\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[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Cardiac Mechanics in Heart Failure with Preserved Ejection Fraction}



\author[1]{Karthik Seetharam}%
\author[2]{Partho Sengupta}%
\author[3]{Christopher Bianco}%
\affil[1]{Mount Sinai Hospital}%
\affil[2]{West Virginia University School of Medicine}%
\affil[3]{West Virginia University Heart and Vascular Institute}%


\vspace{-1em}



  
  \date{May 20, 2024}


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





\selectlanguage{english}
\begin{abstract}
Heart failure with preserved ejection fraction (HFpEF) is a complex
clinical entity associated with significant morbidity and mortality.
Common comorbidities including hypertension, coronary artery disease,
diabetes, chronic kidney disease, obesity, and increasing age predispose
to preclinical diastolic dysfunction that often progresses to frank
HFpEF. That said, clinical HFpEF is typically associated with some
degree of diastolic dysfunction or can occur in the absence of many
conventional diastolic dysfunction indices. The exact biologic links
between risk factors, structural changes, and clinical manifestations
are not clearly apparent. Innovative approaches including deformation
imaging have enabled deeper understanding of HFpEF cardiac mechanics
beyond conventional metrics. Furthermore, predictive analytics through
data driven platforms have allowed for a deeper understanding of HFpEF
phenotypes. This review focuses on the changes in cardiac mechanics that
occur through preclinical myocardial dysfunction to clinically apparent
HFpEF.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Heart Failure with preserved ejection fraction (HFpEF) is a complex
clinical entity associated with significant morbidity and mortality.
HFpEF is responsible for approximately half of all heart failure
hospitalizations, with mean survival similar to those with heart failure
with reduced ejection fraction\textsuperscript{1}. HFpEF typically
begins with risk factor exposure, which promotes abnormal cardiac
mechanics and preclinical myocardial dysfunction. Preclinical diastolic
dysfunction is present in approximately \selectlanguage{ngerman}¼ of the adult
population\textsuperscript{2}. A significant proportion of patients with
diastolic dysfunction go on to develop frank clinical heart failure.

The exact biologic links responsible for diastolic dysfunction and heart
failure are not clearly apparent, but can be attributed to a complex
interplay of various pathological conditions on background
aging\textsuperscript{3}. Until recently, myocardial determinants of
HFpEF were thought primarily related to impairments in left ventricular
relaxation and chamber stiffness. However, contemporary studies
recognize the contribution of subtle left and right ventricular systolic
dysfunction, as well as the impact of left atrial function and
pericardial compliance. Furthermore, myocardial dysfunction may
initially occur only during exercise and the static resting evaluation
may not fully characterize potential dynamic changes. Therefore,
assessment of cardiac mechanics during rest and exercise plays an
important role in diagnosis and prognostication \textsuperscript{4}.
This review focuses on the changes in cardiac mechanics that occur
through the spectrum of preclinical myocardial dysfunction into
clinically apparent HFpEF.

\textbf{Left Ventricular Mechanics}

Each myocardial layer contributes to longitudinal, circumferential, and
radial deformation. Longitudinal deformation is largely dependent on a
well functioning subendocardial fiber layer, while circumferential
deformation of the left ventricle (LV) is predominately dependent on the
subepicardial fiber layer\textsuperscript{5}. LV shearing forces occur
within the three-dimensional myofiber orientation causing the largest
shear force in the circumferential-longitudinal plane referred to as LV
twist or torsional deformation. The rotation of LV base and apex in
opposite directions, combined with twist shearing forces, are
responsible for translating a mere 15-20\% maximal myocyte length
shortening into an ultimate LV cavity volume reduction of over half.
These same shear forces are responsible for untwisting during diastolic
recoil. Left ventricular diastolic untwisting releases stored mechanical
energy and creates a vacuum effect facilitate movement of blood from the
left atrium (LA) to the LV.

The predominant myocardial mechanics of HFpEF or restrictive
cardiomyopathies involve subendocardial fiber dysfunction leading to
decrements in longitudinal strain. Epicardial layer derived
circumferential strain and torsional shear forces remain relatively
preserved or even increased to supranormal values early in the disease
course \textsuperscript{6}. These compensatory circumferential and twist
mechanics allow for preservation of ejection fraction. On the other
hand, constrictive pericarditis, a common HFpEF mimic, leads to a
reciprocal situation of decreased circumferential and torsion forces
while longitudinal forces are preserved \textsuperscript{7}. These
cardiac mechanics can serve as an adjunct to traditional
echocardiographic signs differentiating features of constrictive versus
restrictive physiology, as clinical presentation may be similar between
disease states.

\textbf{Preclinical Myocardial Dysfunction}

Traditional imaging biomarkers may be normal in early subclinical
disease, however reduced global longitudinal strain (GLS) related to
subendocardial dysfunction is common. (Figure 1). The prevalence of
reduced GLS in asymptomatic type 2 diabetes mellitus has been reported
to range from 37\% to 54\% \textsuperscript{8}. Impaired longitudinal
strain may be present prior to the onset of LV remodeling and
hypertrophy \textsuperscript{9}. Furthermore, reduced longitudinal
strain may exist despite normal or near normal conventional Doppler and
tissue Doppler-derived parameters of diastolic
function\textsuperscript{10}(Figure 2). Compensatory subepicardial
hypertrophy may develop in an effort to reduce subendocardial wall
stress and preserve ejection fraction \textsuperscript{11}. Despite
mild, resting impairment in longitudinal mechanics, those with
preclinical myocardial dysfunction typically augment longitudinal strain
to near normal values during exercise \textsuperscript{12}. Complex
interactions between protracted risk factor exposure and progressive
structural changes influence the progression from the asymptomatic phase
to symptomatic heart failure. High risk structural findings in
asymptomatic patients at elevated risk for progression to symptomatic
disease include elevated E/e', left ventricular hypertrophy (LVH), and
abnormal GLS\textsuperscript{12}.

