Sha He, Nongyue He, Yi Guo, Shuhong Chen, Zhu J Cardiovasc Dev Dis Article (1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognostic value of machine learning (ML) for the prediction of MACEs. (2) Methods: Five-hundred patients diagnosed with AMI and who had undergone successful percutaneous coronary intervention were included in the study. Logistic regression (LR) analysis was used to assess the relevance of MACEs and 24 selected clinical variables. Six ML models were developed with five-fold cross-validation in the training dataset and their ability to predict MACEs was compared to LR with the testing dataset. (3) Results: The MACE rate was calculated as 30.6% after a mean follow-up of 1.42 years. Killip classification (Killip IV vs. I class, odds ratio 4.386, 95% confidence interval 1.943–9.904), drug compliance (irregular vs. regular compliance, 3.06, 1.721–5.438), age (per year, 1.025, 1.006–1.044), and creatinine (1 µmol/L, 1.007, 1.002–1.012) and cholesterol levels (1 mmol/L, 0.708, 0.556–0.903) were independent predictors of MACEs. In the training dataset, the best performing model was the random forest (RDF) model with an area under the curve of (0.749, 0.644–0.853) and accuracy of (0.734, 0.647–0.820). In the testing dataset, the RDF showed the most significant survival difference (log-rank p = 0.017) in distinguishing patients with and without MACEs. (4) Conclusions: The RDF model has been identified as superior to other models for MACE prediction in this study. ML methods can be promising for improving optimal predictor selection and clinical outcomes in patients with AMI. MDPI 2022-02-11 /pmc/articles/PMC8880640/ /pubmed/35200709 http://dx.doi.org/10.3390/jcdd9020056 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xiao, Changhu
Guo, Yuan
Zhao, Kaixuan
Liu, Sha
He, Nongyue
He, Yi
Guo, Shuhong
Chen, Zhu
Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction
title Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction
title_full Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction
title_fullStr Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction
title_full_unstemmed Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction
title_short Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction
title_sort prognostic value of machine learning in patients with acute myocardial infarction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880640/
https://www.ncbi.nlm.nih.gov/pubmed/35200709
http://dx.doi.org/10.3390/jcdd9020056
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