\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[ngerman,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{The majority of participants with suspected~ hypertrophic cardiomyopathy
documented during screening Echocardiography have a~ normal
electrocardiogram (EKG)~~~~~~~~}



\author[1]{Mohammad Reza Movahed}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 22, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup








\sloppy


\textbf{Introduction}

Hypertrophic cardiomyopathy (HCM) is the most common inherited
cardiovascular disease~\emph{(1,2)}~ and is the most frequent cause of
sudden cardiac death (SCD) in young individuals, particularly athletes
with a high level of training~\emph{(3--5)}.HCM is a prevalent
hereditary cardiovascular condition that affects one in 500 people in
the general population~\emph{(6,7)}. The cumulative proportion of sudden
cardiac death (SCD) events in childhood hypertrophic cardiomyopathy
(HCM) within five years of diagnosis ranged from 8\% to
10\%~\emph{(8,9)}. It is characterized by inadequate relaxation,
hypercontractility, reduced compliance, and left ventricle
hypertrophy~\emph{(10--12)}. HCM manifests as a chronic, progressive
illness that can have a severe, transformative effect on a person's life
and significantly lower quality of life. Data on the cost to society
associated with HCM has shown significant increases in all-cause
hospitalizations, hospital days, outpatient visits, and total healthcare
costs. The majority of cost increases can be attributed to increased
hospitalizations and hospital days among symptomatic
patients~\emph{(13)}. The most often reported symptoms include syncope,
palpitations, exertional dyspnea, shortness of breath, ankle swelling,
exhaustion, sense of disorientation, and lightheadedness~\emph{(14,15)}.

Among the estimated 700,000 patients with HCM, only 100,000 have been
diagnosed in the United States~\emph{(16)}. Underdiagnosis may be due in
part to challenges in the diagnosis of asymptomatic HCM patients, who
typically receive a diagnosis by chance or via systematic screening
efforts~\emph{(12)}. However, developments in the understanding of
genetic and phenotypic characteristics of HCM have promise for improving
the identification of the condition. Over the last twenty years, the
condition has been linked to abnormalities in genes that encode proteins
of the cardiomyocyte's contractile machinery~\emph{(6,17,18)}. It
appears that significant progress has been made in understanding the
illness from both a genetic and clinical standpoint~\emph{(19)}.

Despite new developments, HCM remains underdiagnosed. Although the
population prevalence of HCM is between 1:200 and 1:500, only 10--20\%
of cases are found by clinical means~\emph{(20)}. Patients with HCM can
have a normal life expectancy but a notable percentage can develop
HCM-related complications including heart failure, atrial fibrillation
(AF), and cardioembolic stroke, while a smaller percentage have SCD or
life-threatening ventricular arrhythmias~\emph{(21)}. SCD is the most
common cause of mortality among these patients and frequently occurs
during exercise. However, it often goes undetected until death, as many
individuals experience minimal or no significant symptoms~\emph{(6,11)}.
Consequently, a high index of diagnostic suspicion, accurate
identification, and a thorough clinical examination of patients and
family members are crucial for early identification and
treatment~\emph{(20)}. Identifying high-risk patients is crucial to
lowering the risk of SCD in young individuals with HCM, as effective
treatment has the potential to significantly reduce HCM mortality and
morbidity~\emph{(22,23)}~This can be achieved through exercise
limitation, medication therapies, and the use of implantable
cardioverter defibrillators (ICDs)~\emph{(24)}~. Therefore, there has
been considerable interest in improving diagnostic accuracy among HCM
patients, especially young patients, to inform intervention~\emph{(21)}.