\textbf{Clinical Heart Failure}

Left ventricular cardiac mechanics associated with symptomatic heart
failure are complex and often counterintuitive. Several investigators
have shown that impaired longitudinal strain is a frequent finding in
symptomatic HFpEF patients (Figure 2) and predictive of poor
outcomes\textsuperscript{13,14}. Although a deterioration in
longitudinal mechanics is common, global circumferential strain (GCS),
LV twist, and twist/untwist rates may remain normal or even increase to
supranormal values\textsuperscript{6} early in the disease course
(Figure 1). This allows for preservation of the ejection fraction via
compensatory contributions from the subepicardial layer to
circumferential and LV twist deformation. If subepicardial dysfunction
is present and compensation is not possible, ejection fraction
falls\textsuperscript{15}. Circumferential and twist deformation may
remain normal or increased early in the disease course. However, time to
peak twist and untwist are often prolonged and signals subtle
impairments of systolic and diastolic function\textsuperscript{16}.
Advanced HFpEF is characterized by a progressive deterioration of
twist/untwist mechanics, as well as circumferential, radial, and
longitudinal deformation\textsuperscript{17,18}.

Resting deformation can be used to predict invasive hemodynamics. In
HFpEF patients, the ratio of mitral E to global longitudinal strain rate
in isovolumic relaxation can predict left ventricular filling
pressure\textsuperscript{19}. Furthermore, the ratio of resting global
circumferential strain to global longitudinal strain can serve as
potential predictor of a pathologic rise in pulmonary capillary wedge
pressure during exercise \textsuperscript{20}.

Exercise imaging for diastolic function and cardiac mechanical
assessment can aid tremendously in the initial HFpEF diagnostic work-up.
Recent guidelines recommend functional exercise echocardiographic stress
testing in patients at intermediate likelihood of HFpEF following an
initial morphofunctional assessment\textsuperscript{21}. Despite
impaired resting longitudinal deformation, those with preclinical
disease can augment longitudinal mechanics during exertion to a greater
extent than those who have progressed to symptomatic heart failure. This
in part explains the initial onset of exertional symptoms alone in
patients transitioning from preclinical disease into frank clinical
heart failure \textsuperscript{22}. Furthermore, impaired GLS during
exercise has been independently associated with an increased occurrence
of all-cause mortality and HF hospitalizations\textsuperscript{23}.

\textbf{Left Atrial Mechanics}

Left atrial (LA) enlargement has long been recognized as a marker of
increased cardiovascular risk \textsuperscript{24}. However, impaired LA
phasic function evident by reduced strain occurs prior to LA enlargement
and may be an early marker of risk\textsuperscript{25}. Thin atrial
walls challenge deformation analysis, however a growing body of evidence
utilizing LA strain is accumulating and recent guidelines for
standardization of LA deformation have been
published\textsuperscript{26}.

The LA functions as a reservoir for pulmonary venous return during
ventricular systole, as a conduit during early ventricular filling, and
as a contractile booster pump that contributes to late ventricular
filling. Atrioventricular deformation are closely interrelated and
governed by the concepts of atrioventricular coupling. Reservoir
function is dependent on both on atrial volume and compliance, as well
as LV systolic longitudinal annular excursion. Conduit function is
dependent on the atrioventricular pressure gradient created during LV
diastolic recoil. Atrial contractile function is dependent on both
intrinsic pump function and late diastolic ventricular afterload.

\textbf{Preclinical Myocardial Dysfunction}

Preclinical LA dysfunction is characterized by reduced reservoir and
conduit function while atrial contractile function remains
normal\textsuperscript{27} or even increased\textsuperscript{28} (Figure
3). Early in the disease spectrum, compensatory enhanced LA contractile
function is necessary for enhanced late LV filling. LA deformation
abnormalities occur prior to clear LA anatomic remodeling. Impaired
reservoir phasic performance may occur in the absence of diastolic
dysfunction or ventricular remodeling\textsuperscript{25}. In patients
with normal LA size, impaired LA reservoir function is associated with
an increased risk of future heart failure
hospitalization\textsuperscript{29}. In at risk hypertensive patients,
decreased LA contractile function is strongly predictive of future
adverse cardiac events and death\textsuperscript{30}. Furthermore,
hypertension risk mitigation strategies including
renin-angiotensin-aldosterone system inhibitor institution leads to
improvements in LA strain\textsuperscript{31}.

