IHP 525 Final Project Data Analysis Guidelines and Rubric

IHP 525 Final Project Data Analysis Guidelines and Rubric
Overview
Now that you have submitted your article review, you will
submit Your Final Project Data Analysis. The Final Project
Article Review was an opportunity to
demonstrate your ability to interpret statistics included in an
article. The Final Project Data Analysis is a chance to show that
you know how to choose the
correct statistics to analyze a set of data and calculate these
using software.
Regardless of their field of interest, health professionals across
disciplines need to be able to run basic biostatistical
calculations to describe a set of data.
The Final Project Data Analysis reinforces these critical skills
by asking you to conduct your own analysis of a small data set,
explain the basic parameters of the
data, graph it, and run simple tests. You will present this data
analysis in a brief statistical report, using language appropriate
to a non-technical audience.
The Final Project Data Analysis consists of four milestones,
submitted in Modules Two, Three, Five, and Seven. The final
submission occurs in Module Nine.
In this assignment, you will demonstrate your mastery of the
following course outcomes:

-appropriate statistical calculations and
hypothesis testing in accurately analyzing biostatistical data
addressing population-based health problems
and analysis
to other health professionals and the general public for
informing their decisions related to
population-based health problems
Prompt
Biostatisticians are constantly called upon to analyze data in
order to help researchers and health officials answer critical
questions about populations’ health.
For this assessment, you will imagine you are a biostatistical
consultant on a small study for a local health organization. In
the Assignments Guidelines and
Rubrics area of the course, you will use the Data Analysis Data
Set and Data Analysis Data Description, along with some
background information on how and
when the data was collected and the general research question
the organization is interested in answering. This is often the
way you will receive data in the real
world.
Your task is to help the organization answer their question by
critically analyzing the data. You will compute your chosen
statistics, interpret the results, and
present the results and recommendations to non-technical
decision makers in the form of a data analysis. Keep in mind
that it is your job to do this from a
statistical standpoint. Be sure to justify your conclusions and
recommendations with appropriate statistical support.

Specifically, you must address the critical elements listed
below. Most of the critical elements align with a particular
course outcome (shown in brackets).
I. Introduction
A. State the overall health question you have been asked to
address in your own words. Be sure you capture the key
elements of the question,
using language that a non-technical audience can understand.
B. Assess the collected data. Use this section to layout the
source, parameters, and any limitations of your data.
Specifically, you should:
1. Describe the key features of your data set. Be sure to assess
how these features affect your analysis.
2. Analyze the limitations of the data set you were provided and
how those limitations might affect your findings. Justify your
response.
C. Process: Propose how you will go about answering the health
question you were asked to address based on the data set
provided.
II. Data Analysis
A. Graphs: In this section, you will use graphical displays to
examine the data.

1. Create at least one graph that gives a sense of the potential
relationship between the two variables that form your chosen
health
question. Include the graph and discuss why you selected it as
opposed to others.
B. Conduct an appropriate statistical test to answer your health
question.
C. Explain why this test is the best choice in this context.
D. Analysis of Biostatistics: Use this section to describe your
findings from a statistical standpoint. Be sure to:
1. Present key biostatistics from the graph(s) and statistical
tests and explain what they mean. Be sure to include a
spreadsheet showing
your work or a copy of your StatCrunch output as an appendix.
2. What statistical inferences or conclusions can you draw based
on the results of your statistical test and graph? Justify your
response.
III. Conclusions and Recommendations
A. How do the findings help answer your overall health
question? Remember to use brief, non-technical language to
ensure audience
understanding.
B. Recommend areas for further research based on your
findings. Remember to use brief, non-technical language to
ensure audience
understanding.