The HCM diagnosis is based on imaging techniques, such as
echocardiography or cardiovascular magnetic resonance (CMR), that reveal
increasing LV wall thickness~\emph{(21)}. Thorough investigation has led
to a better comprehension of risk categorization for patients with HCM.
The latest European Society of Cardiology (ESC) guidelines propose
evaluating clinical examination, family history, 48-hour
electrocardiography (ECG), echocardiography, and exercise testing for
this purpose~\emph{(6,11)}. The European Society of Cardiology (ESC) has
proposed specific cardiac screening guidelines for young competitive
athletes~\emph{(25)}, which include assessing symptoms and family
medical history (e.g., premature death, HCM), conducting a physical
examination, and performing a resting 12-lead ECG. A recent Danish study
revealed that a large proportion of individuals who experienced SCD due
to HCM had previous symptoms, and most of them had sought medical
attention before their death, in contrast to the control
group~\emph{(26)}. These findings suggest there is an opportunity to
improve the identification of HCM among at-risk patients, as many
patients seek treatment.

The ECG continues to be a fundamental aspect of evaluating patients with
HCM. Moreover, it is experiencing a ''renaissance'' in the realm of
cardiomyopathies, not only due to its cost-effectiveness and widespread
accessibility, but also because it offers information pertinent to
morphology, function, and genetic foundation
simultaneously~\emph{(21)}~HCM has diagnosis so far relied on
identification of left ventricular hypertrophy (LVH) with a wall
thickness greater than 15 mm using echocardiography or CMR. However,
this degree of LVH is not exclusive to HCM and may stem from various
other pathological conditions, widening the differential. In such
instances, the ECG is highly valuable in assisting with the
differentiation between sarcomeric HCM and its phenocopies~\emph{(21)}.
There is a growing body of literature evaluating the accuracy of ECG
markers in predicting HCM, however, there remains a need for research on
the extent to which ECG findings are predictive of HCM identified on
echocardiography. Therefore, the aim of this study was to evaluate the
prevalence of abnormal ECG findings, including LVH, T wave inversion,
left bundle branch block (LBBB), and left atrial enlargement in
participants with suspected HCM detected during screening
echocardiography.



\textbf{Methods}

The Anthony Bates Foundation was established in memory of Anthony Bates
who suffered sudden cardiac death. The foundation has been performing
screening echocardiography and ECG in high schools across the United
States in order to prevent sudden death since 2001 by detecting
asymptomatic cardiac abnormalities. The evaluation includes a
questionnaire,~ physical examination, electrocardiogram, and
echocardiogram performed on-site. ~Informed consent is obtained from the
participant and participants younger than 18 needed consent from their
parents. The populations studied were volunteers screened for prevention
of sudden death. Although the screening was aimed at young subjects,
everybody willing to participate would be screened. Any suspected
abnormal echocardiographic or significant EKG findings will be
recommended for follow-up with the participant's physician. ~Data
analysis was performed using SPSS program. A p-value of less than 0.05
is accepted as statistically significant. Board-certified cardiologists
would interpret the results of EKG and echocardiograms. For our study,
we evaluated the prevalence of abnormal ECG in participants with
suspected HCM defined as any left ventricular wall thickness [?] 15 mm in
comparison to controls among subjects presenting for screening.

\textbf{Results}

The final analytic sample consisted of 682 subjects. Approximately 10\%
of the sample was Age distribution seen in Figure 1. African American,
with the remainder being of other race/ethnicity (Figure 2). Chi-square
tests were conducted to compare individuals with suspected HCM to
controls in rates of LVH, T wave inversion, LBBB, and left atrial
enlargement. Rates of ECG abnormalities across HCM patients and controls
are presented in Figure 3. Consistent with our hypotheses, our findings
indicated that rates of LVH were higher in patients with suspected HCM
(23.5\%) than in patients without suspected HCM (5.6\%) (\emph{p~} =
.002). Similarly, we found elevated rates of T wave inversion among
patients with suspected HCM (17.6\%) in comparison to controls (4.1\%)
(\emph{p} ~= .007). Comparisons of rates of LBBB and left atrial
abnormalities were unable to be conducted, as there were no patients
with suspected HCM who showed evidence of these abnormalities on ECG in
our sample. Examinations of the presence of any identified ECG
abnormality showed that less than 25\% of suspected HCM patients had an
ECG abnormality observed on screening, though rates of any abnormality
among HCM patients (23.5\%) remained significantly elevated relative to
controls (8.7\%) (\emph{p} ~= .036).