\textbf{Clinical Heart Failure}

A growing body of literature supports the pivotal role of LA functional
failure leading to pulmonary congestion and symptom onset. Specifically,
atrial contractile failure to compensate for reservoir and/or conduit
dysfunction may lead to overt clinical heart failure\textsuperscript{32}
(Figures 3)\textbf{.} Atrial fibrillation leading to failure of
contractile compensation, may be large determinant of symptom onset, and
compensatory enhanced conduit function becomes essential in this
situation. Early exercise-induced symptoms may also be related to
conduit function failure in the setting of impaired reservoir function
\textsuperscript{33}. Advanced symptomatic HFpEF is characterized by an
eventual impairment in all three atrial functional
phases\textsuperscript{34} (Figures 2 and 3).

Hewing et al demonstrated that in patients with sinus rhythm, LA
reservoir, conduit, and contractile function all inversely correlated
with pulmonary capillary wedge pressure. However, LA contractile strain
could independently predict invasively measured pulmonary capillary
wedge pressure with a higher diagnostic accuracy than average E/E'
ratio\textsuperscript{35}. Furthermore, Lundberg et al found LA strain
to outperform the current guideline recommended algorithm incorporating
E/e', LA volume index, and max tricuspid regurgitation velocity in
determining invasively measured elevated pulmonary capillary wedge
pressure\textsuperscript{36}.

Impaired LA reservoir strain may provide enhanced diagnostic accuracy
beyond conventional echocardiographic measures to discriminate true
HFpEF from non-cardiac dyspnea \textsuperscript{37}. Impaired resting LA
reservoir strain is associated with decreased peak oxygen
consumption\textsuperscript{38}. LA reservoir strain normally increases
during exercise, but this typical exercise related augmentation is
blunted in symptomatic patients. Impaired LA reservoir augmentation with
exercise leads to right ventricular-pulmonary circulation uncoupling and
exercise ventilatory inefficiency\textsuperscript{39}. Furthermore,
resting LA reservoir strain may outperformed LV GLS in its diagnostic
utility and prognostic ability for prediction of heart failure
hospitalization\textsuperscript{38}.

\textbf{Right Heart Mechanics}

Right heart dysfunction is common throughout the clinical progression
from preclinical disease to overt heart failure (Figure 1). Almost half
of patients with HFpEF have right heart dysfunction as assessed by RV
longitudinal strain \textsuperscript{38}. HFpEF patients with prominent
right heart dysfunction may represent a particularly high risk
phenotype\textsuperscript{40}. Although far less data examining right
heart deformation in HFpEF are currently available, recommendations for
the standardization of right heart deformation imaging have been
published\textsuperscript{26} and future study will further our
understanding of right heart dysfunction.

Intact structure, function, and geometry of the interventricular septum
is of upmost importance in preserving RV function. Shearing forces of
the oblique interventricular septal fibers result in systolic twisting
of the base toward the apex, leading to RV longitudinal motion.
Interventricular septal mechanics are normally responsible for
approximately 80\% of RV systolic function. Circumferential compression
of the transversely oriented fibers of the RV free wall result in a
bellows motion, which normally contributes approximately 20\% of RV
systolic function\textsuperscript{41,42}. RV dysfunction initially
involves reduced longitudinal performance with a compensatory increase
in circumferential motion, therefore RV longitudinal strain may serve as
a sensitive tool for identifying early dysfunction.

Asymptomatic patients with metabolic disease commonly exhibit mild
reductions in RV longitudinal strain \textsuperscript{43,44}. With
disease progression into symptomatic heart failure, almost half of HFpEF
patients have RV dysfunction by deformation indices
\textsuperscript{38}(Figure 2). RV longitudinal strain and global
longitudinal early-diastolic~strain rate (RV-SRe) are both more
significantly impaired in patients with symptoms than in those with
preclinical disease \textsuperscript{45}. Right heart dysfunction may
occur as a result of resting pulmonary hypertension or exercise-induced
pulmonary hypertension. However, in some patients LV GLS may be a more
important predictor of impaired RV longitudinal function than pulmonary
arterial systolic pressure \textsuperscript{45}, suggesting a global
myocardial process as the driver of dysfunction as opposed to pulmonary
hypertension alone. Nonetheless, indices incorporating simultaneous RV
function and afterload provide a measure of RV performance and pulmonary
circulation coupling. Among patients with HFpEF, the ratio of RV
longitudinal strain to pulmonary artery systolic pressure independently
predicted the composite endpoint of all-cause death and HF
hospitalization, even after multivariate adjustment\textsuperscript{46}.

Impairment of right atrial deformation has also been appreciated in
HFpEF patients \textsuperscript{47}. Pulmonary hypertension is
associated with a decrement in right atrial strain\textsuperscript{48}.
Right atrial mechanics may prove an indicator of systemic venous
congestion and related cardiorenal dysfunction. A distinct HFpEF
phenogroup characterized by extensive cardiac remodeling with prominent
pulmonary hypertension and right heart failure
exists\textsuperscript{40}. This phenogroup more often includes elderly
patients with chronic kidney disease and appears to be at particularly
high risk.

\textbf{Potential of Machine Learning}

HFpEF is heterogeneous entity affected by multiple clinical variables
and structural alterations. Current methods resort to an oversimplified
approach for assessment and risk stratification \textsuperscript{4}.
This one size fits all approach has not proved applicable in clinical
practice. With the evolution of technology and computer capabilities,
artificial intelligence (AI) is opening new frontiers in cardiovascular
imaging. Machine learning (ML), a subset of AI, is ushering a new era in
cardiovascular imaging by expanding boundaries not limited by
conventional statistics \textsuperscript{49}. Unlike traditional
approaches, ML can decipher hidden patterns and extrapolate hidden
patterns within vast data matrices \textsuperscript{50,51}. This
technology can integrate diastolic indices and speckle tracking
echocardiography to offer innovative insights into
HFpEF\textsuperscript{52} (Figure 4).

