Background
About 8% of patients die within the first months after myocardial infarction, whereas approximately 30% survive more than 20 years [
1],[
2]. Several risk assessment instruments have been developed to quantify the risk of mortality in individual patients, such as the Global Registry of Acute Coronary Events and Thrombolysis in Myocardial Infarction risk scores [
3]-[
5]. These instruments are based on a set of well-known risk factors for mortality such as age, heart failure, and comorbidity. Unfortunately, interactions of these risk factors are rarely investigated optimally, while these could be of relevance in accurately differentiating the high risk from the low risk patients.
Some interactions of this type have been identified, such as between sex and age and between sex and left ventricular ejection fraction (LVEF), in predicting mortality after myocardial infarction [
2],[
6]-[
8]. These interactions suggested that mortality after myocardial infarction is higher in young women than in young men, while poor LVEF was associated with an increased risk of death, especially in men. However, previous studies have only investigated interactions sporadically and did not perform systematic searches through all or many interactions in a sufficiently large dataset to estimate interaction effects reliably. To discover currently unknown interactions of interest, we systematically investigated a large set of interactions that potentially influence prediction of all-cause mortality in patients with myocardial infarction with a novel combination of statistical learning methods [
9],[
10].
Discussion
With a systematic data-driven search through all possible two-way and three-way interactions of risk factors for all-cause mortality after myocardial infarction, we found that some risk factors act differently in men and women. A high depression score was associated with increased mortality risk in men, but not in women. In addition, for women, a younger age was less protective than for men; LVEF <40% was more strongly predicting mortality in men than in women. Another main finding was that, in patients without heart failure, beta-blocker use was more protective than expected.
These results are limited in several ways. First, the data came from studies carried out between 1985 and 2006. As the group of patients and treatment of myocardial infarction are changing over time (more older patients, more female patients, and more comorbidity) [
1],[
25],[
26], we should be cautious in generalizing the results to current patients with myocardial infarction. Indeed, replication is needed with newer data. Second, we could not include all relevant predictors, such as blood pressure, heart rate, waist circumference, kidney function, electrocardiogram findings, or educational attainment, marital status, and socioeconomic status, in our analyses as these data were not available in all studies [
3]-[
5],[
27],[
28]. Inclusion of such measures could have led to more precise risk assessment and possibly lead to other interaction patterns.
Within the context of these limitations, this is the first study performing a systematic search through a variety of interactions of risk factors for all-cause mortality after myocardial infarction using a large multi-study international sample. We applied statistical learning techniques to prevent overfitting, and tested the prediction of the identified model in an independent validation sample. Furthermore, selected interactions were clinically relevant as they indicated the importance of sex in differentiating patients with high and low risk for all-cause mortality after myocardial infarction.
Some interactions that were identified were congruent with previous studies, while others represented novel associations. Certain interactions, including sex, have been reported before such as the interaction between sex and age indicating that young women are at higher risk of all-cause mortality than young men [
6],[
7],[
29]. Likewise, the interaction between sex and LVEF – suggesting that a low LVEF is a more important risk factor for men than for women – was consistent with findings in patients with myocardial infarction [
8] and heart failure [
30]. Two studies that focused on the interaction between sex and depression did not find substantial sex differences with regard to depression as opposed to our study, but the former studies included a relatively low number of women (n = 283 and n = 155, respectively) and used major cardiac events or cardiac mortality as outcome measures rather than all-cause mortality [
8],[
31]. Finally, it has been previously shown that beta-blocker use is more protective after myocardial infarction in the absence of clinical signs of heart failure (Killip class I) than in the presence of these signs (Killip classes II–III) [
32], which is in agreement with the findings in this study.
Our study demonstrates the potential importance of interactions in risk assessment in medicine. If interaction effects are not taken into account, statistical models will return an average estimate of the effect of risk factors in the entire patient population (e.g., depression increases risk of all-cause mortality). Instead, interactions allow for possible differences within the patient population and show that a risk factor might have a different effect in the presence of another risk factor (e.g., depression increases risk of all-cause mortality in men but not in women). In our study, interactions repeatedly suggested sex differences. Although baseline differences between men and women with myocardial infarction are declining, there are still substantial dissimilarities. Men have a higher risk of myocardial infarction, but women have a higher risk of death following myocardial infarction. Women presenting with myocardial infarction are generally older, have more comorbidities such as diabetes, hypertension, and heart failure, increasing the risk of all-cause mortality as compared to men [
2],[
33]-[
35]. All these differences could explain why different risk factors might differentially predict all-cause mortality for men and women.