\textbf{Discussion}

This study aimed to examine the rates of ECG abnormalities among
patients with suspected HCM in comparison to controls in a sample of
individuals presenting for echocardiographic screening as part of the
Anthony Bates Foundation's screening programs. Our primary hypotheses
were supported, indicating that individuals with suspected HCM have
significantly higher rates of LVH and T wave inversion. Aggregated
analysis showed that HCM identified on echocardiography was also
associated with a significantly higher rate of any ECG abnormality
compared to controls. However, despite significant differences from
control subjects, rates of any ECG abnormality among HCM patients were
still low, at less than 25\%. Taken together, our findings suggest that
though HCM is associated with greater rates of ECG abnormalities
compared to controls, due to low rates of ECG abnormality among this
population, ECG alone is likely considerably lacking in sensitivity for
the identification of HCM. In sum, though ECG abnormalities may suggest
potential HCM, they are insufficient for successful prediction and
identification of the condition in the absence of additional
confirmatory markers.

Our findings are consistent with research demonstrating that ECG alone
has inadequate sensitivity for the diagnosis of HCM. However, some
studies have used automation to improve upon sensitivity in identifying
HCM. For example, one study discovered that an automated algorithm
exhibited 88.6\% sensitivity and 98\% specificity in identifying
patients with HCM based on ECG data. Another study concentrated on
classifying HCM patients through ECG analysis. These insights indicate
that while ECG can provide valuable information, it may not be
comprehensive enough for accurate HCM identification when used in
isolation~\emph{(27--29)}. Despite advancements, at present the
diagnosis of HCM still must involve a combination of tests including
echocardiography, genetic testing, and clinical evaluation.

However, some emerging research has shown that novel ECG techniques may
have the potential to improve predictive validity. For example, some
studies suggest that integrating ECG monitoring techniques with machine
learning and novel analytical strategies has the potential to improve
the predictive validity of ECG in identifying HCM. This advancement may
enhance the effectiveness of current therapies for individuals with this
condition. Furthermore, artificial intelligence-enhanced
electrocardiography (AI-ECG) has shown potential utility for the
diagnosis of HCM. These developments offer promise for improving the
management and diagnosis of individuals with HCM~\emph{(30,31)}.

At present, the gold standard for the identification of HCM is
echocardiography, specifically 2D echocardiography or CMR imaging for
adult patients~\emph{(12)}. These imaging techniques are crucial for
establishing a clinical diagnosis of HCM~\emph{(12)}. In cases where HCM
is suspected due to cardiac symptoms, an abnormal 12-lead ECG, or a
family history of inherited heart disease, and where the
echocardiographic examination is inconclusive, CMR imaging is a crucial
supplementary test for establishing a clear diagnosis~\emph{(32--38)}.
In such clinical scenarios, CMR imaging can pinpoint specific areas of
LVH, especially when hypertrophy is localized to particular regions of
the LV wall, such as the anterolateral wall, posterior septum, and apex.
The enhanced sensitivity of CMR imaging in detecting LVH is due to its
high spatial resolution and the absence of limitations caused by poor
acoustic windows resulting from pulmonary or thoracic
parenchyma~\emph{(35--37)}. Some studies state that the most predictive
modality for diagnosing HCM is CMR imaging. This imaging technique
offers detailed insights into the heart's structure and function,
enabling accurate evaluation of myocardial hypertrophy, fibrosis, and
other pathological changes linked to HCM. Its exceptional visualization
and characterization of the myocardium make it an invaluable tool for
diagnosing HCM and gauging its severity. Furthermore, CMR imaging aids
in risk assessment and assists in determining the most suitable
treatment approach for individuals with HCM~\emph{(33,39)}. Despite
advancements in ECG technology and novel approaches for the use of ECG
data via automation and artificial intelligence-based models,
echocardiography is still warranted for confirmation and CMR remains a
useful tool in cases of inconclusive echocardiography.