Lancaster et al performed an unsupervised, hierarchical cluster analysis
of 866 patients with diastolic dysfunction graded using contemporary ASE
recommendations \textsuperscript{53}. Major adverse cardiovascular
events, hospitalization, and mortality were compared between
conventional and cluster-based categorizations. Survival analyses of
patients assessed by clustering algorithms showed improved prediction of
event-free survival by cluster analysis over diastolic grade
classifications for all-cause mortality and cardiac mortality.

Omar et al performed a cluster analysis of LA and LV mechanical
deformation parameters that resulted in a Doppler-independent phenotypic
characterization of diastolic function and provided a noninvasive
estimation of LV filling pressures \textsuperscript{54}. Speckle
tracking features independently clustered patients into three groups
with conventional parameters verifying increasing severity of
dysfunction and LV filling pressure. Subsequent investigations from the
same group lead to a refined ML model for assessing LV filling pressure
using fourteen speckle tracking variables \textsuperscript{55}. This
model correctly identified 80\% of patients with pulmonary capillary
wedge pressure \selectlanguage{english}[?]18 mm Hg,

ML has also been applied to resting and stress deformation imaging.
Tabassian et al explored the role of ML in analyzing LV long axis
mechanics during stress and rest in 100 patients including those with
HFpEF and healthy, hypertensive, and breathless control
subjects\textsuperscript{56}. A ML algorithm was used to model
spatiotemporal patterns of the speckle tracking traces and compare the
ML algorithm predictions with the clinical diagnoses. The ML algorithm
predicted symptoms with a high degree of accuracy and assigning subjects
into four phenotypic groups. ML incorporating strain rate, compared with
standard measurements, provided the greatest improvement in accuracy for
predicting symptoms and 6-min walk distance. Sanchez-Martinez et al also
utilized measurements of LV deformation at rest and exercise to examine
differences between HFpEF and healthy patients from the MEDIA study
(MEtabolic road to DIAstolic heart failure) \textsuperscript{57}. LV
long-axis myocardial velocity patterns analyzed using an unsupervised ML
algorithm identified a continuum from health to disease, including a
transition zone associated with an uncertain diagnosis. As we move
forward in the current era of cardiovascular imaging, ML algorithms will
be increasingly integrated into clinical practice and cardiovascular
research. These methods may help detect previously unrecognized
phenotypes and tailor individualized therapies \textsuperscript{4}.

\textbf{Conclusion}

HFpEF is a complex clinical entity mediated by multiple clinical factors
leading to various structural alterations. Risk factor exposure leads to
an evolution of abnormal myocardial mechanics in each cardiac chamber.
Initially, many deformation parameters only deteriorate during exercise.
Although common mechanical changes are often present, significant
heterogeneity in structure and function may exist. In addition to
multiple imaging biomarkers, heterogeneity of clinical features and
associated medical conditions compound this syndromes complexity.
Current methods resort to an oversimplified approach for assessment and
risk stratification. With the evolution of technology and computer
capabilities, artificial intelligence (AI) is opening new frontiers in
cardiovascular imaging. This technology can be integrated with diastolic
indices and speckle tracking echocardiography to offer innovative
insights into HFpEF \textsuperscript{52}.

\textbf{References}

1. Shah KS, Xu H, Matsouaka RA, et al. Heart Failure With Preserved,
Borderline, and Reduced Ejection Fraction: 5-Year Outcomes. \emph{J Am
Coll Cardiol.} 2017;70(20):2476-2486 DOI:10.1016/j.jacc.2017.08.074.
PubMed PMID: 29141781.

2. Wan SH, Vogel MW, Chen HH. Pre-clinical diastolic dysfunction.\emph{J
Am Coll Cardiol.} 2014;63(5):407-416 DOI:10.1016/j.jacc.2013.10.063.
PubMed PMID: 24291270.

3. Lee DS, Gona P, Vasan RS, et al. Relation of disease pathogenesis and
risk factors to heart failure with preserved or reduced ejection
fraction: insights from the framingham heart study of the national
heart, lung, and blood institute.
\emph{Circulation.}2009;119(24):3070-3077
DOI:10.1161/circulationaha.108.815944. PubMed PMID: 19506115.

4. Bianco CM, Farjo PD, Ghaffar YA, Sengupta PP. Myocardial Mechanics in
Patients With Normal LVEF and Diastolic Dysfunction. \emph{JACC
Cardiovasc Imaging.} 2020;13(1 Pt 2):258-271
DOI:10.1016/j.jcmg.2018.12.035. PubMed PMID: 31202770.

5. Sengupta PP, Tajik AJ, Chandrasekaran K, Khandheria BK. Twist
mechanics of the left ventricle: principles and application. \emph{JACC
Cardiovasc Imaging.} 2008;1(3):366-376 DOI:10.1016/j.jcmg.2008.02.006.
PubMed PMID: 19356451.

