LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
DEPARTMENT OF REHABILITATION
Predictive Factors for the
Prognosis of Post-MI Patients
Master’s Thesis
Alon Bornstein
MEDICAL ACADEMY
FACULTY OF MEDICINE
Supervisor: Assoc. Prof. Jūratė Samėnienė
Table of Contents
1. SUMMARY ... 3
2. ACKNOWLEDGEMENTS ... 4
3. CONFLICT OF INTEREST ... 5
4. CLEARANCE ISSUED BY THE ETHICS COMMITTEE ... 6
5. LIST OF ABBREVIATIONS ... 7
6. LIST OF TERMS ... 8
7. INTRODUCTION ... 9
8. AIM AND OBJECTIVES ... 11
9. LITERATURE REVIEW ... 12
9.1 Ischemic Heart Disease ... 12
9.2 Thrombolytic Therapy ... 12
9.3 Percutaneous coronary intervention ... 13
9.4 Cardiac rehabilitation ... 13
9.5 Smoking cessation ... 14
9.6 Randomized Control trial ... 14
9.7 Physical Activity ... 15
9.8 Body Mass Index ... 15
9.9 Diabetic Mellitus ... 16
9.10 Prevention of Risk Factors for Cardiovascular Diseases ... 16
10. RESEARCH METHOLODOGY ... 17
10.1 STATISTICAL DICTONARY ... 19
11. RESULT AND DISSCUTION ... 20
11.1 Demographic factors and mortality of post-MI patients ... 20
11.1.1 Age ... 20
11.1.2 Gender ... 23
11.2 Cardiac risk factors and mortality of post-MI patients ... 25
11.2.1 BMI and Fitness ... 25
11.2.2 Smoking ... 30
11.3 Medical History and mortality of post-MI patients ... 34
11.3.1 Diabetes Mellitus. ... 34
11.3.2 Other medical history events ... 36
12. CONCLUSIONS ... 38
1. SUMMARY
The research was done by 6th course medicine faculty student Alon Bornstein.
Title: Predictive factors for the prognosis of post-MI patients.
Aim of study: The aim of the study is to review the current literature and study difference predictive
factors that are correlated with mortality among post-MI patients.
Objectives:
1. Determine the influence of post-MI patient characteristics on their morality rate. The characteristics considered are demographic factors (age, gender), cardiac risk factors (BMI, Fitness, Smoking), and medical history (DM, prior HF, prior MI, prior stroke and prior unstable angina).
2. Assessing the predictive factors that correlate with the mortality among post-MI patients.
Methodology: A systematic review was conducted through PubMed electronic database search as
well as google scholar. 10 most relevant articles were identified and included based on the following criteria: Being published during the past decade, whether the article reports results on one of the factors I include in the analysis, and whether the article reports hazard ratios.
Results and conclusions: This study reviewed and evaluated the relationship of the most common
10 articles of post-MI patients on their mortality risk. This study shows that the only factor which seems uncorrelated with mortality among post-MI patients is gender. Age, smoking, and the medical history of patients, are all positively correlated with mortality rates. BMI, and physical fitness are negatively correlated with mortality rates.
2. ACKNOWLEDGEMENTS
Many thanks for Assoc. Prof. Jūratė Samėnienė for helping me and guiding me throughout this research.
3. CONFLICT OF INTEREST
The author declares no conflicts of interest.4. CLEARANCE ISSUED BY THE ETHICS COMMITTEE
No clearance issued by the Ethics Committee are needed in this study.5. LIST OF ABBREVIATIONS
CR – Cardiac RehabilitationCI – Confidence Intervals MI – Myocardial Infarction
PCI – Percutaneous Coronary Intervention HF – Heart Failure
STEMI – ST Elevation Myocardial Infarction RCT – Randomized Controlled Trial
PRISMA - Preferred Reporting Items for Systematic Reviews and Meta-Analysis N/A – Not Applicable
6. LIST OF TERMS
Cardiac Rehabilitation - Cardiac rehabilitation is a series of procedures provided to heart disease
patients, including health education, cardiovascular risk avoidance, physical exercise and stress control (1).
Percutaneous Coronary Intervention (PCI) - Percutaneous coronary intervention is a non-
surgical procedure used to treat stenosis (narrowing) of coronary arteries of the heart found in heart diseases(2).
Myocardial Infarction - Myocardial infarction (MI) is a clinical or pathological event in the setting
of myocardial ischemia in which there is evidence of myocardial injury (3).
Randomized Controlled Trial (RCT) - Randomized control trial is a study in which people are set
apart in random two different groups and received one of several clinical intervention ( for example different treatment) (4),(5).
ST Elevation Myocardial Infarction (STEMI) - A STEMI is a heart attack caused by the
complete blockage of a heart artery. STEMI stands for ST elevation myocardial infarction (STEMI). ST elevation” refers to a particular pattern on an ECG heart tracing and “myocardial infarction” is the medical term for a heart attack(6).
