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New model predicts in-hospital complications in myocardial infarction
INTRODUCTION AND OBJECTIVES: Ischemic cardiopathy constitutes the leading cause of death worldwide. Our aim was to evaluate the prognostic capacity of the leukoglycemic index as well as to create a predictive model of in-hospital complications in patients with ST elevation myocardial infarction. MAT...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Applied Systems srl
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482690/ https://www.ncbi.nlm.nih.gov/pubmed/36133173 http://dx.doi.org/10.15190/d.2022.1 |
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author | Martinez-Garcia, Geovedy Rodriguez-Ramos, Miguel Santos-Medina, Maikel Carrero-Vazquez, Annia Maria Chipi-Rodriguez, Yanitsy |
author_facet | Martinez-Garcia, Geovedy Rodriguez-Ramos, Miguel Santos-Medina, Maikel Carrero-Vazquez, Annia Maria Chipi-Rodriguez, Yanitsy |
author_sort | Martinez-Garcia, Geovedy |
collection | PubMed |
description | INTRODUCTION AND OBJECTIVES: Ischemic cardiopathy constitutes the leading cause of death worldwide. Our aim was to evaluate the prognostic capacity of the leukoglycemic index as well as to create a predictive model of in-hospital complications in patients with ST elevation myocardial infarction. MATERIALS AND METHODS: This was a multicentral and cohort study, which included patients inserted in the Cuban Registry of acute myocardial infarction. The study investigated 900 patients with a validation population represented by 233 external subjects. In order to define the performance of the leukoglycemic index were evaluated the discrimination with the statistical C and the calibration by Hosmer – Lemeshow test. A model of logistic binary regression was employed in order to define the predictive factors. RESULTS: Optimal cut point of the leukoglycemic index to predict in-hospital complications was 1188 (sensibility 60%; specificity 61.6%; area under the curve 0.623; p < 0.001). In-hospital complications were significantly higher in the group with the leukoglycemic index ≥ 1188; a higher value was significantly associated with a higher risk to develop an in-hospital complication [RR (IC 95%) = 2.4 (1.804–3.080); p<0.001]. The predictive model proposed is composed by the following factors: age ≥ 66 years, leukoglycemic index ≥ 1188, Killip-Kimball classification ≥ II and medical history of hypertension. This scale had a good discrimination in both, the training and the validation population. CONCLUSION: The leukoglycemic index possesses a low performance when used to assess the risks for in hospital complications in patients with ST elevation myocardial infarction. The new predictive model has a good performance, which can be applied to estimate risk of in-hospital complications. This model would be able to contribute to the health systems of developing countries without additional cost; it enables prediction of the patients having a higher risk of complications and a negative outcome during the hospitable admission. |
format | Online Article Text |
id | pubmed-9482690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Applied Systems srl |
record_format | MEDLINE/PubMed |
spelling | pubmed-94826902022-09-20 New model predicts in-hospital complications in myocardial infarction Martinez-Garcia, Geovedy Rodriguez-Ramos, Miguel Santos-Medina, Maikel Carrero-Vazquez, Annia Maria Chipi-Rodriguez, Yanitsy Discoveries (Craiova) Original Article INTRODUCTION AND OBJECTIVES: Ischemic cardiopathy constitutes the leading cause of death worldwide. Our aim was to evaluate the prognostic capacity of the leukoglycemic index as well as to create a predictive model of in-hospital complications in patients with ST elevation myocardial infarction. MATERIALS AND METHODS: This was a multicentral and cohort study, which included patients inserted in the Cuban Registry of acute myocardial infarction. The study investigated 900 patients with a validation population represented by 233 external subjects. In order to define the performance of the leukoglycemic index were evaluated the discrimination with the statistical C and the calibration by Hosmer – Lemeshow test. A model of logistic binary regression was employed in order to define the predictive factors. RESULTS: Optimal cut point of the leukoglycemic index to predict in-hospital complications was 1188 (sensibility 60%; specificity 61.6%; area under the curve 0.623; p < 0.001). In-hospital complications were significantly higher in the group with the leukoglycemic index ≥ 1188; a higher value was significantly associated with a higher risk to develop an in-hospital complication [RR (IC 95%) = 2.4 (1.804–3.080); p<0.001]. The predictive model proposed is composed by the following factors: age ≥ 66 years, leukoglycemic index ≥ 1188, Killip-Kimball classification ≥ II and medical history of hypertension. This scale had a good discrimination in both, the training and the validation population. CONCLUSION: The leukoglycemic index possesses a low performance when used to assess the risks for in hospital complications in patients with ST elevation myocardial infarction. The new predictive model has a good performance, which can be applied to estimate risk of in-hospital complications. This model would be able to contribute to the health systems of developing countries without additional cost; it enables prediction of the patients having a higher risk of complications and a negative outcome during the hospitable admission. Applied Systems srl 2022-03-04 /pmc/articles/PMC9482690/ /pubmed/36133173 http://dx.doi.org/10.15190/d.2022.1 Text en Copyright © 2022, Martinez-Garcia G et al., Applied Systems and Discoveries Journals. https://creativecommons.org/licenses/by/4.0/This article is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited and it is not used for commercial purposes. |
spellingShingle | Original Article Martinez-Garcia, Geovedy Rodriguez-Ramos, Miguel Santos-Medina, Maikel Carrero-Vazquez, Annia Maria Chipi-Rodriguez, Yanitsy New model predicts in-hospital complications in myocardial infarction |
title | New model predicts in-hospital complications in myocardial infarction |
title_full | New model predicts in-hospital complications in myocardial infarction |
title_fullStr | New model predicts in-hospital complications in myocardial infarction |
title_full_unstemmed | New model predicts in-hospital complications in myocardial infarction |
title_short | New model predicts in-hospital complications in myocardial infarction |
title_sort | new model predicts in-hospital complications in myocardial infarction |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482690/ https://www.ncbi.nlm.nih.gov/pubmed/36133173 http://dx.doi.org/10.15190/d.2022.1 |
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