\textbf{Conclusion}

Our findings suggest that standard ECG is limited in its ability to
identify the condition. In our sample, the prevalence of abnormal ECG
among participants with HCM detected during screening echocardiography
was less than 25\%. This suggests that even in the presence of normal
ECG findings, there is value in additional workup for HCM via
echocardiography and CMR imaging to comprehensively rule out the
condition. It is especially important to conduct comprehensive screening
among high-level athletes, as these individuals are at increased risk
for SCD.

\textbf{Limitation:}

We evaluate volunteers in high schools with a relatively young
population. Therefore, our results may not be accurate for the older
population. Echocardiographic measurements are body habit dependent and
a hand-held echocardiography has limited accuracy in a participant with
poor window. Furthermore, other causes of increased left ventricular
thickness such as hypertension or infiltrative diseases could be
excluded limiting our results.

\textbf{References:}

1. Ackerman M, Atkins DL, Triedman JK. Sudden cardiac death in the
young. Circulation. 2016 Mar 8;133(10):1006--26.

2. McLeod CJ, Ackerman MJ, Nishimura RA, Tajik AJ, Gersh BJ, Ommen SR.
Outcome of patients with hypertrophic cardiomyopathy and a normal
electrocardiogram. J Am Coll Cardiol. 2009 Jul 14;54(3):229--33.

3. Montgomery JV, Harris KM, Casey SA, Zenovich AG, Maron BJ. Relation
of electrocardiographic patterns to phenotypic expression and clinical
outcome in hypertrophic cardiomyopathy. Am J Cardiol. 2005 Jul
15;96(2):270--5.

4. Maron BJ, Wolfson JK, Cir\selectlanguage{ngerman}ó E, Spirito P. Relation of
electrocardiographic abnormalities and patterns of left ventricular
hypertrophy identified by 2-dimensional echocardiography in patients
with hypertrophic cardiomyopathy. Am J Cardiol. 1983 Jan
1;51(1):189--94.

5. Shapiro LM, McKenna WJ. Distribution of left ventricular hypertrophy
in hypertrophic cardiomyopathy: a two-dimensional echocardiographic
study. J Am Coll Cardiol. 1983 Sep;2(3):437--44.

6. Maron BJ, Maron MS. Hypertrophic cardiomyopathy. Lancet. 2013 Jan
19;381(9862):242--55.

7. Maron BJ, Gardin JM, Flack JM, Gidding SS, Kurosaki TT, Bild DE.
Prevalence of hypertrophic cardiomyopathy in a general population of
young adults. Echocardiographic analysis of 4111 subjects in the CARDIA
Study. Coronary Artery Risk Development in (Young) Adults. Circulation.
1995 Aug 15;92(4):785--9.

8. Miron A, Lafreniere-Roula M, Steve Fan C-P, Armstrong KR, Dragulescu
A, Papaz T, et al. A validated model for sudden cardiac death risk
prediction in pediatric hypertrophic cardiomyopathy. Circulation. 2020
Jul 21;142(3):217--29.

9. Norrish G, Ding T, Field E, Ziólkowska L, Olivotto I, Limongelli G,
et al. Development of a Novel Risk Prediction Model for Sudden Cardiac
Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids). JAMA
Cardiol. 2019 Sep 1;4(9):918--27.

10. Marian AJ, Braunwald E. Hypertrophic cardiomyopathy: genetics,
pathogenesis, clinical manifestations, diagnosis, and therapy. Circ Res.
2017 Sep 15;121(7):749--70.

11. Authors/Task Force members, Elliott PM, Anastasakis A, Borger MA,
Borggrefe M, Cecchi F, et al. 2014 ESC Guidelines on diagnosis and
management of hypertrophic cardiomyopathy: the Task Force for the
Diagnosis and Management of Hypertrophic Cardiomyopathy of the European
Society of Cardiology (ESC). Eur Heart J. 2014 Oct 14;35(39):2733--79.

12. Ommen SR, Mital S, Burke MA, Day SM, Deswal A, Elliott P, et al.
2020 AHA/ACC guideline for the diagnosis and treatment of patients with
hypertrophic cardiomyopathy: executive summary: A report of the american
college of cardiology/american heart association joint committee on
clinical practice guidelines. J Am Coll Cardiol. 2020 Dec
22;76(25):3022--55.

