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Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis
BACKGROUND: Although a number of risk factors are known to predict mortality within the first years after myocardial infarction, little is known about interactions between risk factors, whereas these could contribute to accurate differentiation of patients with higher and lower risk for mortality. T...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4292997/ https://www.ncbi.nlm.nih.gov/pubmed/25515680 http://dx.doi.org/10.1186/s12916-014-0242-y |
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author | van Loo, Hanna M van den Heuvel, Edwin R Schoevers, Robert A Anselmino, Matteo Carney, Robert M Denollet, Johan Doyle, Frank Freedland, Kenneth E Grace, Sherry L Hosseini, Seyed H Parakh, Kapil Pilote, Louise Rafanelli, Chiara Roest, Annelieke M Sato, Hiroshi Steeds, Richard P Kessler, Ronald C de Jonge, Peter |
author_facet | van Loo, Hanna M van den Heuvel, Edwin R Schoevers, Robert A Anselmino, Matteo Carney, Robert M Denollet, Johan Doyle, Frank Freedland, Kenneth E Grace, Sherry L Hosseini, Seyed H Parakh, Kapil Pilote, Louise Rafanelli, Chiara Roest, Annelieke M Sato, Hiroshi Steeds, Richard P Kessler, Ronald C de Jonge, Peter |
author_sort | van Loo, Hanna M |
collection | PubMed |
description | BACKGROUND: Although a number of risk factors are known to predict mortality within the first years after myocardial infarction, little is known about interactions between risk factors, whereas these could contribute to accurate differentiation of patients with higher and lower risk for mortality. This study explored the effect of interactions of risk factors on all-cause mortality in patients with myocardial infarction based on individual patient data meta-analysis. METHODS: Prospective data for 10,512 patients hospitalized for myocardial infarction were derived from 16 observational studies (MINDMAPS). Baseline measures included a broad set of risk factors for mortality such as age, sex, heart failure, diabetes, depression, and smoking. All two-way and three-way interactions of these risk factors were included in Lasso regression analyses to predict time-to-event related all-cause mortality. The effect of selected interactions was investigated with multilevel Cox regression models. RESULTS: Lasso regression selected five two-way interactions, of which four included sex. The addition of these interactions to multilevel Cox models suggested differential risk patterns for males and females. Younger women (age <50) had a higher risk for all-cause mortality than men in the same age group (HR 0.7 vs. 0.4), while men had a higher risk than women if they had depression (HR 1.4 vs. 1.1) or a low left ventricular ejection fraction (HR 1.7 vs. 1.3). Predictive accuracy of the Cox model was better for men than for women (area under the curves: 0.770 vs. 0.754). CONCLUSIONS: Interactions of well-known risk factors for all-cause mortality after myocardial infarction suggested important sex differences. This study gives rise to a further exploration of prediction models to improve risk assessment for men and women after myocardial infarction. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-014-0242-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4292997 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42929972015-01-14 Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis van Loo, Hanna M van den Heuvel, Edwin R Schoevers, Robert A Anselmino, Matteo Carney, Robert M Denollet, Johan Doyle, Frank Freedland, Kenneth E Grace, Sherry L Hosseini, Seyed H Parakh, Kapil Pilote, Louise Rafanelli, Chiara Roest, Annelieke M Sato, Hiroshi Steeds, Richard P Kessler, Ronald C de Jonge, Peter BMC Med Research Article BACKGROUND: Although a number of risk factors are known to predict mortality within the first years after myocardial infarction, little is known about interactions between risk factors, whereas these could contribute to accurate differentiation of patients with higher and lower risk for mortality. This study explored the effect of interactions of risk factors on all-cause mortality in patients with myocardial infarction based on individual patient data meta-analysis. METHODS: Prospective data for 10,512 patients hospitalized for myocardial infarction were derived from 16 observational studies (MINDMAPS). Baseline measures included a broad set of risk factors for mortality such as age, sex, heart failure, diabetes, depression, and smoking. All two-way and three-way interactions of these risk factors were included in Lasso regression analyses to predict time-to-event related all-cause mortality. The effect of selected interactions was investigated with multilevel Cox regression models. RESULTS: Lasso regression selected five two-way interactions, of which four included sex. The addition of these interactions to multilevel Cox models suggested differential risk patterns for males and females. Younger women (age <50) had a higher risk for all-cause mortality than men in the same age group (HR 0.7 vs. 0.4), while men had a higher risk than women if they had depression (HR 1.4 vs. 1.1) or a low left ventricular ejection fraction (HR 1.7 vs. 1.3). Predictive accuracy of the Cox model was better for men than for women (area under the curves: 0.770 vs. 0.754). CONCLUSIONS: Interactions of well-known risk factors for all-cause mortality after myocardial infarction suggested important sex differences. This study gives rise to a further exploration of prediction models to improve risk assessment for men and women after myocardial infarction. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-014-0242-y) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-17 /pmc/articles/PMC4292997/ /pubmed/25515680 http://dx.doi.org/10.1186/s12916-014-0242-y Text en © van Loo et al.; licensee BioMed Central. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article van Loo, Hanna M van den Heuvel, Edwin R Schoevers, Robert A Anselmino, Matteo Carney, Robert M Denollet, Johan Doyle, Frank Freedland, Kenneth E Grace, Sherry L Hosseini, Seyed H Parakh, Kapil Pilote, Louise Rafanelli, Chiara Roest, Annelieke M Sato, Hiroshi Steeds, Richard P Kessler, Ronald C de Jonge, Peter Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis |
title | Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis |
title_full | Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis |
title_fullStr | Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis |
title_full_unstemmed | Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis |
title_short | Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis |
title_sort | sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4292997/ https://www.ncbi.nlm.nih.gov/pubmed/25515680 http://dx.doi.org/10.1186/s12916-014-0242-y |
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