“Sudden Cardiac Death” or (SCD) is the widely accepted condition of abrupt failure of the heart
to function conventionally which results in death. Its prevalence is an epidemiological concern
and is universally accepted to affect sedentary and active individuals. The objective of this
analysis is to explore the potential causes of heart failure and how might some health
complications be attributed to (SCD) of athletes. While the importance of studying (SCD)
universally is of utmost concern, in athletics, the death of an athlete not only affects the
individual, friends, family, and associates, it may also complicate a franchise’s success, fan base,
investments, participation, and credibility. Additionally, intuitively, many assume that a
seasoned athlete of peak conditioning and one who abides by strict health regulations is unlikely
to be a victim of heart complications, but this is not the case. (SCD) is widely known as one of
the leading causes of death in athletes 1 . Consequently, the prominence of (SCD) in athletes is
paradoxical, counterintuitive, and not well understood. Given that perplexing census, three
primary source studies related to Sudden Cardiac Death of athletes will be analyzed to
disseminate information on select causes of (SCD), the methodology used by these studies, and
the results of these studies. Furthermore, a discussion of concluding remarks about the three
(SCD) studies will be provided, that aspire to succinctly integrate these findings and add some
additional supplementary information from other primary sources. (SCD) is difficult to study, as
1 David Pickham, Et Al. Optimizing QT Interval Measurement for the Preparation Screening of Young
Athletes, 2016. Pg 1745
Nilanduwa2
most researches only have access to postmortem specimens, predispositions that may cause
(SCD) can be asymptomatic and finding an athlete with heart complications near a controlled
Electrocardiogram is not likely. Thus, these issues contribute to the lack of congruity and
understanding of (SCD) of athletes in the scientific community. Therefore, (SCD) has been
linked to many causes, and the studies on (SCD) have the propensity to have conflicting
conclusions between unassociated research groups. Consequently, it is essential to understand
that by no means does this paper aim to cure, prevent, explain, or validate health afflictions. This
paper provides information to the public that is intended to be interpreted and internalized by the
audience’s own accord and is a tool that can be used to explore athlete (SCD) or strengthen an
understanding about the allusive condition.
II. Research Conducted in Various Studies
Study 1
In 1987, medical doctors Kussmaul and Laskey pioneered testing of atrial flow
concerning heart failure by measuring arterial flow wave reflections in a prospective study. Their
hypothesis was cardiovascular problems such as cardiomyopathy which increases vascular
resistance altering flow rate and wave function which ultimately taxes the heart. Doctors
Kussmaul and Laskey indicate that by having the heart increase its workload, heart failure
resulting in sudden death is possible. They derived wave reflection indexes from ascending aortic
pressure and velocity recordings in 17 subjects with clinical heart failure secondary to idiopathic
dilated cardiomyopathy and 11 control subjects free of detectable cardiovascular disease.
Patients were studied at rest as well as during submaximal supine bicycle exercise. Eight of the
subjects with cardiomyopathy were also studied during infusion of nitroprusside at rest and on
exercise. 2
Nilanduwa3
Methodology
Doctors Laskey and Kussmaul used multi-sensor manometers to derive left ventricle and
ascending aortic pressure. Consent was inquired and recorded by all participating subjects. The
17 subjects which consisted of 12 men and five women from the proximate ages between 24-66
with signs indicating cardiomyopathy were sent to their cardiac catheterization laboratory for
endomyocardial biopsy. The patients were dilated, hypokinetic left ventricle on M mode or
cross-sectional echocardiography. Each patient was in sinus rhythm, and ejection fraction from
the left ventricle was considered to reside within standard parameters. 3
These tests were to ensure that the patients were stable for at least two weeks for further
testing and to limit confounding variables caused by unaccounted for poor health. 11 of the
patients exemplified symptoms of cardiomyopathy and eight were previously diagnosed. The 11
patients that were not clinically diagnosed to have the disease were utilized as the control group.
To get a more precise reading on aortic pressure, pressure transducers were used. One transducer
was located 5 cm proximal to the tip (ventricular) of the other pressure transducer. Both
transducers were pre-calibrated against a mercury manometer for the highest accuracy. Both
doctors checked and ensured that both pressure transducers were equisensitive. An
electromagnetic flow probe was also used to record instantaneous aortic blood flow velocity.