6. Wang J, Khoury DS, Yue Y, Torre-Amione G, Nagueh SF. Preserved left
ventricular twist and circumferential deformation, but depressed
longitudinal and radial deformation in patients with diastolic heart
failure. \emph{Eur Heart J.} 2008;29(10):1283-1289
DOI:10.1093/eurheartj/ehn141. PubMed PMID: 18385117.

7. Sengupta PP, Krishnamoorthy VK, Abhayaratna WP, et al. Disparate
patterns of left ventricular mechanics differentiate constrictive
pericarditis from restrictive cardiomyopathy. \emph{JACC: Cardiovascular
imaging.} 2008;1(1):29-38.

8. Wang Y, Marwick TH. Update on Echocardiographic Assessment in
Diabetes Mellitus. \emph{Curr Cardiol Rep.} 2016;18(9):85
DOI:10.1007/s11886-016-0759-0. PubMed PMID: 27443381.

9. Szelenyi Z, Fazakas A, Szenasi G, et al. The mechanism of reduced
longitudinal left ventricular systolic function in hypertensive patients
with normal ejection fraction. \emph{Journal of
hypertension.}2015;33(9):1962-1969.

10. Ernande L, Bergerot C, Rietzschel ER, et al. Diastolic dysfunction
in patients with type 2 diabetes mellitus: is it really the first marker
of diabetic cardiomyopathy? \emph{J Am Soc
Echocardiogr.}2011;24(11):1268-1275.e1261
DOI:10.1016/j.echo.2011.07.017. PubMed PMID: 21907542.

11. Zhang J. Myocardial energetics in cardiac hypertrophy. \emph{Clin
Exp Pharmacol Physiol.} 2002;29(4):351-359
DOI:10.1046/j.1440-1681.2002.03657.x. PubMed PMID: 11985549.

12. Kosmala W, Marwick TH. Asymptomatic Left Ventricular Diastolic
Dysfunction: Predicting Progression to Symptomatic Heart
Failure.\emph{JACC Cardiovasc Imaging.} 2020;13(1 Pt 2):215-227
DOI:10.1016/j.jcmg.2018.10.039. PubMed PMID: 31005530.

13. Shah AM, Claggett B, Sweitzer NK, et al. Prognostic Importance of
Impaired Systolic Function in Heart Failure With Preserved Ejection
Fraction and the Impact of Spironolactone.
\emph{Circulation.}2015;132(5):402-414
DOI:10.1161/circulationaha.115.015884. PubMed PMID: 26130119.

14. Morris DA, Ma XX, Belyavskiy E, et al. Left ventricular longitudinal
systolic function analysed by 2D speckle-tracking echocardiography in
heart failure with preserved ejection fraction: a
meta-analysis.\emph{Open Heart.} 2017;4(2):e000630
DOI:10.1136/openhrt-2017-000630. PubMed PMID: 29018535.

15. Sengupta PP, Narula J. Reclassifying heart failure: predominantly
subendocardial, subepicardial, and transmural. \emph{Heart Fail
Clin.}2008;4(3):379-382 DOI:10.1016/j.hfc.2008.03.013. PubMed PMID:
18598989.

16. Stohr EJ, Shave RE, Baggish AL, Weiner RB. Left ventricular twist
mechanics in the context of normal physiology and cardiovascular
disease: a review of studies using speckle tracking
echocardiography.\emph{Am J Physiol Heart Circ Physiol.}
2016;311(3):H633-644 DOI:10.1152/ajpheart.00104.2016. PubMed PMID:
27402663.

17. Yip GW, Zhang Q, Xie JM, et al. Resting global and regional left
ventricular contractility in patients with heart failure and normal
ejection fraction: insights from speckle-tracking
echocardiography.\emph{Heart.} 2011;97(4):287-294
DOI:10.1136/hrt.2010.205815. PubMed PMID: 21193686.

18. Nguyen JS, Lakkis NM, Bobek J, Goswami R, Dokainish H. Systolic and
diastolic myocardial mechanics in patients with cardiac disease and
preserved ejection fraction: impact of left ventricular filling
pressure. \emph{J Am Soc Echocardiogr.} 2010;23(12):1273-1280
DOI:10.1016/j.echo.2010.09.008. PubMed PMID: 20970305.

19. Hatipoglu S, Ozdemir N, Babur Guler G, et al. Prediction of elevated
left ventricular filling pressures in patients with preserved ejection
fraction using longitudinal deformation indices of the left
ventricle.\emph{Eur Heart J Cardiovasc Imaging.} 2015;16(10):1154-1161
DOI:10.1093/ehjci/jev063. PubMed PMID: 25896356.

20. Biering-Sorensen T, Santos M, Rivero J, et al. Left ventricular
deformation at rest predicts exercise-induced elevation in pulmonary
artery wedge pressure in patients with unexplained dyspnoea. \emph{Eur J
Heart Fail.} 2017;19(1):101-110 DOI:10.1002/ejhf.659. PubMed PMID:
27878925.

21. Pieske B, Tschope C, de Boer RA, et al. How to diagnose heart
failure with preserved ejection fraction: the HFA-PEFF diagnostic
algorithm: a consensus recommendation from the Heart Failure Association
(HFA) of the European Society of Cardiology (ESC). \emph{Eur Heart
J.}2019;40(40):3297-3317 DOI:10.1093/eurheartj/ehz641. PubMed PMID:
31504452.