13. Jain SS, Li SS, Xie J, Sutton MB, Fine JT, Edelberg JM, et al.
Clinical and economic burden of obstructive hypertrophic cardiomyopathy
in the United States. J Med Econ. 2021;24(1):1115--23.

14. Wang Y, Wang J, Zou Y, Bao J, Sun K, Zhu L, et al. Female sex is
associated with worse prognosis in patients with hypertrophic
cardiomyopathy in China. PLoS ONE. 2014 Jul 21;9(7):e102969.

15. Zaiser E, Sehnert AJ, Duenas A, Saberi S, Brookes E, Reaney M.
Patient experiences with hypertrophic cardiomyopathy: a conceptual model
of symptoms and impacts on quality of life. J Patient Rep Outcomes. 2020
Dec 1;4(1):102.

16. Maron MS, Hellawell JL, Lucove JC, Farzaneh-Far R, Olivotto I.
Occurrence of clinically diagnosed hypertrophic cardiomyopathy in the
united states. Am J Cardiol. 2016 May 15;117(10):1651--4.

17. Seidman JG, Seidman C. The genetic basis for cardiomyopathy: from
mutation identification to mechanistic paradigms. Cell. 2001 Feb
23;104(4):557--67.

18. Geisterfer-Lowrance AA, Kass S, Tanigawa G, Vosberg HP, McKenna W,
Seidman CE, et al. A molecular basis for familial hypertrophic
cardiomyopathy: a beta cardiac myosin heavy chain gene missense
mutation. Cell. 1990 Sep 7;62(5):999--1006.

19. Mande\selectlanguage{polish}ş \selectlanguage{english}L, Ro\selectlanguage{polish}ş\selectlanguage{english}ca M, Ciuperc\selectlanguage{polish}ă \selectlanguage{english}D, Popescu BA. The role of
echocardiography for diagnosis and prognostic stratification in
hypertrophic cardiomyopathy. J Echocardiogr. 2020 Sep;18(3):137--48.

20. Maron BJ, Desai MY, Nishimura RA, Spirito P, Rakowski H, Towbin JA,
et al. Diagnosis and Evaluation of Hypertrophic~Cardiomyopathy: JACC
State-of-the-Art Review. J Am Coll Cardiol. 2022 Feb 1;79(4):372--89.

21. Finocchiaro G, Sheikh N, Biagini E, Papadakis M, Maurizi N, Sinagra
G, et al. The electrocardiogram in the diagnosis and management of
patients with hypertrophic cardiomyopathy. Heart Rhythm. 2020
Jan;17(1):142--51.

22. Maron BJ. Clinical course and management of hypertrophic
cardiomyopathy. N Engl J Med. 2018 Aug 16;379(7):655--68.

23. Maron BJ, Rowin EJ, Casey SA, Maron MS. How hypertrophic
cardiomyopathy became a contemporary treatable genetic disease with low
mortality: shaped by 50 years of clinical research and practice. JAMA
Cardiol. 2016 Apr 1;1(1):98--105.

24. B\selectlanguage{ngerman}örjesson E, Svennblad B, Wisten A, Börjesson M, Stattin E-L.
Symptoms and ECG changes precede sudden cardiac death in hypertrophic
cardiomyopathy-A nationwide study among the young in Sweden. PLoS ONE.
2022 Sep 14;17(9):e0273567.

25. Corrado D, Pelliccia A, Bjørnstad HH, Vanhees L, Biffi A, Borjesson
M, et al. Cardiovascular pre-participation screening of young
competitive athletes for prevention of sudden death: proposal for a
common European protocol. Consensus Statement of the Study Group of
Sport Cardiology of the Working Group of Cardiac Rehabilitation and
Exercise Physiology and the Working Group of Myocardial and Pericardial
Diseases of the European Society of Cardiology. Eur Heart J. 2005
Mar;26(5):516--24.

