Longitudinal wall fractional shortening: an M-mode index based on mitral annular plane systolic excursion (MAPSE) that correlates and predicts left ventricular longitudinal strain (LVLS) in intensive care patients
Huang et al. Critical Care
Longitudinal wall fractional shortening: an M-mode index based on mitral annular plane systolic excursion (MAPSE) that correlates and predicts left ventricular longitudinal strain (LVLS) in intensive care patients
Stephen J. Huang 0
Iris Ting 2
Andrea M. Huang 1
Michel Slama 0 3
Anthony S. McLean 0
0 Department of Intensive Care Medicine, Nepean Hospital, University of Sydney , Sydney, NSW 2747 , Australia
1 Sydney Medical Program, Nepean Clinical School, University of Sydney , Sydney, NSW , Australia
2 Cardiovascular Ultrasound Laboratory, Nepean Hospital , Sydney, NSW , Australia
3 Unité de réanimation médicale CHU Sud Amiens, and unité INSERM 1088, UPJV , Amiens , France
Background: Left ventricular longitudinal strain (LVLS) is a modern measurement for LV function. However, strain measurement is often difficult in critically ill patients. We sought to show LVLS can be estimated using M-mode-derived longitudinal wall fractional shortening (LWFS), which is less dependent on image quality and is easier to perform in critically ill patients. Methods: Transthoracic echocardiographic records were retrospectively screened and 80 studies suitable for strain and M-mode measurements in the apical 4-chamber view were selected. Longitudinal wall fractional shortening was derived from conventional M-mode (LWFS) and curved anatomical M-mode (CAMMFS). The relationships between LVLS and mitral annular plane systolic excusion (MAPSE) and M-mode-derived fractional shortening were examined using univariate generalized linear model in a training set (n = 50) and was validated in a separate validation set (n = 30). Results: MAPSE, CAMMFS, and LWFS demonstrated very good correlations with LVLS (r = 0.852, 0.875 and 0.909, respectively). LWFS was the best unbiased predictor for LVLS (LVLS = 1.180 x LWFS - 0.737, P < 0.001). Intra- and inter-rater agreement and reliability for LWFS measurement were good. Conclusions: LVLS can be estimated by LWFS in the critically ill patients. It provides a fast and accurate prediction of LVLS. LWFS is a reproducible and reliable measurement which can be used as a potential index in place of LVLS in the critically ill population.
Left ventricular function; MAPSE; M-mode; Longitudinal strain; Speckle tracking
Global longitudinal strain (GLS) is a modern clinical
utility that has superior sensitivity in detecting early
cardiac dysfunction before clinical manifestations [
For example, strain was able to identify impaired
ventricular function in patients with early septic shock
but preserved ejection fractions . GLS can also be
used for predicting outcomes in patients with heart
failure and myocardial infarction [
transthoracic echocardiographic (TTE) images are often
suboptimal for strain measurement in the critically ill
patients. To be a useful left ventricular (LV) systolic
function marker in the critical care setting, the marker should
be easily obtained even if the image quality is suboptimal.
Myocardial strain is most commonly defined as
“deformation of the myocardium” and is usually
measured by speckle-tracking echocardiography in modern
]. To many critical care physicians, the
meaning of “strain” (a negative number) and
“deformation” is not as intuitive as other traditional indices such
as ejection fraction and dP/dt. Further, different
definitions of GLS are adopted by different researchers and
vendors, adding further complexity to interpretations
and usage. For example, some define GLS as the change
in length for the “entire U-shaped length of LV”, whilst
others define GLS as the average of the 17 segments [
Although mathematical definition of longitudinal strain
is often quoted as “change in length divided by its
original length” in the literature, the fact that the definition
is simply an expression of fractional shortening is less
Mitral annular plane systolic excursion (MAPSE) is a
reliable marker for LV systolic function and is less
dependent on image quality [
]. We proposed that
LV longitudinal strain (LVLS) can be understood as
longitudinal wall fractional shortening (LWFS) (in
percentage), i.e. total MAPSE normalized by the LV
length. We further proposed that LWFS correlates
with and predicts LVLS.
Echocardiographic strain is defined as the percentage
change in length or width. From Fig. 1a, Led is the
ventricular length for the entire U-shaped LV at
enddiastole and Les is the LV length at end-systole (Fig. 1).
LVLS in the apical view is:
LV longitudinal strain ðLVLSÞ ¼
From Fig. 1b, (Les – Led) is the change in length, which
is the sum of the change in length in both the medial
(ΔLmed) and lateral (ΔLlat) walls:
The negative sign represents myocardial shortening.