7. INTRODUCTION
Myocardial infarction (MI) is a clinical or pathological event in the setting of myocardial ischemia in which there is evidence of myocardial injury. MI is the number one cause of death in modern society. Out of the 57 million deaths in 2016, about 10 million people have died due to an MI event (3). The second cause of death, for comparison, was a stroke, from which less than 6 million people died. (Figure 1.) Outlines the top 10 causes of mortality, which shows the large difference between MI related deaths to other causes of death. But not all MI events immediately lead to mortality. Medicine has allowed many post-MI patients to survive (7). In this systematic review, I study the predictive factors of mortality among post-MI patients.
Figure 1. Top ten causes of mortality worldwide
There are several reasons for which understanding the predictive factors of mortality are important. First, patients may want to know their mortality rate, and a good understanding of such predictive factors can give them a better estimate.
Second, these factors may be important in designing cardiac rehabilitation (CR) programs. Cardiac rehabilitation programs have become a major part of the cardiology quality of treatment.
Their scope has shifted from the focus on exercise therapy to comprehensive secondary prevention strategies that can affect patient outcomes by managing risk factors, nutritional, psychological, behavioral and social factors (8). Cardiac rehabilitation includes predominantly secondary
prevention, and is based on early diagnosis of the disease process and the application of therapies to avoid disease development. Clinical studies have demonstrated that risk assessment and improvement techniques can delay, stabilize or even reverse atherosclerosis development and reduce cardiovascular events that can lead to mortality (9).
The systematic review of articles was conducted following the PRISMA statement (10). I first identified more than 500 articles related to factors which can potentially predict mortality among post-MI patients. After an initial elimination, a total of almost 80 articles were reviewed. Literature was selected through a search of PubMed and Google Scholar electronic databases. The 10 most relevant articles were selected to be included in the final analysis.
I categorize predictive factors into three groups: demographic factors, cardiac risk factors, and medical history factors. The demographic factors include age and gender. The cardiac risk factors include BMI, physical fitness, and smoking. The medical history factors include prior DM, prior stroke, prior MI, prior HF, and prior unstable angina.
In the process of reviewing recent studies, I reexamine two paradoxes that the early literature has raised. The first is the “obesity paradox” which states that patients with higher BMI are less likely to die following an MI event (11), (12). I complement the review of the “BMI paradox” by also considering physical fitness as a factor (13). The second paradox I revisit is the “smoker paradox” (14). This paradox states that current smokers are less likely to die following an MI event (15). I also review whether smoking cessation can help reducing mortality rates among patients who smoked prior to their MI event.
8. AIM AND OBJECTIVES
Aim of the study: The aim of the study is to review the current literature and study difference
predictive factors that are correlated with mortality among post-MI patients.
Objective:
1. Determine the influence of post-MI patient characteristics on their morality rate such as factors considered as demographic factors (age, gender), cardiac risk factors (BMI, Fitness, Smoking), and medical history (DM, prior HF, prior MI, prior stroke and prior unstable angina).
9. LITERATURE REVIEW
9.1 Ischemic Heart Disease - Ischemic heart disease know as well as coronary heart disease is
a disease characterized by reduced blood supply and oxygen to the heart.
It is the most common cause of death in most western countries. The coronary arteries, supply blood to the heart muscle and there are no other blood supply exists, so a blockage in the coronary arteries reduces the supply of blood to heart muscle.
Most ischemic heart diseases (IHD) are caused by atherosclerosis, usually present even when the artery lumens appear normal it is usually felt as angina, especially if a large area is affected. The narrowing or closure is predominantly caused by the covering of atheromatous plaques within the wall of the artery rupturing, leading to a heart attack. Types include stable angina, unstable angina, myocardial infarction (MI) and sudden cardiac death.
A common symptom is chest pain, it may be feel like heart burn.
Usually symptoms occur with emotional stress and exercise within less than few minutes and relived by rest. Shortness of breath may also present. In some cases the first sign is heart attack (16).
9.2 Thrombolytic Therapy – Blood clot (thrombus) developed in circulatory system due to injury of endothelial layer. It results as a mechanism of the body to repair the injured blood vessel. When thrombus is formed when is not needed this can significant produce consequence like embolism, heart attack, stroke, ischemia and so forth. Embolism occurs when the blood clot is formed inside blood vessel which fully or partially block the blood supply to a part of the body resulting potentially severe consequences. For example, emboli to the brain can cause ischemic stroke, pulmonary embolism leads to breathing difficulty, hemoptysis and chest pain. Thrombolytic therapy improve blood flow and break clots , thrombus and embolism that formed within the blood vessels. Thrombolytics can be used as treatment as well as prophylaxis for predisposing risk group ( i.e. Pulmonary Embolism, Deep Vein Thrombosis, Ischemic Heart Diseases (IHD), Myocardial Infarction (MI), arterial thromboembolism and acute ischemic stroke). Thrombolytic drugs rapidly lyse thrombi by catalyzing the formation of plasminogen activator ( Streptokinase, tissue plasminogen activator t-PA, Urokinase) that convert plasminogen into plasmin and further more break fibrin by fibrinolysis.(17)
9.3 Percutaneous coronary intervention – Percutaneous coronary intervention is a non-
surgical procedure used to treat stenosis (narrowing) of coronary arteries of the heart found in heart diseases. After entering the blood stream through the femoral or radial artery, the procedure uses coronary catheterization to visualize the blood vessels on X-ray imaging. After the procedure, cardiologist can perform coronary angioplasty using balloon catheter in which deflated balloon is advanced into obstructed artery and inflate to relieve the narrowing. Primary angioplasty is the urgent use of PCI in patients with acute MI (2).
