1. Sex dependent risk factors for mortality after myocardial infarction
- Author
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Matteo Anselmino, Louise Pilote, Chiara Rafanelli, Kenneth E. Freedland, Frank Doyle, Robert M. Carney, Seyed Hamzeh Hosseini, Ronald C. Kessler, Edwin R. van den Heuvel, Kapil Parakh, Hiroshi Sato, Hanna M. van Loo, Sherry L. Grace, Robert A. Schoevers, Richard P. Steeds, Peter de Jonge, Johan Denollet, Annelieke M. Roest, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Perceptual and Cognitive Neuroscience (PCN), Life Course Epidemiology (LCE), Stochastic Operations Research, Statistics, 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, Chiara, Roest, Am, Sato, H, Steeds, Rp, Kessler, Rc, de Jonge, P., and Medical and Clinical Psychology
- Subjects
Male ,Interactions ,Global Health ,SDG 3 – Goede gezondheid en welzijn ,ACUTE CORONARY EVENTS ,Medicine ,Prospective Studies ,Myocardial infarction ,COORDINATE DESCENT ,Prospective cohort study ,Medicine(all) ,Smoking ,General Medicine ,Middle Aged ,All-cause mortality ,DEPRESSION ,REGULARIZATION PATHS ,Meta-analysis ,POPULATION TRENDS ,Regression Analysis ,HEART-FAILURE ,Female ,Sex ,Risk assessment ,GLOBAL REGISTRY ,Research Article ,medicine.medical_specialty ,Interaction ,Lower risk ,Sex Factors ,SDG 3 - Good Health and Well-being ,Internal medicine ,Diabetes Mellitus ,Humans ,Aged ,Proportional Hazards Models ,business.industry ,Proportional hazards model ,AGE INTERACTION ,medicine.disease ,Surgery ,BODY-MASS INDEX ,Risk factors ,SYSTOLIC DYSFUNCTION ,Myocardial infarction complications ,Risk factor ,business ,Prediction ,Body mass index - Abstract
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
- Published
- 2014
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