LVLS can thus be re-written as:
LVLS ¼ − ðΔLmed þ ΔLlatÞ
Theoretically, LVLS can be estimated by M-mode
(motion-mode) measurements. Figures 2 and 3 show an
example of curved anatomical M-mode (CAMM) and a
conventional M-mode, respectively. CAMM, only
available in some machines, collects M-mode information
along a curved cursor. If traced along the LV
myocardium in the apical longitudinal view, the motion of the
whole U-shaped LV can thus be captured (Fig. 2).
CAMMLed and CAMMLes are the largest and smallest
separations of the medial and lateral mitral annuli, and
represent the end-diastolic length and end-systolic
length of the LV, respectively (Fig. 2). We define CAMM
fractional shortening (CAMMFS) as:
By comparing Eqs. (1) and (4), since CAMMLed ≈ Led
and CAMMLes ≈ Les, it follows that
MAPSE is the longitudinal excursion of the mitral
annulus from end-diastole to end-systole (Fig. 3a).
Comparing Figs. 1 and 3a, MAPSEmed and MAPSElat are
approximately equal to ΔLmed and ΔLlat, respectively.
MAPSEsum ¼ MAPSEmed þ MAPSElat≈ΔLmed þ ΔLlat
Figure 3b shows the actual images of an A4C view and
conventional M-mode of the LV. MMVL is the
enddiastolic ventricular length, after excluding the body wall
and muscle layer, measured in M-mode. From Fig. 3a,
the total M-mode left ventricular length (MMVLtotal) is:
MMV Ltotal≈MMV Lmed þ MMV Llat
LV longitudinal wall fractional shortening (LWFS) is
Since MMVLtotal approximates Led (see Additional file 1),
from Eqs. (3), (6) and (8),
TTE studies between June to November 2016 were
screened for suitability for inclusion into the study. To
cover a wider range of LVLS, hence predictability, a
priori decision was made to extend the range of LVLS
by including approximately 40% of TTE studies that
showed abnormal LV systolic function, defined as the
presence of one or more segmental wall dysfunction or
LV ejection fraction (LVEF) < 50%. Inclusion criteria
were: (1) the TTE study must contain a apical
4chamber (A4C) view with at least three cardiac cycles
recorded, (2) the image quality must be of adequate
quality to allow successful speckle tracking (low
background noise and good delineation of endocardial border),
(3) all LV inferoseptal and anterolateral segments and
the mitral annulus must be visible throughout the
cardiac cycle, (4) the LV long axis must lie along the
midline of the sector for proper M-mode measurement, (5)
the LV should not be foreshortened, (6) there should
not be significant translational artefacts causing
out-ofplane displacement, (7) the two-dimensional (B-mode)
frame rate must be 50 fps or higher, and (8) the patient
must be in sinus rhythm. A total of 127 studies
(patients) were included in the first round of screening for
LV systolic function. Of these, 65 patients had abnormal
LV systolic function. Forty-seven studies were excluded
upon further screening for quality. Most of these
studies were excluded for more than one reason: low frame
rate (n = 21), inadequate study quality (n = 31) and
angulated heart axis (n = 15) were the main reasons.
“Inadequate study quality” includes studies that did
not satisfy any of points (2) to (7) of the inclusion
All included TTE studies were performed using GE
Vivid 7 or E9 machine (GE Healthcare, Horton, Norway)
and EchoPac software (version 201, Revision 61.0, GE
Healthcare, Wauwatosa, WI, USA) was used for analysis.
LVLS from the A4C were measured offline using speckle
tracking. One complete cardiac cycle, excluding the first
and the last cycles, was used in strain analysis. After
optimizing the overall gain, the endocardial border was
traced manually from the medial to the lateral mitral
annulus making sure the trabeculae and papillary
muscles were excluded. The width of the region of
interest was adjusted to exclude the pericardium. The
software automatically tracked the myocardial speckles
and calculated the LVLS. For comparison, LVEF in
this study was measured using Simpson’s monoplane
method in the A4C view.
CAMM was measured offline using the EchoPac
software. The entire U-shaped LV was traced along the
middle of the myocardium in the A4C view at
enddiastole, including also the medial and lateral mitral annuli
(Fig. 2). CAMMLed and CAMMLes were the distances
(ventricular lengths) between the medial and lateral annuli
at end-diastole and end-systole, respectively. Inner edge
was used in the measurements and post-systolic shortening
was excluded when measuring CAMMLes [
] (Fig. 4).