9.4 Cardiac rehabilitation - Cardiac rehabilitation participation following percutaneous
coronary intervention (PCI) has been related to a substantial reduction in mortality rates.
Cardiac rehabilitation is a complex of procedures that are given to patients diagnosed with heart disease, which includes health education, advice of cardiovascular risk reduction, physical activity and stress management(1). Evidence that CR reduces mortality, morbidity, unplanned hospital admissions in addition to improvements in exercise capacity, quality of life and psychological well-being is increasing, and it is now recommended in international guidelines(18).
Several meta-analyses have examined the benefits of CR for individuals following myocardial infarction (MI)
and revascularization and for those with heart failure (HF)(19).
Cardiac Rehabilitation comprises three distinct phases: inpatient, outpatient, and in the
community/home. Participation in these programs is determined by appropriate risk to maximize
health care resources and benefits. The inpatient program consists of low-level activities that gradually progress throughout the
hospital stay to prevent reconditioning. Education and counselling also begin at this time. The outpatient program is the most common CR model in use and may last from 2 to 4 months. These programs combine physician- supervised exercise sessions with cardiovascular risk reduction, commonly using a case management model. A full risk-factor and lifestyle assessment is
conducted both at the beginning and end of the program. On completion of the outpatient program, patients can then continue in a local community center CR program. Patients who are at low risk may appropriately continue their program in a home-based setting.(20) (Figure 2.) Outlines the multidisciplinary nature of the modern CR program.
Figure 2. Multidisciplinary nature of the modern CR program. (20)
9.5 Smoking cessation – (also known as quitting smoking) Is a process of discontinuing Tabaco smoking. Tabaco smoke contains nicotine, and it’s addictive and can cause dependence. There are a lot of different strategies that are used for smoking cessation including abruptly quitting without assistance, behavioral counseling, and medications such as Bupropion, Cytisine and Nicotine Replacement Therapy. In nicotine dependent smokers, quitting smoking can lead to symptoms of nicotine withdrawal, craving, anxiety, depression, and weight gain. The health benefits over time of stop smoking include: blood pressure and heart rate decreases, carbon monoxide level in the blood decrease to normal, sense of taste and smell start recovering, circulation and lung function improve, there are decrease in cough and shortness of breath. Within 1 year, the risk of coronary heart disease is decreased by half. In 10 years the risk of dying from lung cancer is also decreased by half, as well the risk of larynx and pancreas cancer decreases. (21), (22)
9.6 Randomized Control trial – Randomized control trial is a study in which people are set apart in random two different groups and received one of several clinical intervention ( for example
different treatment). This interventions is the standard of comparison of control. The control may be comparison of standard practice or placebo ( “sugar pill”) or no intervention at all. RCT are quantitively, comparative, controlled experiments in which investigators study two or more interventions in a series of individuals who received them in random order. The groups are followed under conditions of a trial, designed to see how effective the experimental intervention was (4),(5). 9.7 Physical Activity – Regular physical activity is an important factor in patients with coronary
artery disease which helps to prevent and reduce mortality, morbidity and risk factors. Among patients with established coronary diseases, regular physical activity has well been found to improve and prevent heart attacks and other risk factors contributes to cardiovascular diseases and mortality. Sedentary life style is an important cardiovascular risk factor. It is the leading cause of death in developing and developed countries. Intensity is the amount of physical power expresses as a percentage of the maximal oxygen consumption that the body uses when preforming an activity. Intensity levels are categorized into three different intensity. Those levels are low, moderate and vigorous and are measured by Metabolic equivalent of task (MET).
MET <5 = Light physical activity ( sleeping , walking)
MET 5-10 = Moderate physical activity (bicycling, moderate effort) MET >10 = Vigorous physical activity (jogging, running, jumping)
Exercise intensity levels are very subjective and depend on fitness level.(23), (24).
9.8 Body Mass Index – BMI is the most practical way to evaluate the degree of obesity. BMI is
calculated from the height and weight as follows: BMI= body weight (kg)/ square of height ( in meters) BMI classification by WHO :
Underweight – BMI of < 18.5 kg/m2
Normal weight – BMI of 18.5 to 24.9 kg/m2 Overweight – BMI of 25.0 to 29.9 kg/m2 Grade 1 obesity – BMI of 30.0 to 34.9 kg/m2 Grade 2 obesity – BMI of 35.0 to 39.9 kg/m2 Grade 3 or severe obesity – BMI > 40.0 kg/m2 (11)
9.9 Diabetic Mellitus – DM is a group of metabolic disorders characterized by hyperglycemia
over a prolonged period. DM is classified into 2 groups:
DM type 1 – DM type 1 characterized by autoimmune destruction of the pancreatic beta cells, leading to absolute insulin deficiency. DM type 1 account for 10-20 % in adults.
