Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis.
BackgroundAlthough 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.MethodsProspective 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.ResultsLasso 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
Funding
National Heart, Lung, and Blood Institute (NIH). Mazandaran University of Medical Sciences.Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology. Japan Society for the Promotion of Science. VIDI grant from the Netherlands Research Foundation. VICI grant from the Netherlands Research Foundation.
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The original article is available at www.biomedcentral.comPublished Citation
van Loo HM, van den Heuvel ER, Schoevers RA, Anselmino M, Carney RM, Denollet J, Doyle F, Freedland KE, Grace SL, Hosseini SH, Parakh K, Pilote L, Rafanelli C, Roest AM, Sato H, Steeds RP, Kessler RC, de Jonge P. Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis. BMC Medicine. 2014 Dec 17;12(1):242.Publication Date
2014-12-17External DOI
PubMed ID
25515680Department/Unit
- Health Psychology