MMVL and MAPSE of the medial and lateral walls
were measured in the A4C view using the leading-edge
method. End-diastolic MMVL was the M-mode distance
between the apical pericardium to the mitral annulus
(Fig. 3b). MAPSE was measured from the nadir
(enddiastole) to the peak (end-systole) but avoiding
postsystolic shortening (Fig. 4). MAPSEsum and MMVLtotal
were calculated using Eqs. (6) and (7), respectively. If
M-mode image was not available in the original study,
post-processing M-mode was constructed from the A4C
cineloop. M-mode measurements were performed
separately to the LVLS measurements and the investigators
(SJH and AMH) were blinded to the LVLS results at the
time of measurements.
The included studies (n = 80) were computer-randomized,
by generating a set of randomized binary codes according
to a uniform distribution, into a training set (n = 50)
and a validation set (n = 30). Univariate generalized
linear models were constructed from the training set
using maximum likelihood estimation assuming Gaussian
family distribution. LVLS was the response variable and
MAPSEsum, CAMMFS or LWFS was the predictor:
LVLS ¼ b0 þ b1ðpredctorÞ þ ε
where b0 and b1 are the regression coefficients (the
intercept and the slope, respectively) and ε represents
random or measurement errors. Student t test was used
to test if b0 and b1 were equal to zero. Model fitness was
tested using Chi-square test and Hosmer-Lemeshow test.
Model selection was also based on maximum likelihood
pseudo-R2 (reported as R2), and dispersion (reported as
mean squared error, MSE). Correlation between two
variables was assessed using Pearson correlation. All
models were diagnosed for linearity, residual normality
and equal variance using QQ plot and residual versus
fitted values plot to ensure model validity.
Predictive capability of the selected model was tested
on the validation set by comparing the MSE from the
training set with the mean squared prediction error
(MSPE), which is defined as:
X LV LSmeas−LV LSpred
where n is the sample size, and the subscripts meas and
pred represent the measured (observed) and predicted
values. Agreement between the LVLSmeas and LVLSpred
was analysed using the Bland and Altman method [
Intra- and inter-observer agreement and reliability were
examined using Bland and Altman plot and intraclass
correlation coefficient (ICC), respectively.
LVLS was presented as absolute (positive) values in this
study. Measurement data were summarized as mean ± SD.
LVLS and LWFS data were normally distributed for the
normal and abnormal LV function groups (Shapiro-Wilk
test, P > 0.05). Model parameters (such as intercepts and
slopes) and test statistics were presented as mean ± SE or
mean [upper, lower 95% confidence interval (CI)]. 95%CI
was presented wherever possible and when effect size was
more informative, otherwise P value was presented [
All analyses were carried out using the open source
software R (version 3.3.1) (The R Foundation for Statistical
Computing, Vienna, Austria).
Power and sample size
Sample size for the training cohort was estimated using a
power (1 - β) of 0.90 and a critical significance level (α) of
0.005 to ensure reproducibility of the results [
With one predictor and assuming a correlation (r) of 0.55
(R2 of 0.3) a sample size of 42 achieves a power of 0.90.
A total of 80 patients’ records were included in this
study and their characteristics are displayed in Table 1.
Forty-seven patients (59%) were reported to have normal
LV function with mean LVEF of 57 ± 5%. Thirty-three
patients (41%) were reported to have LV dysfunction
and the mean LVEF was 28 ± 11%. The patient
characteristics for the training set and validation set are
summarized in Table 2. The characteristics for the two data
sets were similar.
Training set (n = 50) Validation set (n = 30)
61.8 ± 14.1
LVEF LV ejection fraction, LVEDV LV end-diastolic volume, MAPSE mitral annular
plane systolic excursion, LWFS longitudinal wall fractional shortening, CAMMFS
curved-anatomical M-mode fractional shortening, LVLS LV longitudinal strain
The correlation matrix between LV ejection fraction
(LVEF), LVLS and various predictors for the whole data
set (n = 80) is shown in Fig. 5. These variables showed
good correlation with each other, although LVEF
demonstrated the weakest correlations with the other
measurements. LVLS shown good correlations with
CAMMFS, MAPSEsum and LWFS with r = 0.86, 0.82 and
0.89, respectively (P < 0.001 for all).
Model building and selection from training set (n = 50)
Three models were built using MAPSEsum, CAMMFS or
LWFS separately as predictor. The results are shown
in Fig. 6a to c and Table 3. All three predictors
showed good to very good correlations with LVLS
with r = 0.852 [0.752, 0.914], 0.875 [0.780, 0.928] and
0.909 [0.844, 0.974] for MAPSEsum, CAMMFS and
LWFS, respectively. The intercepts (bo) were not
significantly different from zero and the slopes (b1)
were greater than zero (Table 3).