DM type 2 – Type 2 diabetes is the most common type of diabetes in adults accounts for >90%. It is characterized by hyperglycemia usually due to progressive loss of insulin secretion from beta cell overlying on a background of insulin resistance, resulting in relative insulin. The classical symptoms of hyperglycemia including polyuria, polydipsia, nocturia, blurred vision and weight loss. (25) 9.10 Prevention of Risk Factors for Cardiovascular Diseases – Cardiovascular disease is the leading cause of death in developing countries with the prevalence that is increasing rapidly. Many risk factors are vary by preventive measures with their opportunity to reduce the prevalence of CVD. Patients without coronary heart disease have a risk of subsequent cardiovascular disease that is equal to those patients with established coronary disease. Examples of those high- risk patients include patients with noncoronary atherosclerotic arterial disease, DM, and chronic kidney failure. The treatment should be equal for those with coronary heart disease as well as for those with prior coronary heart disease.
The risk factors are: Family history for coronary heart disease, HT and dyslipidemia. Effectively treating both HT and dyslipidemia can significantly reduce the risk for further CVD. A variety of lifestyle factors, including , diet, cigarette smoking , alcohol consumption, obesity, and exercise, significantly prevent for developing cardiovascular diseases.
10. RESEARCH METHOLODOGY
This systematic review was conducted following the PRISMA statement (10). I first identified 532 articles related to the topic of this study. After an initial elimination, a total of 78 articles were reviewed. Literature was selected through a search of PubMed and Google Scholar electronic databases. The 30 most relevant, by abstract and full text review, articles were selected. Out of these 30 articles, 20 articles were excluded as they were not included in meta-analysis part to my research. 10 articles were included in meta – analysis part to my research.
The research methodology included five steps to identify articles for the final analysis: Finding most relevant keywords, Identifying articles related to this topic via these keywords, Eliminating according to broad criteria, Reviewing abstracts and eliminating according to relevance, and reviewing full articles and eliminating according to degree of relevance.
The first step I took was identifying the keywords which I’ve used to look for papers related to the topic of analysis. To do so, I reviewed a few articles which gave a broad analysis of the factors which can predict mortality in post MI patients (1), (2). As written in previous sections, I have identified demographic factors, cardiac risk factors, and medical history factors. In particular, some keywords I have used are: Myocardial infarction (MI), smoking cessation, age, gender, DM, medical history, BMI, mortality.
In the second stage of analysis I used several combinations of these keywords to identify papers relevant to my study. I used two data sources. The first is the PubMed database, and the second is the Google Scholar database. After the first stage, I have identified 532 papers that seemed related to the topic I study.
The third stage of analysis included a preliminary review to eliminate papers that were written prior to 2009, as well as eliminating duplicates. After such preliminary review, I have remained with 309 articles for review.
The fourth step of analysis included title and abstract review. The key elimination criteria I used for this stage is whether the article used mortality as the outcome variable. Despite using mortality as a keyword, many articles did not include mortality as their main outcome variable. One such example is (3), which despite using the word mortality several times, have analyze the risk factors for decreased CO following an MI event.
In the fifth step, I have reviewed the remaining 78 full articles and assessed how relevant they are to my study. I have decided to include 30 articles in the final analysis. Out of the 30 articles, 20 articles were excluded as they were not included in the meta-analysis part to my research. 10 most
relevant articles were identified and analyzed in meta-analysis part to my research. (Figure 3.) Presents a flow diagram with the number of articles in each of these five steps.
Figure 3. Flow chart for articles exclusion and inclusion processes in the study.
532 relevant articles identified
309 remained after preliminary
elimination
78 remained for careful review after
initial review
30 articles included in this systematic
review
10 articles were included in
meta-analysis part to my research
Out of these 309 articles, 231 were excluded because they didn’t use mortality as their main outcome variable Out of these 532 articles, 223 were excluded as they were posted before 2009 or were duplicates
Out of these 78 articles, 48 articles were excluded as they were not closely related to my research question
Out of these 30 articles, 20 articles were excluded as they were not included in meta-analysis part to my research.
10.1 STATISTICAL DICTONARY HR
The hazard ratio (HR) is the comparison of mortality probability between 2 different groups. For example, an HR estimate equal to 2 implies that mortality rate is 100% higher compared to the control group.
Confidence Intervals (CI)
95% confidence interval is a range of values which contains the true parameter being estimated with 95% probability. When the value 1 of the hazard ratio (HR) is inside the 95% confidence interval there is no statistically significant difference between the two groups.
For example HR= 1.03 (0.99 - 1.05). à No statistically significant.
P- value
P value helps determine the significance of results. The p-value is between 0-1. If the p-value is very small, this means that the HR is significantly different than 1 (there is a difference between the two groups).
Lower p value (typically <0.05) à Significant. Higher p-value (typically >0.05) à Non-significant.
Adjusted group
An adjusted analysis takes into account different in prognostic factors between groups that may influence the outcome. The model adjusted for age, BMI, HT, DM, recurrent IHD.
For example, an unadjusted HR estimate for smoking only compares smokers to non-smokers, while an adjusted HR controls for the gender as well so it compares smoking men/women to non-smokers men/women.