The results reported here suggest three broad classes of extensions in further research. First, follow-up studies could focus on additional interactions of interest in order to obtain more specific risk assessment for subgroups of patients with myocardial infarction. The investigated set of interactions in this study was not comprehensive. Moreover, as Lasso tends to leave out correlated interactions, there could be interactions of interest that we did not identify in this study such as diabetes and depression [
36], smoking and sex [
8], and LVEF and Killip class [
37]. In addition, it would be worth looking at interactions including other well-known risk factors of all-cause mortality after myocardial infarction such as cardiac arrest at admission, family history, and anxiety disorders [
5],[
38],[
39]. Second, more research is needed to identify mechanisms underlying the identified interactions. For instance, why is depression more strongly related to all-cause mortality in men than in women? A study in one of the datasets included in MINDMAPS found that part of this interaction effect can be explained by the fact that men with depression are more likely to have a poor LVEF than women with depression [
40]. Depression reflected more severe heart disease in men but not in women, which partly explains why depressed men are more at risk for all-cause mortality than depressed women. Other confounders could underlie the remaining interaction effect between sex and depression found in this study after controlling for severity of heart disease. Thus, the interpretation of interactions should be done in the context of possible confounders. The finding that smoking did not have a significant main effect in this study should also be seen in this light. In addition to a broad set of important risk factors, such as history of myocardial infarction, heart failure, age, and comorbidity, smoking did not significantly predict all-cause mortality risk, but this does not imply that smoking on itself does not increase risk of all-cause mortality after myocardial infarction.
Finally, the interactions including sex lead to doubt on the performance of general risk assessment instruments for both sexes: are they equally accurate for men and women? Future studies could explore if different prediction algorithms for men and women would increase prediction accuracy, and if this benefit would outweigh the complexity for clinical practice of working with two different instruments instead of one.
Acknowledgements
We thank Deirdre A. Lane, PhD, University of Birmingham, Centre for Cardiovascular Sciences, and Catharina Welin, RN, PhD, Institute of Health and Care Sciences, Sahlengrenska Academy, University of Gothenburg, for providing data for this individual patient data meta-analysis for the MINDMAPS collaboration. We thank Klaas J. Wardenaar, PhD, ICPE, Department of Psychiatry, University of Groningen, University Medical Center Groningen, for checking the correctness of the statistical analyses.
Funding
Kenneth E. Freedland is supported by the National Heart, Lung, and Blood Institute (NIH), Bethesda, Maryland, USA. Seyed H. Hosseini is supported by a research deputy of Mazandaran University of Medical Sciences, Sari, Iran. Hiroshi Sato is supported by research grants-in-aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology (11794035) and from the Japan Society for the Promotion of Science (15590743, 21590894, 25350910). Peter de Jonge is supported by a VIDI grant from the Netherlands Research Foundation (NWO-ZonMW no: 016.086.397) and a VICI grant from the Netherlands Research Foundation (NWO-ZonMW no: 91812607).
Competing interests
In the past three years, Dr. Kessler has been a consultant for Hoffman-La Roche, Inc., Johnson & Johnson Wellness and Prevention, and Sanofi-Aventis Group. Dr. Kessler has served on advisory boards for Mensante Corporation, Plus One Health Management, Lake Nona Institute, and U.S. Preventive Medicine. Dr. Kessler owns 25% share in DataStat, Inc.
Authors’ contributions
MA, RMC, JD, FD, KEF, SLG, SHH, KP, LP, CR, AMR, HS, RPS, and PdJ were involved in data acquisition. HMvL, PdJ, ERvdH, and RAS designed the study. HMvL, PdJ, and ERvdH analyzed the data and interpreted the results. RCK contributed to statistical methods. HMvL and PdJ drafted the manuscript. All authors read and revised the manuscript critically and approved the final manuscript.