22. Kosmala W, Rojek A, Przewlocka-Kosmala M, Mysiak A, Karolko B,
Marwick TH. Contributions of Nondiastolic Factors to Exercise
Intolerance in Heart Failure With Preserved Ejection Fraction. \emph{J
Am Coll Cardiol.} 2016;67(6):659-670 DOI:10.1016/j.jacc.2015.10.096.
PubMed PMID: 26868691.

23. Wang J, Fang F, Wai-Kwok Yip G, et al. Left ventricular long-axis
performance during exercise is an important prognosticator in patients
with heart failure and preserved ejection fraction. \emph{Int J
Cardiol.} 2015;178:131-135 DOI:10.1016/j.ijcard.2014.10.130. PubMed
PMID: 25464236.

24. Tsang TS, Barnes ME, Gersh BJ, Bailey KR, Seward JB. Left atrial
volume as a morphophysiologic expression of left ventricular diastolic
dysfunction and relation to cardiovascular risk burden. \emph{The
American journal of cardiology.} 2002;90(12):1284-1289.

25. Cameli M, Lisi M, Righini FM, et al. Left atrial strain in patients
with arterial hypertension. Paper presented at: International
Cardiovascular Forum Journal2013.

26. Badano LP, Kolias TJ, Muraru D, et al. Standardization of left
atrial, right ventricular, and right atrial deformation imaging using
two-dimensional speckle tracking echocardiography: a consensus document
of the EACVI/ASE/Industry Task Force to standardize deformation
imaging.\emph{European Heart Journal-Cardiovascular
Imaging.}2018;19(6):591-600.

27. Fang NN, Sui DX, Yu JG, et al. Strain/strain rate imaging of
impaired left atrial function in patients with metabolic
syndrome.\emph{Hypertens Res.} 2015;38(11):758-764
DOI:10.1038/hr.2015.76. PubMed PMID: 26178155.

28. Brecht A, Oertelt-Prigione S, Seeland U, et al. Left Atrial Function
in Preclinical Diastolic Dysfunction: Two-Dimensional Speckle-Tracking
Echocardiography-Derived Results from the BEFRI Trial. \emph{J Am Soc
Echocardiogr.} 2016;29(8):750-758 DOI:10.1016/j.echo.2016.03.013. PubMed
PMID: 27156904.

29. Morris DA, Belyavskiy E, Aravind-Kumar R, et al. Potential
Usefulness and Clinical Relevance of Adding Left Atrial Strain to Left
Atrial Volume Index in the Detection of Left Ventricular Diastolic
Dysfunction. \emph{JACC Cardiovasc Imaging.} 2018;11(10):1405-1415
DOI:10.1016/j.jcmg.2017.07.029. PubMed PMID: 29153567.

30. Kaminski M, Steel K, Jerosch-Herold M, et al. Strong cardiovascular
prognostic implication of quantitative left atrial contractile function
assessed by cardiac magnetic resonance imaging in patients with chronic
hypertension. \emph{Journal of Cardiovascular Magnetic
Resonance.}2011;13(1):42.

31. Dimitroula H, Damvopoulou E, Giannakoulas G, et al. Effects of
renin--angiotensin system inhibition on left atrial function of
hypertensive patients: an echocardiographic tissue deformation imaging
study. \emph{American journal of hypertension.} 2010;23(5):556-561.

32. Sanchis L, Gabrielli L, Andrea R, et al. Left atrial dysfunction
relates to symptom onset in patients with heart failure and preserved
left ventricular ejection fraction. \emph{Eur Heart J Cardiovasc
Imaging.} 2015;16(1):62-67 DOI:10.1093/ehjci/jeu165. PubMed PMID:
25187609.

33. von Roeder M, Rommel K-P, Kowallick JT, et al. Influence of left
atrial function on exercise capacity and left ventricular function in
patients with heart failure and preserved ejection
fraction.\emph{Circulation: Cardiovascular Imaging.} 2017;10(4):e005467.

34. Santos AB, Kraigher\selectlanguage{english}-Krainer E, Gupta DK, et al. Impaired left atrial
function in heart failure with preserved ejection
fraction.\emph{European journal of heart failure.}
2014;16(10):1096-1103.

35. Hewing B, Theres L, Spethmann S, Stangl K, Dreger H, Knebel F. Left
atrial strain predicts hemodynamic parameters in cardiovascular
patients. \emph{Echocardiography.} 2017;34(8):1170-1178.

36. Lundberg A, Johnson J, Hage C, et al. Left atrial strain improves
estimation of filling pressures in heart failure: a simultaneous
echocardiographic and invasive haemodynamic study. \emph{Clinical
Research in Cardiology.} 2019;108(6):703-715.

37. Reddy YNV, Obokata M, Egbe A, et al. Left atrial strain and
compliance in the diagnostic evaluation of heart failure with preserved
ejection fraction. \emph{Eur J Heart Fail.} 2019;21(7):891-900
DOI:10.1002/ejhf.1464. PubMed PMID: 30919562.

38. Freed BH, Daruwalla V, Cheng JY, et al. Prognostic Utility and
Clinical Significance of Cardiac Mechanics in Heart Failure With
Preserved Ejection Fraction: Importance of Left Atrial Strain.\emph{Circ
Cardiovasc Imaging.} 2016;9(3) DOI:10.1161/circimaging.115.003754.
PubMed PMID: 26941415.