11. RESULT AND DISSCUTION
In this section, I review the results and conclusions of the articles which I’ve identified as the most related to the topic I study. I group the predictive factors of mortality among post-MI patients into three groups. The first group is demographic factors. The two demographic factors I surveyed are age and gender. The second group is cardiac risk factors. I focused primarily on BMI and smoking, and included also the estimates related to physical activity. The third and final group is factors related to the medical history of patients. This group of factors include prior diabetes mellitus (DM), prior hypertension (HT), and prior Myocardial infarction (MI) event.
11.1 Demographic factors and mortality of post-MI patients
11.1.1 Age. Age is considered a key risk factor in the mortality rates of post-MI patients. All articles
which study how different factors affect mortality rates include patient’s age as a factor. In this section, I review the results of 4 articles who analyzed how age is correlated with mortality rates among post-MI patients. Before presenting the results, let me give a brief description of each study:
1. Gruppetta et al. (27) followed 337 patients in Malta. 2. Yu et al. (28) ran an RCT with 3,602 patients in the US. 3. Shih et al. (29) followed 643 patients in Taiwan.
4. Steele et al. (15) followed 3,133 patients in the UK.
Table 1 presents the hazard ratio (HR) estimates of different age groups, or age as a continuous variable. We see that all estimates point to a positive correlation between age and mortality. That is, older patients have a higher chance of mortality following an MI event.
Three studies have considered age as a running variable: Gruppetta et al. (27) ,Yu et al. (28), and Shih et al. (29). The three found that an additional year of age can increase the risk of mortality between 3% to 8%. Steele et al. (15), on the hand, divided patients into six age groups. They found that patients older than 75 years old have substantially higher risk of mortality both in the first 30 days after the MI event, as well as one year following the event. In particular, they are about 7 to 10 times more likely to die relative to patients younger than 45 years old.
I also observe that the P-value of these estimates are small, implying that these estimates are significant. I conclude that age is significantly related to mortality among post-MI patients.
The articles I surveyed all indicate that age has a significant impact on mortality rate (15),(27),(28). Older patients have higher mortality risk following an MI event.
Table 1. Results of age as a predictive factor of mortality
Study Variable Outcome Estimate 95% CI P-value
Shih et al. (2019)
(29) Age (per 1 year) HR 1.03 (0.99, 1.05) 0.09
Steele et al. (2019) (15) Age: 45-55 HR (30 days post-MI) 2.36 (0.90, 6.15) 0.079 Age: 55-65 3.45 (1.36, 8.74) 0.009 Age: 65-75 3.65 (1.42, 9.40) 0.007 Age: 75-85 7.14 (2.72, 18.74) 0.001 Age: >85 8.83 (2.97, 26.28) 0.001 Steele et al. (2019) (15) Age: 45-55 HR (1 year post-MI) 2.26 (1.06, 4.84) 0.046 Age: 55-65 2.49 (1.68, 7.27) 0.001 Age: 65-75 3.95 (1.88, 8.29) 0.001 Age: 75-85 8.73 (4.12, 18.51) 0.001 Age: >85 14.54 (10.47, 24.11) 0.001 Gruppetta et al. (2010)
(27) Age (per 1 year) HR 1.06 (1.04, 1.08) 0.001
Yu et al. (2014)
Figure 4.Post-MI mortality rate: Hazard ratio estimates of an additional year of age
Figure 5. 30 days Post-MI mortality rate: Hazard ratio estimates for different age groups
Figure 6. One year Post-MI mortality rate: Hazard ratio estimates for different age groups
11.1.2 Gender. Gender is another key factor which is potentially correlated with mortality among
post MI patients. There is a consensus that gender hormones such as estrogen protect against MI and delay its occurrence (30) and (31) These results imply that females are less likely to suffer an MI event, but does not predict whether or not being a female is associated with mortality after suffering an MI event.
To study the effect of gender on mortality, I compare the results of 4 studies: 1. Deaton et al. (32) followed 317 patients in the US.
2. Yu et al. (28) ran an RCT with 3,602 patients in the US. 3. Kang et al. (33) followed 14,253 patients in South Korea. 4. Shih et al. (29) followed 643 patients in Taiwan.
Table 2 presents the estimated HR of these four studies. We see that with the exception of Yu et al. (28) , all studies found the female have a higher risk of mortality following an MI event when not adjusting for other factors. Shih et al. (29) find the largest estimate, indicating that female are almost 2.5 times more likely to die relative to men following an MI event. Both Deaton et al. (32) and Kang et al. (33) find coefficients close to 2. That is, they find that female are about two times more likely to die following an MI event. The only study that finds mortality among men is more prevalent is Yu et al. (28). However, note that this estimate is close not significantly different than one, as the 95% confidence interval include the value one.
It is important to note that female are approximately ten years older when suffering an MI event (30) and (31) So a researcher must adjust for age when studying mortality among men and women following an MI event. If we use the age estimates from the previous section, being older by 10 years is associated with doubling the probability of mortality. When adjusting for age, Yu et al. (28) indeed find a coefficient insignificantly different than one.
The correlation between gender and mortality rate, is not significant for adjusted female group for age, BMI, and cardiovascular risk factors (HT, DM, recurrent IHD) (29),(33),(28). While the unadjusted HR of female is positive. The difference between the adjusted and unadjusted estimates are because female suffer MI events later in their life, on average.
