Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis

Hdl Handle:
http://hdl.handle.net/10147/338187
Title:
Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis
Authors:
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
Citation:
van Loo, H.M. et al., 2014. Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis. BMC Medicine, 12 (1) pp 242
Issue Date:
17-Dec-2014
URI:
http://dx.doi.org/10.1186/s12916-014-0242-y; http://hdl.handle.net/10147/338187
Abstract:
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 <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.
Language:
en
Keywords:
MORTALITY; CARDIOVASCULAR DISEASE
Local subject classification:
MYOCARDIAL INFARCTION; GENDER

Full metadata record

DC FieldValue Language
dc.contributor.authorvan Loo, Hanna Men_GB
dc.contributor.authorvan den Heuvel, Edwin Ren_GB
dc.contributor.authorSchoevers, Robert Aen_GB
dc.contributor.authorAnselmino, Matteoen_GB
dc.contributor.authorCarney, Robert Men_GB
dc.contributor.authorDenollet, Johanen_GB
dc.contributor.authorDoyle, Franken_GB
dc.contributor.authorFreedland, Kenneth Een_GB
dc.contributor.authorGrace, Sherry Len_GB
dc.contributor.authorHosseini, Seyed Hen_GB
dc.contributor.authorParakh, Kapilen_GB
dc.contributor.authorPilote, Louiseen_GB
dc.contributor.authorRafanelli, Chiaraen_GB
dc.contributor.authorRoest, Annelieke Men_GB
dc.contributor.authorSato, Hiroshien_GB
dc.contributor.authorSteeds, Richard Pen_GB
dc.contributor.authorKessler, Ronald Cen_GB
dc.contributor.authorde Jonge, Peteren_GB
dc.date.accessioned2015-01-13T12:17:12Z-
dc.date.available2015-01-13T12:17:12Z-
dc.date.issued2014-12-17-
dc.identifier.citationvan Loo, H.M. et al., 2014. Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis. BMC Medicine, 12 (1) pp 242en_GB
dc.identifier.urihttp://dx.doi.org/10.1186/s12916-014-0242-y-
dc.identifier.urihttp://hdl.handle.net/10147/338187-
dc.description.abstractAbstract 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.-
dc.language.isoenen
dc.subjectMORTALITYen_GB
dc.subjectCARDIOVASCULAR DISEASEen_GB
dc.subject.otherMYOCARDIAL INFARCTIONen_GB
dc.subject.otherGENDERen_GB
dc.titleSex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysisen_GB
dc.language.rfc3066en-
dc.rights.holderHanna M van Loo et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2015-01-12T20:04:45Z-
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