Milestones
Milestone One: Select Health Question
In Module Two, you will identify the health question you will
be researching for instructor feedback and approval. Milestone
One should be several sentences in
length. This milestone is graded with the Milestone One Rubric.
Milestone Two: Describe the Data
In Module Three, you will describe the key features of the data
set, including limitations that might exist. Milestone Two
should be one or two paragraphs in
length. This milestone is graded with the Milestone Two Rubric.
Milestone Three: Process and Calculations
In Module Five, you will create a table in which you propose
the calculations (descriptive statistics and statistical test) and
graph(s) you will need to perform to
answer the health question you are investigating. Then you will
complete the table. For Milestone Three, you will submit this
completed table. This milestone is
graded with the Milestone Three Rubric.
Milestone Four: Data Analysis
In Module Seven, you will submit the data analysis section of
Final Project Data Analysis. This section includes the graph(s)
and statistics you conducted on the
data set to answer the health question. This milestone is graded
with the Milestone Four Rubric.
Final Project Data Analysis
In Module Nine, complete the conclusions section and the rest
of the completed data analysis (including graph(s), statistical
output for chosen test, or Excel

spreadsheet with calculations). You have been working on this
analysis in Milestones One through Four. Before submitting,
revise each section of the analysis
based on the feedback you received from your instructor and
peers. It should be a complete, polished artifact containing all
of the critical elements of the final
product. It should reflect the incorporation of feedback gained
throughout the course. This submission will be graded using the
Final Project Data Analysis
Rubric.
Deliverables
Milestone Deliverable Module Due Grading
One Select Health Question Two Graded separately; Milestone
One Rubric
Two Describe the Data Three Graded separately; Milestone Two
Rubric
Three Process and Calculations Five Graded separately;
Milestone Three Rubric
Four Data Analysis Seven Graded separately; Milestone Four
Rubric
Final Submission: Final Project Data Analysis Nine Graded
separately; Final Project Data Analysis Rubric
Final Project Data Analysis Rubric

Guidelines for Submission: Your data analysis should be
approximately 3–5 pages long (including graphs or spreadsheet
with calculations), double-spaced, 12-
point Times New Roman font, with one-inch margins and
citations in APA format. Be sure to use language and a style
appropriate for a non-technical audience.
Critical Elements Exemplary Proficient Needs Improvement
Not Evident Value
Introduction: Health
Question
[IHP-525-05]
N/A States overall health question
in own words, capturing key
elements of question while
using language appropriate for
a non-technical audience
States overall health question in
own words, but response
contains inaccuracies, omits key
details, or does not use language
appropriate for a non-technical
audience
Does not state overall health
question in own words
8
Introduction: Data:
Key Features

[IHP- 525-03]
Meets “Proficient” criteria and
demonstrates a sophisticated
awareness of the features’
influence
Describes key features of data
set and assesses how features
affect analysis
Describes key features of data set
and assesses how features affect
analysis, but response contains
inaccuracies or omits key details
Does not describe key features of
data set and assess how features
affect analysis
9
Introduction: Data:
Limitations
[IHP-525-05]
justification provides keen
insight into how data quality
affects findings
Analyzes limitations of data set
provided and how those affect
findings, justifying response

Analyzes limitations of data set
and how those affect findings,
but does not justify response,
response contains inaccuracies,
or justification is illogical
Does not analyze limitations of
data set and how those affect
findings
8
Introduction: Process
[IHP-525-02]
process proposed is well-
aligned with health question,
taking most direct path to
answer
Proposes process of answering
health question based on the
data set provided
Proposes process of answering
health question based on data
set, but response contains
inaccuracies, omits key
procedures, or procedures
suggested are inappropriate
Does not propose process of
answering health question based
on data set

9
Data Analysis:
Graphs: Graph
[IHP-525-02]
graph incorporates appropriate
scaling and is exceptionally
well-tailored to the intended
audience
Creates a graph that gives a
sense of the potential
relationship between two
variables that form the chosen
health question and discusses
why this graph was selected
over others
Creates a graph that gives a sense
of the potential relationship
between two variables that form
the health question and discusses
why this graph was selected over
others, but graph is
inappropriate, reasons are
illogical, or response contains
inaccuracies
Does not create a graph that
gives a sense of the potential
relationship between two
variables
9