I therefore conclude that gender is not correlated with mortality among post-MI patients, when adjusting for age.
Table 2. Results of gender as a predictive factor of mortality
Study Variable Outcome Estimate 95% CI P-value
Deaton et al. (2009) (32) Male HR 0.59 (0.31, 1.09) 0.093
Yu et al. (2014) (28) Female HR 0.92 (0.68,1.26) N/A
Kang et al. (2012) (33)
Female HR (adj.) HR (unadj.) 1.01 2.15 (0.87, 1.18) (1.90, 2.43) 0.878 0.001 Shih et al. (2019) (29) Female HR 2.46 (1.15, 5.24) 0.02 *Adjusted for age, BMI, cardiovascular risk factors (HT, DM, recurrent IHD)
Figure 7. Post-MI mortality rate: Hazard ratio estimates of patient׳s gender
11.2 Cardiac risk factors and mortality of post-MI patients
11.2.1 BMI and Fitness. The first two cardiac risk factors I consider are BMI and fitness. Earlier
papers that studied the correlation between BMI and mortality rates among post-MI patients found a surprising negative relationship (34), (12). This negative correlation has been termed the “obesity paradox”(34). “Obesity paradox” refer to the consideration that although obesity is a major risk to develop of cardiovascular or peripheral vascular disease, when acute cardiovascular decompression occurs, for example myocardial infarction (MI), obese patients may have a survival benefits. Furthermore it appears that obese men with chronic hypertensive heart disease live longer than men with normal weight. Major hypothesis for this apparent survival effect include: Obese patients may have better and more aggressive medical care, and enhanced observation than normal weight patients. Obese patients tend to be younger at the time of acute cardiovascular event, which confers the age benefit. Other investigations suggest the way we measure obesity is unsatisfactory and may explain the paradox results. (35)
In this section, I review recent studies which analyzed the relation between BMI and morality rates of post-MI patients and further examine studies which examined both BMI and fitness indicators. I consider three studies:
1. McAuley et al. (13) followed 9,563 male patients in the US. 2. Bucholz et al. (34) followed 124,981 patients in the US. 3. Shih et al. (29) followed 643 patients in Taiwan.
Table 3 presents the results of these three studies. Consistent with the “BMI paradox”, the HR estimates of BMI are negative (13). Thus indicating that patients with higher BMI have lower risk of mortality following an MI event. Shih et al. (29) estimates that an additional BMI point reduces mortality risk by 6% to 7%. The estimates of Bucholz et al. (34) suggest that obese patients are less likely to die following an MI event relative to overweight patients, which in turn are less likely to die relative to normal BMI patients. Note that these estimates are significantly lower than one, indicated by the confidence intervals that exclude the value one.
McAuley et al. (13) considers fitness in addition to BMI levels. She finds that patients with the same BMI are less likely to die if their fitness is higher. In particular, normal BMI patients with low-fitness are 60% more likely to die relative to high-low-fitness patients in the same BMI range. For patients in the overweight BMI range, high-fitness patients are 25% less likely to die relative to low-fitness patients. They find the same pattern for obese I, II, and III patients. Note that this is not in direct contradiction to the “BMI paradox”. McAuley et al. (13) do find that high fitness patients are less likely to die if their BMI is higher. Obese I patients with high fitness are about 20% less likely to die than overweight patients with high fitness, which are about 16% less likely to die than normal BMI with high fitness patients.
To conclude, also recent studies confirm that high BMI is less correlated with mortality, but also find that high fitness is less correlated with mortality. Therefore, the “BMI paradox” does not suggest that patients should not get fit.
Table 3. Results of BMI and fitness as predictive factors of mortality
Study Variable Outcome Estimate 95% CI P-val
Shih et al. (2019) (29) BMI HR 0.93 (0.87, 0.99) 0.03 HR (adj.) 0.94 (0.87, 1.01) N/A Bucholz et al. (2016) (34) BMI: overweight HR (30 day) 0.7 (0.68, 0.73) N/A
BMI: overweight (adj.) 0.92 (0.89, 0.95) N/A
BMI: overweight
HR (1 year)
0.67 (0.66,0.69) N/A
BMI: overweight (adj.) 0.87 (0.85, 0.89) N/A
BMI: obese
HR (30 days)
0.69 (0.66, 0.73) N/A
BMI: obese (adj.) 0.96 (0.91, 1.01) N/A
BMI: obese HR
(1 year post-MI)
0.64 (0.62, 0.66) N/A
BMI: obese (adj.) 0.86 (0.83, 0.89) N/A
McAuley et al. (2012) (13)
BMI: normal, low
fitness HR 1.60 (1.24, 2.05) N/A
BMI: overweight, low
fitness 1.09 (0.88, 1.36) N/A
BMI: overweight, high
fitness 0.84 (0.68, 1.03) N/A
BMI: obese I, low
fitness 1.38 (1.04, 1.82) N/A
BMI: obese I, high
fitness 0.65 (0.38,1.10) N/A
BMI: obese II/III, low
fitness 2.43 (1.55, 3.80) N/A
BMI: obese II/III, high fitness 2.46 (0.61, 9.96) N/A
*HR 30 days and 1 year post-MI
*Adjusted group of Shih et al. (2019) – adjusted for age and gender.