Additionally, a floating balloon catheter was placed into the pulmonary artery to gauge cardiac
output. Patients were told to exercise on specially designed bikes for 20-30 minutes, and their
bodies positioned in a supine state and legs elevated. After some calculation were accounted for,
patients exercised for 3-5 minutes with a workload capacity of 300kg m/min. 5 of the 17
participants who were diagnosed with cardiomyopathy were given rest until their oxygen
2 Warren K. Laskey Et Al., Arterial Wave Reflection in Heart Failure, 1987. Pg 711
3 Warren K. Laskey Et Al., Arterial Wave Reflection in Heart Failure, 1987. Pg 712
Nilanduwa4
saturation, heart rate, aortic pressure, and left ventricle returned to acceptable testing parameters
within 10% of control values. Following, an intravenous fusion of nitroprusside was dosed at
0.25 ug/kg, min and increased every 5 minutes until there was a 10% reduction in mean aortic
pressure or 25% reduction in left ventricular end-diastolic pressure. Calculations were made, and
data was recorded on line-graphs for interpretation upon completion of testing. 4
Results
After testing, it was clear that forward flow reflection wave returns faster and amplitude
increases as cardio output increases. Flow reflection magnitude decreased with exercise,
however. Groups with cardiomyopathy had larger amplitudes and magnitudes relative to any
other group. Nitroprusside treated subjects had lower amplitude and considerably less vascular
resistance compared to the non-treated healthy group and the non-treated cardiomyopathy group.
The treated group also had a magnitude drop that was significant when resting but not as
substantial when exercise began. 5
Nevertheless, those patients on the vasodilator had an earlier return on the backward
wave and the smallest magnitude of all tested subjects. Therefore, the study resulted in exercise
taxing the heart but there is a significant more stress on a heart suffering from idiopathic
cardiomyopathy as the blood flow in impinged. This study demonstrates that a heart with an
ailment such as cardiomyopathy is more likely to stress the heart under a physically exhaustive/
situation which can lead to heart failure and sudden death.
Study 2
4 Warren K. Laskey Et Al., Arterial Wave Reflection in Heart Failure, 1987. Pg 713
5 Warren K. Laskey Et Al., Arterial Wave Reflection in Heart Failure, 1987. Pg 714
Nilanduwa5
A Stanford University School of Medicine cohort study about the topic of Long (QT)
disorder was published in 2016 by Pickham, Hsu, Soofi, Goldberg, Saini, Hadley, Perez, and
Froelicher. Long (QT) disorder is characterized by unusually rapid heart rhythm and is noted to
be another cause for (SCD). There are 15 genes linked to the congenital form for LQTS. QT
interval is the measure of time from the start of the Q wave and end of the T wave of a heart’s
electrical cycle. This study aimed to optimize estimation formulas to account for an uncorrected
QT interval. Correcting for a QT interval allows health professionals to gauge if a patient is at
increased risk for rapid heart rate that can contribute to the occurrence of (SCD). 6
These researchers collected electrocardiogram data for a total of 5 years with a goal of
determining an accurate estimator; the QT interval was juxtaposed to the traditional QT
correction formulas of Bazett, Friderecia, Hodges, and Framingham. Unfortunately, this interval
estimator formulas seemed inclined toward error. The Bazett correction formula was created
long ago in 1920 with only 50 subjects, and since then has been esteemed for systemic
measurement bias in athlete by overcorrecting at high heart rate and under-correcting at low
heart rate. Therefore, the researchers would deem the QT formula with the lowest mean slope as
the appropriate formula when conventional formulas noticeably did not establish realistic
estimations. 7
Methodology
6 David Pickham, Et Al. Optimizing QT Interval Measurement for the Preparation Screening of Young
Athletes, 2016. Pg 1745
7 David Pickham, Et Al. Optimizing QT Interval Measurement for the Preparation Screening of Young
Athletes, 2016. Pg 1746
Nilanduwa6
Researchers studied 2077 athlete’s ECG data that was customarily collected for sport
prescreening. The data was obtained in the United States of America from June 2010 to March
2015 and consent was inquired and authorized by subject signature. Three cohorts were tested
with high school athletes, collegiate athletes, and professional athletes. All ECG data was
recorded using CardeaScreen at high-resolution 16-s. Data recorded at 1kHz and bandpass
filtered between 0.05 and 150Hz. The CardeaScreen performs waveform analysis and interval
measurements. 8
Considering accuracy and potential for machine error, researchers plotted heart rate and
determined if plot points exceeded three standard deviations from the population mean QT
interval; existing data points that exceeded three standard deviations were deemed outliers.
Corrections were made using the same Global QT interval used for the automatic measures of
the CardeaScreen after careful inspection of outliers. Only 2% of ECG data need manual
correction. To determine the best fitting model, a regression analysis was performed, and a liner,
exponential, and power function model was developed for an uncorrected QT interval. Then to
test the appropriateness of each model the QTc intervals were calculated using the Bazett,
Friderecia, Hodges, and Framingham correction formulas. The 95th and the 99th percentile
measure of the uncorrected QT interval were affirmed and clustered by Heart Rate. Then
researchers used classification examination to compare Bazett corrected QT interval groups.