Not Evident Value
Data Analysis: Test
[IHP-525-02]
demonstrates an astute ability
to accurately and effectively
conduct the test
Conducts appropriate
statistical test accurately to
answer chosen health question
Conducts statistical test to
answer chosen health question,
but response contains
inaccuracies or test is not
conducted appropriately
Does not conduct appropriate
statistical test to answer chosen
health question
9
Data Analysis: Best
Choice
[IHP-525-02]

makes cogent connections
between the test and graph or
data
Explains why test is the best
choice in this context
Explains why test is the best
choice in this context, but
explanation is cursory or contains
inaccuracies
Does not explain why test is the
best choice in this context
9
Data Analysis:
Analysis: Biostatistics
[IHP-525-03]
explanation effectively
communicates meaning of the
calculations in audience-
appropriate language
Presents graph and statistical
test results, including
spreadsheet showing work or
computer output, and
accurately explains what
chosen calculations mean
Presents graph and statistical test

results, including spreadsheet
showing work or computer
output, and explains what they
mean, but response contains
inaccuracies or omits key details
Does not present graph and
statistical test results, including
spreadsheet showing work or
computer output and does not
explain what these calculations
mean
9
Data Analysis:
Analysis: Statistical
Inferences
[IHP-525-03]
thoroughly explains how these
statistics define the population
Draws appropriate statistical
inferences based on statistical
hypothesis test results and
graph and justifies response
Draws appropriate statistical
inferences based on test results
and graph, but does not justify
response, justification is illogical,
or response contains inaccuracies

Does not draw appropriate
statistical inferences or
conclusions based on test results
and graph
9
Conclusions: Findings
[IHP-525-05]
demonstrates a complex grasp
of elements necessary to
answer the overall health
question
Assesses how findings help
answer overall health question,
using brief non-technical
language
Assesses how findings help
answer overall question, but does
not use brief, non-technical
language, or response contains
inaccuracies
Does not assess how findings
help answer overall health
question
8
Conclusions:
Recommendations

[IHP-525-05]
recommendations including
what additional information
would help better answer
question
Recommends areas for further
research based on findings,
using brief, non-technical
language
Recommends areas for further
research based on findings,
including additional information
to better answer question, but
does not use brief, non-technical
language, recommendations are
illogical, or response contains
inaccuracies
Does not recommend areas for
further research based on
findings, including additional
information that would help
better answer question
8
Articulation of
Response
Submission is free of errors
related to citations, grammar,
spelling, syntax, and

organization and is presented
in a professional and easy to
read format
Submission has no major errors
spelling, syntax, or organization
Submission has major errors
that negatively impact readability
and articulation of main ideas
Submission has critical errors
that prevent understanding of
ideas
5
Not Evident Value
Total 100%
Running Head: Milestone Four: Data Analysis
DATA ANALYSIS

7-2 Final Project Data Analysis Milestone Four: Data Analysis
To What Extent Do the Ages of MI Patients Vary by Gender?
Southern New Hampshire University
Biostatistics 21TW4
6/26/2021
Data Analysis
Bar chart Graph
The age with the highest number of MI cases is 80-90 years old.
This is a clear indication that as patients get old, they are more
prone to suffer from MI than when they are young. This is
supported by the findings of the graph whereby the closest
highest number of MI cases are recorded for individuals ages
between 70-80 years and 60-70 years. Thus, younger patients
are not susceptible to MI because few cases were recorded in
the graph above.
Simple Linear Regression Analysis
Simple linear regression results:
Dependent Variable: gender
Independent Variable: age
gender = -0.27342755 + 0.0091344696 age
Sample size: 100
R (correlation coefficient) = 0.27492767
R-sq = 0.075585226
Estimate of error standard deviation: 0.46324539
Parameter estimates:
Parameter
Estimate
Std. Err.