*Adjusted group of Bucholz et al. (2016) - adjusted for age, gender, race, cardiovascular risk factors ( DM, HT, smoking and prior coronary events).
Figure 9. 30 days Post-MI mortality rate: Hazard ratio estimates for different BMI groups
Figure 10. One year Post-MI mortality rate: Hazard ratio estimates for different BMI groups
Figure 11. Post-MI mortality rate: Hazard ratio estimates across BMI and fitness activity groups
11.2.2 Smoking. Cigarette smoking is an established risk factor for cardiovascular disease (15).
However, much like the “BMI paradox”, early studies find that while smoking increases the risk of an MI event, it is negatively correlated with mortality rates in post-MI patients. That is, current smokers have lower mortality rates following MI relative to non-smokers. This negative correlation is termed the “smoker paradox”. The “smokers paradox” is a controversial phenomenon of an unexpected favorable outcome of smokers post-acute myocardial infarction (MI). The definitive explanation for this unexpected protective effect of smoking remains unclear.(36)
Some studies showed that this negative correlation persists even when adjusting for patients’ age (15). In this section, I review the “smoker paradox” in light of three recent medical studies:
1. Shah et al. (22) followed 2,231 patients in a RCT study in the US. 2. Yu et al. (28) ran an RCT with 3,602 patients in the US.
3. Steele et al. (15) followed 3,133 patients in the UK.
Table 4 presents the results of these three studies. Surprisingly, the two recent studies, Steele 2019 et al.(15) and Yu et al. (28), who compare current smokers to non-smokers find that current smokers have higher mortality risk post-MI. Steele et al. (15) find that current smokers have 30% higher mortality rates relative to non-smokers. Yu et al. (28) estimate the increase in mortality rates to be 50% for smokers. Steele et al. (15) also study the difference between ex-smokers and non-smokers, but they find no statistically significant difference between the two groups. The value 1 of the hazard ratio is inside the 95% confidence interval.
So why do these recent studies contradict earlier studies which find the smokers have lower mortality rates? Steele et al. (15) suggest an explanation. They mention that all earlier studies have examined patients who were treated with thrombolytic therapy following the MI event. However, Steele et al. only considered patients who were treated with PCI. It is important to note that the prevalent therapy used nowadays is PCI, and not thrombolytic therapy. This suggests that smokers may have gained a greater therapeutic benefit from thrombolytic therapy relative to non-smokers. Subsequently, if all patients are treated by thrombolytic therapy, this could lead to a “smoker paradox” being observed (15).
Finally, I also review the finding of Shah et al. (22). They study the difference in mortality rates across a group of persistent smokers and a group of patients who smoked prior to the MI event and then ceased smoking. Their finding suggest that smoking cessation has a substantial impact on lowering mortality rates. They find that the 6-months smoking cessation group are about 50% less likely to die following an MI event. These estimates are similar also for the 12-months group (42%), and the 24-months group (47%). These estimates are all significantly lower than one, as indicated in the confidence intervals (the only exclusion being the 24-months smoking cessation estimate that the confidence interval is greater than one).
In conclusion, when using PCI therapy, non-smokers have lower mortality rates relative to smokers. However, patients who smoked prior to their MI event can potentially reduce by a factor of two their risk of mortality, if they just quit smoking (15).
Table 4. Results of smoking as a predictive factor of mortality P-val 95% CI Estimate Outcome Variable Study 0.212 (0.87-1.93) 1.29 HR (30 days) Current smoker Steele et al. (2019) ) 15 ( HR (1 year) 1.32 (0.97-1.81) 0.079 Current smoker 0.353 (0.53-1.25) 0.82 HR (30 days) Ex-smoker 0.765 (0.77-1.43) 1.05 HR (1 year) Ex-smoker N/A (0.35-0.81) 0.53 HR 6-months smoking cessation Shah et al. (2010) ) 22 ( N/A (0.36-0.91) 0.57 6-months smoking cessation (adj.) N/A (0.33-0.99) 0.58 12-months smoking cessation (adj.) N/A (0.25-1.08) 0.53 24-months smoking cessation (adj.) 0.001 (1.12-1.99) 1.49 HR Smoking Yu et al. (2014) ) 28 (
* HR 30 days and 1 year post-MI
*Steele et al. (2019) - Comparison group for smoking status is never smokers.
*Ex – smokers - patients who smoked prior the MI and stop smoking after the myocardial event. *Shah et al. (2010) - adjusted of data on LV function post-MI.
Figure 12. 30 days Post-MI mortality rate: Hazard ratio estimates for current smokers and ex-smokers groups compared to non-smokers
Figure 13. One year Post-MI mortality rate: Hazard ratio estimates for current smokers and ex-smokers groups compared to non-smokers
Figure 14. Post-MI mortality rate: Hazard ratio estimates for smokers versus smokers quitters
11.3 Medical History and mortality of post-MI patients
11.3.1 Diabetes Mellitus. The final section of analysis includes the medical history of patients as a
predictive factor of post-MI mortality. I start by analyzing five studies which consider prior diabetes mellitus (DM) among patients:
1. Gruppetta et al. (27) followed 337 patients in Malta. 2. Yu et al. (28) ran an RCT with 3,602 patients in the US. 3. Jernberg et al. (37) followed 97,254 patients in Sweden. 4. Shih et al. (29) followed 643 patients in Taiwan.