39. Sugimoto T, Bandera F, Generati G, Alfonzetti E, Bussadori C, Guazzi
M. Left Atrial Function Dynamics During Exercise in Heart Failure:
Pathophysiological Implications on the Right Heart and Exercise
Ventilation Inefficiency. \emph{JACC Cardiovasc Imaging.} 2017;10(10 Pt
B):1253-1264 DOI:10.1016/j.jcmg.2016.09.021. PubMed PMID: 28017395.

40. Shah SJ, Katz DH, Selvaraj S, et al. Phenomapping for novel
classification of heart failure with preserved ejection
fraction.\emph{Circulation.} 2015;131(3):269-279
DOI:10.1161/circulationaha.114.010637. PubMed PMID: 25398313.

41. Buckberg G, Hoffman JI, Nanda NC, Coghlan C, Saleh S, Athanasuleas
C. Ventricular torsion and untwisting: further insights into mechanics
and timing interdependence: a viewpoint.
\emph{Echocardiography.}2011;28(7):782-804.

42. Buckberg G, Hoffman JI. Right ventricular architecture responsible
for mechanical performance: unifying role of ventricular
septum.\emph{The Journal of thoracic and cardiovascular
surgery.}2014;148(6):3166-3171. e3164.

43. Kosmala W, Przewlocka-Kosmala M, Mazurek W. Subclinical right
ventricular dysfunction in diabetes mellitus--an ultrasonic
strain/strain rate study. \emph{Diabet Med.} 2007;24(6):656-663
DOI:10.1111/j.1464-5491.2007.02101.x. PubMed PMID: 17367309.

44. Wong CY, O'Moore-Sullivan T, Leano R, Hukins C, Jenkins C, Marwick
TH. Association of subclinical right ventricular dysfunction with
obesity. \emph{Journal of the American College of
Cardiology.}2006;47(3):611-616.

45. Morris DA, Gailani M, Vaz Perez A, et al. Right ventricular
myocardial systolic and diastolic dysfunction in heart failure with
normal left ventricular ejection fraction. \emph{J Am Soc
Echocardiogr.}2011;24(8):886-897 DOI:10.1016/j.echo.2011.04.005. PubMed
PMID: 21601421.

46. Bosch L, Lam CSP, Gong L, et al. Right ventricular dysfunction in
left-sided heart failure with preserved versus reduced ejection
fraction. \emph{Eur J Heart Fail.} 2017;19(12):1664-1671
DOI:10.1002/ejhf.873. PubMed PMID: 28597497.

47. Jain S, Kuriakose D, Edelstein I, et al. Right atrial phasic
function in heart failure with preserved and reduced ejection
fraction.\emph{JACC: Cardiovascular Imaging.} 2019;12(8 Part
1):1460-1470.

48. Deschle HA, Amenabar A, Casso NA, et al. Behavior of right atrial
strain in high systolic pulmonary artery
pressure.\emph{Echocardiography.} 2018;35(10):1557-1563.

49. Seetharam K, Shresthra S, Mills JD, Sengupta PP. Artificial
Intelligence in Nuclear Cardiology: Adding Value to
Prognostication.\emph{Current Cardiovascular Imaging Reports.}
2019;12(5) DOI:10.1007/s12410-019-9490-8.

50. Seetharam K, Kagiyama N, Sengupta PP. Application of mobile health,
telemedicine and artificial intelligence to echocardiography. \emph{Echo
Res Pract.} 2019;6(2):R41-R52 DOI:10.1530/ERP-18-0081. PubMed PMID:
30844756.

51. Seetharam K, Shrestha S, Sengupta P. Artificial Intelligence in
Cardiac Imaging. \emph{US Cardiology Review.} 2020;13:110-116
DOI:10.15420/usc.2019.19.2.

52. Narula J. Are we up to speed?: from big data to rich insights in CV
imaging for a hyperconnected world. \emph{JACC Cardiovasc
Imaging.}2013;6(11):1222-1224 DOI:10.1016/j.jcmg.2013.09.007. PubMed
PMID: 24229779.

53. Lancaster MC, Salem Omar AM, Narula S, Kulkarni H, Narula J,
Sengupta PP. Phenotypic Clustering of Left Ventricular Diastolic
Function Parameters: Patterns and Prognostic Relevance. \emph{JACC
Cardiovasc Imaging.} 2019;12(7 Pt 1):1149-1161
DOI:10.1016/j.jcmg.2018.02.005. PubMed PMID: 29680357.

54. Omar AMS, Narula S, Abdel Rahman MA, et al. Precision Phenotyping in
Heart Failure and Pattern Clustering of Ultrasound Data for the
Assessment of Diastolic Dysfunction. \emph{JACC Cardiovasc
Imaging.}2017;10(11):1291-1303 DOI:10.1016/j.jcmg.2016.10.012. PubMed
PMID: 28109936.

55. Salem Omar AM, Shameer K, Narula S, et al. Artificial
Intelligence-Based Assessment of Left Ventricular Filling Pressures From
2-Dimensional Cardiac Ultrasound Images. \emph{JACC Cardiovasc
Imaging.}2018;11(3):509-510 DOI:10.1016/j.jcmg.2017.05.003. PubMed PMID:
28734920.