Alternative
DF
T-Stat
P-value
Intercept
-0.27342755
0.22505505
≠ 0
98
-1.2149363
0.2273
Slope
0.0091344696
0.0032268983
≠ 0
98
2.8307275
0.0056
Analysis of variance table for regression model:
Source
DF
SS
MS
F-stat
P-value
Model
1
1.7195639
1.7195639
8.013018
0.0056
Error
98
21.030436
0.21459629

Total
99
22.75
Why Linear Regression Was Chosen for This Analysis
By fitting the observed data to a linear equation, linear
regression attempts to predict the relationship between two
variables. One Variable is a dependent variable, whereas the
other is an explanatory variable. For example, in linear
regression models, a modeler could want to relate patients'
weights to their heights. A modeler should first determine if the
variables of interest are linked or not before attempting to fit a
linear model to the data. This does not necessarily imply that
one element causes the other (for example, higher SAT scores
do not always imply higher grades), but rather that the two
factors are linked. A dispersion can help you figure out how
strong the relationship between two variables is. A linear
regression model is typically not a feasible model to match the
data if there appears to be no link between the hypothesized
explanatory and dependent variables (i.e., the dispersion plot
shows no rising or falling trends). The correlation coefficient,
which ranges from -1 to 1, indicates the strength of the
observed data's link with both variables and is a useful
numerical measure of the relationship between the two
variables.
Description Of the Results
The p-value of the result is less than 0,05, and the p-value of
0,0056 shows a gender-related age for the MI patient. As a
result, substantial gender differences in total procedural rates
were completely disguised compared to male MI patients by the
older age profile of female MI patients. Male MI patients were
not more aggressive than female MI patients, but elderly

patients were lower in therapy, and women MI patients were
older than male MI patients. Males had significantly more MI
than females, which helps explain why the number of
procedures performed by men at each period was larger than
that of ladies for each treatment. The gender differences in most
patients getting procedures were far lower than gender
differences in the number of operations conducted. However,
they were still highly significant, showing that some, but not
all, gender disparities are explained by MIs.
This study shows that essential cardiac therapies following MI
in Manitoba presently have no gender bias and that similar
analyses in other jurisdictions can lead to similar conclusions.
Their greater age profile fully explained the lower incidence of
procedure among female MI patients in relation to male MI
patients since both male and female intervention rates fall
substantially when they are older (Vaccarino et al., 2018).
These findings are important for doctors and policymakers
because they show that while the patient's age impacts decisions
after the MI procedure, it is not the gender of the patient. The
fact that MI patients were treated equally for both men and
women in our research may show a change in clinical practice
as almost any other recent studies which have adequately
reflected their age have shown comparable irrelevant or slight
gender disparities. Bypass surgery may be an exception, and
further studies are necessary (Wilkinson et al., 2019). After MI,
proof of similar treatment rates also ignores other sex-related
heart disease issues, such as likely variance in health
conditions, presentation, diagnostics, treatment options, and
effectiveness.
Many studies have revealed that males had a larger incidence of
cardiac operations than females following an acute myocardial
infarction (MI), which indicates that males are more aggressive
than females. On the other hand, others did not reveal
significant differences following age adjustment, raising
questions about genuine gender bias in heart therapy. The
administrative data were used in this study to calculate the age-

