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

BMC Medicine

Peter de Jonge^1,13  Ronald C Kessler²  Richard P Steeds⁶  Hiroshi Sato³  Annelieke M Roest^1,13  Chiara Rafanelli⁸  Louise Pilote^1,14  Kapil Parakh⁹  Seyed H Hosseini⁴  Sherry L Grace⁷  Kenneth E Freedland¹  Frank Doyle^1,11  Johan Denollet⁵  Robert M Carney¹  Matteo Anselmino^1,10  Robert A Schoevers^1,13  Edwin R van den Heuvel^1,12  Hanna M van Loo^1,13 

[1]Department of Psychiatry, Washington University School of Medicine, 4320 Forest Park Avenue, St. Louis 63108, Missouri, USA

[2]Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston 02115, Massachusetts, USA

[3]School of Human Welfare Studies, Kwansei Gakuin University, 1-1-155, Uegahara, Nishinomiya 662-8501, Hyogo, Japan

[4]Psychiatry and Behavioral Sciences Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Psychosomatic department, Imam hospital, Sari, Iran

[5]CoRPS-Center of Research on Psychology in Somatic diseases, Tilburg University, Warandelaan 2, Tilburg, 5000 LE, The Netherlands

[6]Department of Cardiology, Queen Elizabeth Hospital, Edgbaston, Birmingham B15 2TH, West Midlands, UK

[7]Faculty of Health, York University and University Health Network, 368 Norman Bethune, 4700 Keele Street, Toronto M3J 1P3, Canada

[8]Department of Psychology, University of Bologna, Viale Berti Pichat 5, Bologna, 40127, Italy

[9]John Hopkins School of Medicine, John Hopkins Bloomberg School of Public Health, John Hoplins Bayview Medical Center, 4940 Eastern Avenue, Baltimore 21224, Maryland, USA

[10]Division of Cardiology, Department of Medical Sciences, Città della Salute e della Scienza, University of Turin, C.so A.M. Dogliotti, 14, Turin, 10126, Italy

[11]Division of Population Health Sciences (Psychology), Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland

[12]Department of Mathematics and Computer Science, Eindhoven University of Technology, Den Dolech 2, Eindhoven, 5612 AZ, The Netherlands

[13]Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Department of Psychiatry, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, 9700 RB, The Netherlands

[14]Division of General Internal Medicine, McGill University, McGill University Health Centre, 687 Pine Avenue West, V Building, V2.17, Montreal H3A 1A1, Canada

关键词: Sex;    Risk factors;    Prediction;    Myocardial infarction;    Interactions;    All-cause mortality;   

Others  :  1118043
DOI  :  10.1186/s12916-014-0242-y

 received in 2014-09-12, accepted in 2014-11-21,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 van Loo et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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