56. Tabassian M, Sunderji I, Erdei T, et al. Diagnosis of Heart Failure
With Preserved Ejection Fraction: Machine Learning of Spatiotemporal
Variations in Left Ventricular Deformation. \emph{J Am Soc
Echocardiogr.} 2018;31(12):1272-1284.e1279
DOI:10.1016/j.echo.2018.07.013. PubMed PMID: 30146187.

57. Sanchez-Martinez S, Duchateau N, Erdei T, et al. Machine Learning
Analysis of Left Ventricular Function to Characterize Heart Failure With
Preserved Ejection Fraction. \emph{Circ Cardiovasc
Imaging.}2018;11(4):e007138 DOI:10.1161/circimaging.117.007138. PubMed
PMID: 29661795.

Figure 1 Temporal Evolution of Myocardial Mechanics

*modified from Bianco et al. J Am Coll Cardiol Img 2020;13:258--71

LV = left ventricular, LA = left atrial, RV = right ventricular

Figure 2 Parameters of Myocardial Mechanics in HFpEF

A. Impairment of left ventricular global longitudinal strain and
mechanical dispersion are common HFpEF mechanical changes. B. Reductions
in left atrial reservoir, conduit, and contractile function commonly
encountered in symptomatic HFpEF. C. HFpEF associated impairment of
right ventricular free wall and four chamber deformation. D. Despite
normal or near normal tissue Doppler-derived parameters of diastolic
function, significant deteriorations of mechanical deformation may
exist. GSL\_Endo\_Peak\_Avg = Peak endocardial global longitudinal
strain average, LASr\_ED = Left atrial reservoir strain at end-diastole,
LAScd\_ED = Left atrial conduit strain at end-diastole, LAct\_ED = Left
atrial contractile strain at end-diastole, RVFWSL = Right ventricular
free wall longitudinal strain, RV4CSL = Right ventricular 4 chamber
longitudinal strain

Figure 3 Temporal Evolution of Left Atrial Failure

Preclinical dysfunction is associated with a reduction in reservoir and
conduit function, while relative contractile function remains normal or
increased. Atrial contractile failure, along with further reductions in
reservoir and conduit function, are associated with symptom onset.
Advanced symptomatic HFpEF is characterized by an eventual decrement in
all 3 atrial strain phases.

Figure 4 Clustering Dendrograms for Conventional Variables and Their STE
Correspondents

*reprinted with permission from \textsuperscript{54} Omar et al J Am
Coll Cardiol Img.2017:10(11):1291-1303

Clustering dendrograms using STE and conventional variables together.
The dissimilarity matrix is given as a heat map of Euclidean distance
(red). The AU (red numbers) and BP (green numbers) were calculated. AU
values are shown only for leaflets that had an AU\textgreater{}95\%
(considered statistically significant). Significant proximity of
variables in the clustering leaflets were decided using 2D-LAVmax, E/e',
A-wave velocity, and a' velocity were shown to be in perfect proximity
with their STE counterparts STE-L\textsubscript{AV} max,
VR-E/SR-E\textsubscript{AV}, VR-A\textsubscript{AV}, and
SR-A\textsubscript{AV}, respectively (AU = 97\%, 96\%, 98\%, and 100\%,
respectively). The conventional parameters e'/a' and E/A were also in
significant proximity to their STE counterparts
SR-E/SR-A\textsubscript{AV} and VR-E/VR-A\textsubscript{AV},
respectively (AU = 98\%) and also between e' and s' and their STE
counterparts SR-E\textsubscript{AV} and SR-S\textsubscript{AV},
respectively (AU = 100\%). STE = speckle tracking echocardiogram, E =
pulsed Doppler derived mitral flow early diastolic velocity; E' = tissue
Doppler derived mitral annular early diastolic velocity; E/A = Doppler
derived mitral flow early to late diastolic velocity ratio; E/e' = ratio
of Doppler derived mitral flow early diastolic velocity to tissue
Doppler derived mitral annular early diastolic velocity; LAV = left
atrial maximum volume; AU = approximately unbiased probability; 2D =
2-dimensional; VR-E = rate of volume expansion at early diastole,
SR-A\textsubscript{AV} = peak atrioventricular strain rate during atrial
contraction; SR-E\textsubscript{AV} = early diastolic peak
atrioventricular strain rate; SRE/SRA\textsubscript{AV} = ratio between
atrioventricular strain rate at early diastole and during atrial
contraction; TLVd = total left heart volume during ventricular diastole;
TLVs = total left heart volume during ventricular systole;
VR-A\textsubscript{AV} = peak atrioventricular volume expansion rate at
left atrial contraction; VR-E\textsubscript{AV} = early diastolic peak
atrioventricular volume expansion rate; VRE/SRE\textsubscript{AV} =
ratio between atrioventricular volume expansion rate and strain rate at
early diastole; VRE-VRA\textsubscript{AV} = ratio between
atrioventricular volume expansion rate at early diastole and during
atrial contraction.

Figure 1 Temporal Evolution of Myocardial Mechanics\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 2 Parameters of Myocardial Mechanics in HFpEF\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 3 Temporal Evolution of Left Atrial Failure\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 4 Clustering Dendrograms for conventional variables and their
Speckle Tracking Correspondents\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

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