specific rate of the procedure by sex using an inhabitant's
cohort approach. Gender differences and relationships have
been evaluated using chi-square and generalized linear models.
In all four operations studied, men had significantly higher rates
than women (p0.01) (Khera et al., 2017). On the other hand,
age-specific rates showed minimal significant gender
differences and a fast decrease in intervention rates for men and
women as they became older. Using generalized linear
modeling, patient age was an important predictor for
intervention rates, but not sex. Compared to the male
counterparts, the older age profile of female MI patients
completely confounded the considerable gender difference
overall.
References
Khera, R., Jain, S., Pandey, A., Agusala, V., Kumbhani, D. J.,
Das, S. R., ... & Girotra, S. (2017). Comparison of readmission
rates after acute myocardial infarction in 3 patient age groups
(18 to 44, 45 to 64, and≥ 65 years) in the United States. The
American journal of cardiology, 120(10), 1761-1767.
Vaccarino, V., Sullivan, S., Hammadah, M., Wilmot, K., Al
Mheid, I., Ramadan, R., ... & Raggi, P. (2018). Mental Stress–
Induced-Myocardial Ischemia in Young Patients with Recent
Myocardial Infarction: Sex Differences and Mechanisms.
Circulation, 137(8), 794-805.
Wilkinson, C., Bebb, O., Dondo, T. B., Munyombwe, T.,
Casadei, B., Clarke, S., ... & Gale, C. P. (2019). Sex differences
in quality indicator attainment for myocardial infarction: a
nationwide cohort study. Heart, 105(7), 516-523.

To what extent do the ages of MI patients vary by gender?
Maria Williams
Biostatistics 21TW4
06/13/2021
Description Of the Statistics That Will Be Used to Answer the
Health Question
Descriptive statistics are analyzing, synthesizing, and reporting
information from a sample or complete population. The three
main kinds of explanatory figures are Central Tendency
Measures, Inferential Analysis, and Variation Measures. While
descriptive statistics may give information about a data set, they
could be used to conclude data collection and analysis as well
as describe the relevant facts. Besides, descriptive statistics
improve data visualization (Kaliyadan & Kulkarni, 2019). It
allows data to be presented in a more meaningful and
understandable way, providing a better comprehension of the
data set at hand. Furthermore, descriptive statistics allow a data
set to be summarized and presented in various ways, including
tabular and graphical displays, as well as a commentary of the
findings. Also, descriptive statistics are used to summarize
complex quantitative data. The basic objective is to estimate
population parameters in inferential statistics. The unknown
values for the whole population, such as sampling distribution
and standard deviation, are these parameters.
The independent t-test, also recognized as the two-sample t-test,
student's t-test, or independent-samples t-test, is a statistical
inference test that determines if the means of two unrelated
groups differ statistically substantially (Emerson, 2017). In a T-
test, complete samples are simplified into a single t-value.
Since t-values are unitless, without an extra context they might

be difficult to comprehend. Therefore, t-distributions are
utilized; they assume that the zero hypothesis is true of the
sampling population and give a wider framework for
determining the uniqueness of t-value.
On the other hand, side-by-side box charts are useful for
comparing critical information about two data sets, such as the
median values and the range of values represented by the data.
Side-by-side box charts provide a concentrated overview and
evaluation of data. On their own, boxplots can only deal with
one quantitative variable. However, due to the 5-number data
summary, a box track can manage a vast quantity of data and
offer a summary. This is because a box plot comprises the
median, indicating the middle point of the data set, the upper
and lower quartiles, indicating the highest, lower, and minimum
data quarters, and the upper and lower limits data values above
and below. The organization of data in a plot of boxes utilizing
five key principles is an effective technique for dealing with
vast amounts of data that are too unmanageable for graphs like
line plots or leaf and stem plots. The separate sample T-test is a
statistical tool to test hypotheses that assesses if the mean of
two independent samples is statistically significantly different.
A box painting is a highly visually efficient approach to see a
concise overview of one or more data sets. It is especially
handy for swiftly summarizing and comparing several sets of
findings from various trials. On a look, a box plot allows the
distribution of findings to be graphically displayed and offers
symmetry hints within the data (Yu et al., 2019). However, the
plot does not maintain the exact values and intricacies of the
distribution outcomes, which in this case is a problem with
processing such vast volumes of data. A box plot displays a
brief overview of the distribution of the results to see the
findings and compare them with other data quickly. It should be
noted that one of the relatively few techniques of statistical
graphs showing outliers is a box plot. There may be one outlier
or several of them in a set of data, both below and above the
lowest and maximum data values. Thus, the box graph outlines