Table 5 presents the results of these studies. All five studies find that the hazard ratios at one year horizons are greater than one for patients with prior DM event. In other words, patients who suffered from DM prior to their MI event suffer higher mortality rates relative to patients who did not suffer from DM. The estimates are mostly in the 30%-40% range of increase in mortality rate (15), (27), (37), and (28). Shih et al. (29) find a much larger coefficient. They estimate that prior DM event can increase mortality by a factor of four. I tend to think Jernberg et al. (37) is the most reliable as it uses almost 100,000 patients to conduct the analysis. This is reflected in the relatively small confidence interval.
For the 30-days HR, Steele et al. (15) find a coefficient very close to 1, and not significantly different than one. Jernberg et al. (37) point out that it is important to consider longer time horizons to assess the correlation between mortality rates and prior medical history events.
Table 5. Results of prior DM as a predictive factor of mortality
Study Variable Outcome Estimate 95% CI P-value
Shih et al (2019) (29) DM HR 4.39 (1.83, 10.38) 0.001 HR (adj.) 3.89 (1.63, 9.28) 0.002 Steele et al. (2019) (15) DM HR (30 days) 1.02 (0.65, 1.59) 0.093 DM HR (1 year) 1.38 (1.01, 1.87) 0.04 Gruppetta et al. (2010) (27) DM HR 1.31 (1.02, 1.69) 0.038 Jernberg et al. (2015) (37) DM HR 1.37 (1.34, 1.40) 0.001 Yu et al. (2014) (28) DM HR 1.43 (1.06, 1.93) 0.02
*Shih et al. (2019) - adjusted for age and gender. * HR - 30 days and 1 year post-MI
Figure 15. Post-MI mortality rate: Hazard ratio estimates an additional year for patients who suffered DM prior MI
I have also reviewed other medical history events, using the same articles I used to survey prior DM as a predictive factor. Table 6 presents the results of these studies.
11.3.2 Other medical history events. The first other medical history event I consider is a prior MI
event. Gruppetta et al. (27), Jernberg et al. (37) and Yu et al. (28) all find that such prior event is positively correlated with mortality rates. Gruppetta et al. (27) and Jernberg et al. (37) estimate an increase in mortality rate of about 45%, while Yu et al. (28) estimates the increase in mortality rate to be almost 80%. In all three articles this estimate is significantly greater than one, as one is outside of the 95% confidence interval.
Jernberg et al. (37) also consider prior stroke, prior HF, and prior unstable angina. They find that all three are significantly associated with an increase in mortality rates. Prior stroke increases mortality rate by 50%, prior HF by almost 60%, and prior unstable angina by 10%.
In of this section, I find that prior medical history events are positively correlated with mortality rates among post-MI patients. There is much evidence that prior DM event increases mortality rates
by about 30 to 40%. And other medical history such as prior MI, prior stroke, prior HF, and prior unstable angina, are also significantly correlated with mortality rates.
Table 6. Results of other medical history as a predictive factor of mortality
Study Variable Outcome Estimate 95% CI P-value
Gruppetta et al. (2010) (27) Prior MI HR 1.47 (1.07, 2.04) 0.016 Jernberg et al. (2015) (37) Prior MI HR 1.44 (1.40, 1.49) 0.001 Prior stroke 1.49 (1.44, 1.54) 0.001 Prior HF 1.57 (1.35, 1.61) 0.001
Prior unstable angina 1.13 (1.08, 1.17) 0.001 Yu et al.
(2014) (28) Prior MI HR 1.79 (1.29, 2.49) 0.001
Figure 16. Post-MI mortality rate: Hazard ratio estimates for patients who suffered other medical events prior MI
12. CONCLUSIONS
In this study, I surveyed 10 articles about the correlation of mortality among patients who suffered an MI event with different patient characteristics. I categorized factors into three groups: demographic factors ( age, and gender), cardiac risk factors ( BMI, fitness, and smoking) , and the medical history of patients (DM, prior MI, prior stroke, prior heart failure, and prior unstable angina).
u Summary of results
u Older patients: higher mortality rates
u Females: higher mortality rates, yet not significant difference
u BMI: higher BMI associated with lower mortality rates (Obesity Paradox) u Fitness: better fitness reduces mortality rates
u Smoking: smoking is associated with higher mortality rates
u Medical history: Diabetes Mellitus associated with higher mortality rates
In conclusion, the only factor which seems uncorrelated with mortality among post-MI patients is gender. Age, smoking, and the medical history of patients, are all positively correlated with mortality rates. BMI, and physical fitness are negatively correlated with mortality rates.
Limitations. It is important to note that the presence of a correlation between two variables does
not imply causation. In particular, the fact that BMI is negatively correlated with mortality rates does not imply that if a patient gains a lot of weight by eating junk food, then he or she would have lower mortality rates. More research is needed to understand the correlation between BMI and mortality rates.
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