Cargando…
Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction
INTRODUCTION: Liver parameters are associated with cardiovascular disease risk and severity of stenosis. It is unclear whether liver parameters could predict the long-term outcome of patients after acute myocardial infarction (AMI). We performed an unbiased analysis of the predictive value of serum...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Termedia Publishing House
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6963137/ https://www.ncbi.nlm.nih.gov/pubmed/32051708 http://dx.doi.org/10.5114/aoms.2018.75678 |
_version_ | 1783488216948539392 |
---|---|
author | Baars, Theodor Sowa, Jan-Peter Neumann, Ursula Hendricks, Stefanie Jinawy, Mona Kälsch, Julia Gerken, Guido Rassaf, Tienush Heider, Dominik Canbay, Ali |
author_facet | Baars, Theodor Sowa, Jan-Peter Neumann, Ursula Hendricks, Stefanie Jinawy, Mona Kälsch, Julia Gerken, Guido Rassaf, Tienush Heider, Dominik Canbay, Ali |
author_sort | Baars, Theodor |
collection | PubMed |
description | INTRODUCTION: Liver parameters are associated with cardiovascular disease risk and severity of stenosis. It is unclear whether liver parameters could predict the long-term outcome of patients after acute myocardial infarction (AMI). We performed an unbiased analysis of the predictive value of serum parameters for long-term prognosis after AMI. MATERIAL AND METHODS: In a retrospective, observational, single-center, cohort study, 569 patients after AMI were enrolled and followed up until 6 years for major adverse cardiovascular events, including cardiac death. Patients were classified into non-survivors (n = 156) and survivors (n = 413). Demographic and laboratory data were analyzed using ensemble feature selection (EFS) and logistic regression. Correlations were performed for serum parameters. RESULTS: Age (73; 64; p < 0.01), alanine aminotransferase (ALT; 93 U/l; 40 U/l; p < 0.01), aspartate aminotransferase (AST; 162 U/l; 66 U/l; p < 0.01), C-reactive protein (CRP; 4.7 U/l; 1.6 U/l; p < 0.01), creatinine (1.6; 1.3; p < 0.01), γ-glutamyltransferase (GGT; 71 U/l; 46 U/l; p < 0.01), urea (29.5; 20.5; p < 0.01), estimated glomerular filtration rate (eGFR; 49.6; 61.4; p < 0.01), troponin (13.3; 7.6; p < 0.01), myoglobin (639; 302; p < 0.01), and cardiovascular risk factors (hypercholesterolemia p < 0.02, family history p < 0.01, and smoking p < 0.01) differed significantly between non-survivors and survivors. Age, AST, CRP, eGFR, myoglobin, sodium, urea, creatinine, and troponin correlated significantly with death (r = –0.29; 0.14; 0.31; –0.27; 0.20; –0.13; 0.33; 0.24; 0.12). A prediction model was built including age, CRP, eGFR, myoglobin, and urea, achieving an AUROC of 77.6% to predict long-term survival after AMI. CONCLUSIONS: Non-invasive parameters, including liver and renal markers, can predict long-term outcome of patients after AMI. |
format | Online Article Text |
id | pubmed-6963137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Termedia Publishing House |
record_format | MEDLINE/PubMed |
spelling | pubmed-69631372020-02-12 Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction Baars, Theodor Sowa, Jan-Peter Neumann, Ursula Hendricks, Stefanie Jinawy, Mona Kälsch, Julia Gerken, Guido Rassaf, Tienush Heider, Dominik Canbay, Ali Arch Med Sci Clinical Research INTRODUCTION: Liver parameters are associated with cardiovascular disease risk and severity of stenosis. It is unclear whether liver parameters could predict the long-term outcome of patients after acute myocardial infarction (AMI). We performed an unbiased analysis of the predictive value of serum parameters for long-term prognosis after AMI. MATERIAL AND METHODS: In a retrospective, observational, single-center, cohort study, 569 patients after AMI were enrolled and followed up until 6 years for major adverse cardiovascular events, including cardiac death. Patients were classified into non-survivors (n = 156) and survivors (n = 413). Demographic and laboratory data were analyzed using ensemble feature selection (EFS) and logistic regression. Correlations were performed for serum parameters. RESULTS: Age (73; 64; p < 0.01), alanine aminotransferase (ALT; 93 U/l; 40 U/l; p < 0.01), aspartate aminotransferase (AST; 162 U/l; 66 U/l; p < 0.01), C-reactive protein (CRP; 4.7 U/l; 1.6 U/l; p < 0.01), creatinine (1.6; 1.3; p < 0.01), γ-glutamyltransferase (GGT; 71 U/l; 46 U/l; p < 0.01), urea (29.5; 20.5; p < 0.01), estimated glomerular filtration rate (eGFR; 49.6; 61.4; p < 0.01), troponin (13.3; 7.6; p < 0.01), myoglobin (639; 302; p < 0.01), and cardiovascular risk factors (hypercholesterolemia p < 0.02, family history p < 0.01, and smoking p < 0.01) differed significantly between non-survivors and survivors. Age, AST, CRP, eGFR, myoglobin, sodium, urea, creatinine, and troponin correlated significantly with death (r = –0.29; 0.14; 0.31; –0.27; 0.20; –0.13; 0.33; 0.24; 0.12). A prediction model was built including age, CRP, eGFR, myoglobin, and urea, achieving an AUROC of 77.6% to predict long-term survival after AMI. CONCLUSIONS: Non-invasive parameters, including liver and renal markers, can predict long-term outcome of patients after AMI. Termedia Publishing House 2018-05-15 /pmc/articles/PMC6963137/ /pubmed/32051708 http://dx.doi.org/10.5114/aoms.2018.75678 Text en Copyright: © 2018 Termedia & Banach http://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license. |
spellingShingle | Clinical Research Baars, Theodor Sowa, Jan-Peter Neumann, Ursula Hendricks, Stefanie Jinawy, Mona Kälsch, Julia Gerken, Guido Rassaf, Tienush Heider, Dominik Canbay, Ali Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction |
title | Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction |
title_full | Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction |
title_fullStr | Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction |
title_full_unstemmed | Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction |
title_short | Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction |
title_sort | liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction |
topic | Clinical Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6963137/ https://www.ncbi.nlm.nih.gov/pubmed/32051708 http://dx.doi.org/10.5114/aoms.2018.75678 |
work_keys_str_mv | AT baarstheodor liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT sowajanpeter liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT neumannursula liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT hendricksstefanie liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT jinawymona liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT kalschjulia liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT gerkenguido liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT rassaftienush liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT heiderdominik liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction AT canbayali liverparametersaspartofanoninvasivemodelforpredictionofallcausemortalityaftermyocardialinfarction |