26. Lynge TH, Risgaard B, Jabbari R, Glinge C, Bundgaard H, Maron B, et
al. Cardiac symptoms before sudden cardiac death caused by hypertrophic
cardiomyopathy: a nationwide study among the young in Denmark. Europace.
2016 Dec;18(12):1801--8.

27. Campbell MJ, Zhou X, Han C, Abrishami H, Webster G, Miyake CY, et
al. Electrocardiography screening for hypertrophic cardiomyopathy.
Pacing Clin Electrophysiol. 2016 Sep;39(9):944--50.

28. Rahman QA, Tereshchenko LG, Kongkatong M, Abraham T, Abraham MR,
Shatkay H. Identifying Hypertrophic Cardiomyopathy Patients by
Classifying Individual Heartbeats from 12-lead ECG Signals. Proceedings
(IEEE Int Conf Bioinformatics Biomed). 2014 Nov;2014:224--9.

29. Rodday AM, Triedman JK, Alexander ME, Cohen JT, Ip S, Newburger JW,
et al. Electrocardiogram screening for disorders that cause sudden
cardiac death in asymptomatic children: a meta-analysis. Pediatrics.
2012 Apr;129(4):e999-1010.

30. Krittanawong C, Rogers AJ, Johnson KW, Wang Z, Turakhia MP, Halperin
JL, et al. Integration of novel monitoring devices with machine learning
technology for scalable cardiovascular management. Nat Rev Cardiol. 2021
Feb;18(2):75--91.

31. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial
intelligence-enhanced electrocardiography in cardiovascular disease
management. Nat Rev Cardiol. 2021 Jul;18(7):465--78.

32. Maron MS, Maron BJ, Harrigan C, Buros J, Gibson CM, Olivotto I, et
al. Hypertrophic cardiomyopathy phenotype revisited after 50 years with
cardiovascular magnetic resonance. J Am Coll Cardiol. 2009 Jul
14;54(3):220--8.

33. Rickers C, Wilke NM, Jerosch-Herold M, Casey SA, Panse P, Panse N,
et al. Utility of cardiac magnetic resonance imaging in the diagnosis of
hypertrophic cardiomyopathy. Circulation. 2005 Aug 9;112(6):855--61.

34. Moon JCC, Fisher NG, McKenna WJ, Pennell DJ. Detection of apical
hypertrophic cardiomyopathy by cardiovascular magnetic resonance in
patients with non-diagnostic echocardiography. Heart. 2004
Jun;90(6):645--9.

35. Hindieh W, Weissler-Snir A, Hammer H, Adler A, Rakowski H, Chan RH.
Discrepant measurements of maximal left ventricular wall thickness
between cardiac magnetic resonance imaging and echocardiography in
patients with hypertrophic cardiomyopathy. Circ Cardiovasc Imaging. 2017
Aug;10(8).

36. Corona-Villalobos CP, Sorensen LL, Pozios I, Chu L, Eng J, Abraham
MR, et al. Left ventricular wall thickness in patients with hypertrophic
cardiomyopathy: a comparison between cardiac magnetic resonance imaging
and echocardiography. Int J Cardiovasc Imaging. 2016 Jun;32(6):945--54.

37. Bois JP, Geske JB, Foley TA, Ommen SR, Pellikka PA. Comparison of
maximal wall thickness in hypertrophic cardiomyopathy differs between
magnetic resonance imaging and transthoracic echocardiography. Am J
Cardiol. 2017 Feb 15;119(4):643--50.

38. Maron MS, Rowin EJ, Maron BJ. How to image hypertrophic
cardiomyopathy. Circ Cardiovasc Imaging. 2017 Jul;10(7).

39. Kamal MU, Riaz IB, Janardhanan R. Cardiovascular magnetic resonance
imaging in hypertrophic cardiomyopathy: Current state of the art.
Cardiol J. 2016 Apr 11;23(3):250--63.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1 Age distribution}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2: Race distribution.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3: Presence of abnormal EKG in participant with suspected
hypertrophic cardiomyopathy

\selectlanguage{english}
\FloatBarrier
\end{document}