or obfuscates findings by stretching the lesser and larger data to
a maximum of 1.5 times the interquartile range. Any data
findings that go outside of the upper and lower bounds known
as outliers may be easily identified on a diagram in a box.
References
Emerson, R. W. (2017). ANOVA and t-tests. Journal of Visual
Impairment & Blindness, 111(2), 193-196.
Kaliyadan, F., & Kulkarni, V. (2019). Types of variables,
descriptive statistics, and sample size. Indian dermatology
online journal, 10(1), 82.
Yu, X., Li, C., Chen, H., & Yu, X. (2019). Evaluate the
effectiveness of multiobjective evolutionary algorithms by box
plots and fuzzy TOPSIS. International Journal of Computational
Intelligence Systems, 12(2), 733-743.
Running Head: Data Analysis Milestone Two: Describe the Data
DATA ANALYSIS
3-3 Final Project Data Analysis Milestone Two: Describe the
Data
Maria Williams
Biostatistics 21TW4
05/29/2021
Summary statistics

Column
n
Mean
Std. dev.
Median
Range
Min
Max
Q1
Q3
Age
100
68.25
14.42807
71
60
32
92
59.5
80.5
Gender
100
0.35
0.47937249
0
1
0
1
0
1
The total number of participants in this study was n=100. The
mean age is 68.25 years, with a standard deviation of 14.42.
The median age is 71 years with a range of 60. The lower
quartile is 59.5, while the upper quartile is 80.5. On the other
hand, the mean for gender is 0.35 with a standard deviation of

0.4793. The range value is 1 for gender.
Limitations
The key limitation is that the sample data is not representative
enough to be generalized. Attention is focused on key aspects of
"representativeness" and "generalizability" in both clinical and
epidemiological studies (Sam et al., 2018). This is an
encouraging trend, yet fundamental notions such as internal and
external validity are often misrepresented, hiding the core
difficulties of validity. In the text, the writers outline these
difficulties and illustrate how they are interconnected and apply
to other situations as well. Using real-world examples, they
demonstrate ways in which distinct kinds of bias and confusion
threaten validity. In addition, they include applicable concerns
such as the selection of samples, exposures, and assessments,
both in the clinic-based and population-based contexts.
Variables That Will Be Used
The study will focus on the significant factors of gender and
age. Studies show that, after AMI, men's incidence of cardiac
operations is much greater than that of women (Walli-Attaei et
al., 2020). However, in certain cases, results indicate that there
are no significant differences after accounting for age.
Therefore, it is impossible to say whether or not a gender bias
exists in cardiac treatment. To measure age-specific procedure
rates by sex from administrative data, a population-based cohort
technique will be utilized. Individual age-specific rates are most
likely to show few significant variations by gender, and the age
of intervention for both genders decreases dramatically after
age twenty. When patient age is factored into the model, there is
a substantial correlation between intervention rates and patient
age, although sex is not. The striking gender gap in the overall
AMI rates is obscured by the older age profile of women with
AMI.
References

Sam, D., Gresham, G., Abdel-Rahman, O., & Cheung, W. Y.
(2018). Generalizability of clinical trials of advanced melanoma
in the real-world, population-based setting. Medical Oncology,
35(7), 1-8.
Walli-Attaei, M., Joseph, P., Rosengren, A., Chow, C. K.,
Rangarajan, S., Lear, S. A., ... & Yusuf, S. (2020). Variations
between women and men in risk factors, treatments,
cardiovascular disease incidence, and death in 27 high-income,
middle-income, and low-income countries (PURE): a
prospective cohort study. The Lancet, 396(10244), 97-109.
Milestone 1
Maria Williams
Biostatistics
05/23/2021
To What Extent Do the Ages of MI Patients Vary by Gender?
This question inquires about whether there are any differences
between men's and women's ages at the time of myocardial
infarction. In the United States, heart disease is the top cause of
mortality for both men and women (Ladwig et al., 2017). On the
other hand, males have a higher rate of MIs and have them at a
younger age than females (Ladwig et al., 2017). Women had a
lower incidence but a higher fatality rate than males at the time

of their MI, despite being older at the time (Stehli et al., 2019).
However, research has shown that differences in symptoms and
treatment periods significantly impact the death rate among
women (Ladwig et al., 2017). Because the symptoms of a heart
attack differ so greatly between men and women, many women
may not know they have one and do not seek medical help
quickly. It might be the difference between life and death if you
wait until your symptoms become unbearable.
Women are more likely than males to have nausea and
discomfort in their arms, back, jaw, and throat. Men were more
likely than women to describe their symptoms to a heart cause.
When the prehospital delay was enhanced by experiencing
shoulder discomfort, assuming symptoms were noncardiac,
consulting a close relative, and calling several medical
practitioners. Women were more likely than males to be tired in
the year leading up to the event. According to the findings of
Stehli et al. (2019, women had a wider range of symptoms than
males. Prehospital delay may be influenced by acute symptoms,
symptom interpretation, and disease behaviour. When the
different age categories of individuals with acute myocardial
infarction are examined, the impact of sex on the death rate
becomes even less evident. In recent research of 155,565
females and 229,313 males, Stehli et al. (2019) found that
younger ladies have greater in-hospital mortality following an
acute myocardial infarction than males of the same age.
However, in a review of the ISIS-3 study's 36,080 participants,
Ladwig et al. (2017) found a greater disparity in mortality
among genders with age reduction and only a minor intimate
relations effect on mortality, which was somewhat greater in the
female sex.
Acute myocardial infarction presents differently in the youthful
population, with distinct entomopathogenic, anatomical, and
prognosis features distinguishing these individuals from the
elderly. Young people with metastatic myocardial infarction
suffer considerably more severe emotional and economic
implications when they get unwell during their years of

increased output. Sex, like age, appears to have an impact on
the clinical manifestation of acute myocardial infarction.
Females with myocardial infarction had a greater prevalence of
pulmonary blood hypertension, diabetes, normal carotid artery,
and clinical symptoms of heart failure, in addition to being
around ten years older than men. It is unclear if the increased
mortality in females with acute myocardial infarction is due to
their advanced age, the differing frequencies of numerous risk
variables, or an independent relationship between female sex,
morbidity, and death following acute myocardial infarction.
Several studies have found that females had a lower rate of
surgical myocardial revascularization than men 67-71, a
tendency that has recently reversed 72 (Stehli et al., 2019).
Males and females had identical surgical indications, possibly
because of the lack of statistically significant variations in the
frequency of triple-vessel coronary heart disease and
deterioration of left ventricular systolic between the sexes. In
terms of clinical progression, no significant difference in the
prevalence of complications was seen between the sexes.
Females have been observed to have greater mitral
incompetence, heart failure, ventricle rupture, bradyarrhythmia,
and heart failure following acute myocardial infarction than
men; males had a greater prevalence of ventricular
tachyarrhythmia. The lack of age, systemic arterial
hypertension, diabetes mellitus, myocardial pericardial effusion,
and coronary blockage pattern variations between the sexes may
have led to the same frequency of sequelae.

References
Ladwig, K. H., Fang, X., Wolf, K., Hoschar, S., Albarqouni, L.,
Ronel, J., ... & Schunkert, H. (2017). Comparison of delay times
between symptom onset of an acute ST-elevation myocardial
infarction and hospital arrival in men and women< 65 years
versus≥ 65 years of age.: findings from the multicenter Munich
Examination of Delay in Patients Experiencing Acute
Myocardial Infarction (MEDEA) study. The American journal
of cardiology, 120(12), 2128-2134.
Stehli, J., Martin, C., Brennan, A., Dinh, D. T., Lefkovits, J., &
Zaman, S. (2019). Sex differences persist in time to
presentation, revascularization, and mortality in myocardial
infarction treated with percutaneous coronary intervention.
Journal of the American Heart Association, 8(10), e012161.

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