While these models exhibited good to very good
correlations between the predictor and LVLS, the data points
for model 1 and 2 were more dispersed (variable) than
model 3 as evident from the MSEs (9.34 vs 7.98 vs 5.95).
LWFS (model 3) also explained the variability better
than models 1 and 2 (i.e. largest R2) (Table 3). Model 3
was therefore used to predict LVLS in the validation set.
Model validation using the validation set (n = 30)
The intercept (bo) and slope (b1) after fitting a regression
line to the validation set were similar to model 3
(Tables 3 and 4). Although R2 was slightly lower than the
training set (0.7305 vs 0.8257), the MSEs were similar and
was reasonably small (6.10 vs 5.95).
The mean LVLSpred was 14.5 ± 4.3%, which was similar
to LVLSmeas in the validation set (14.5 ± 4.7%). LVLSpred
exhibited a very good correlation with LVLSmeas (Fig. 7a).
The slope = 0.938 [0.769, 1.107] and the intercept was
not statistically significant from zero (0.860 [-1.59,
3.31]). MSPE was 5.75, which was similar to the MSE of
the training set (model 3) indicating the absence of
significant bias and had good prediction capacity.
Figure 7b shows the Bland and Altman plot for
relationship between the difference and the mean between
LVLSmeas and LVLSpred. The differences were normally
distributed, and there was no bias in the prediction
(mean difference = -0.03% [95%CI: -0.89, 0.95]). The 95%
limits of agreement (LOA) were -4.75% and 4.82%.
Intercepts and slopes are presented as mean ± SE. *Goodness-of-fit (GoF) tests.
Δdeviance (χ2-test) compares the change in deviance from the null model
(without predictor) to one containing the predictor. Smaller P value in indicates
more significant change after adding the predictor. Hosmer-Lemeshow (HL)
test examines the difference between the model and the observed data. Larger
P value indicates no difference. MAPSE mitral annular plane systolic excursion,
CAMMFS curved anatomical M-mode ventricular length, LWFS longitudinal wall
fractional shortening, SE standard error, MSE mean squared error
Intra- and inter-observer agreement and reliability of
The bias and LOA for intra-observer measurements of
LWFS were 0.219 [-0.182, 0.621] and ± 3.539,
respectively. The ICC was 0.91 [0.87, 0.94]. These indicate
nonbias agreement and good intra-rater reliability. On the
other hand, very small but insignificant bias was
observed between two independent observers (bias = 0.589
[0.000, 1.176] and LOA = 3.196). Good inter-rater reliability
was observed (ICC = 0.93 [0.91, 0.94]).
correlations with LVLS in the apical 4-chamber view.
Between CAMMFS and LWFS, the latter provides a
better goodness-of-fit with LVLS. LWFS measurement
was repeatable and reliable.
To date, strain studies in critically ill patients are scarce.
Most studies were performed on intensive care septic
]. The main consistent findings from these
studies were that left ventricular longitudinal strain was
more sensitive than LVEF in detecting systolic
dysfunction, and longitudinal strain could not predict mortality
in septic patients [
]. The relatively small
number of critical care studies available may reflect (1) the
difficulties in obtaining optimal images for speckle
tracking in this population, (2) speckle-tracking software is
not available in the ultrasound machines, which are
mostly used as a point of care device, and/or (3) most
critical care clinicians are not trained in speckle-tracking
The present study supports the notion that LVLS and
longitudinal M-mode indices (MAPSE, CAMMFS and
LWFS) are closely related and highly correlated with
each other. The results are not surprising because all of
these indices, including LVLS, measure the motion of
myocardial in the longitudinal plane. On the other
hand, the correlation between LVEF and LVLS was
also highly correlated but less ideal than M-mode
indices (r = 0.76). A similar correlation between LVEF
and LVLS (r = 0.7) has been reported recently [
One explanation for the poorer correlation is that
LVEF reflect not only longitudinal contraction but
also radial and circumferential contraction, whereas
LVLS reflects mainly the longitudinal contraction.
The present study shows that, using univariate linear
model, both CAMMFS and LWFS exhibited very good
MAPSE was first described in 1932 by Hamilton and
Rompf as caudal-cephalad movement of the atrioventricular
]. Since the first ultrasound study in 1967,
MAPSE has been reported as a consistent and reliable
marker for longitudinal function of the LV [
MAPSE correlates with LVEF with reported r ranged
from 0.55 to 0.95 [
]. The present study also found
a good correlation between the MAPSEsum with LVEF
(r = 0.70). Although MAPSE only demonstrates the
longitudinal motion of the LV, it was more sensitive than
LVEF in detecting early LV dysfunction, such as in
hypertensive patients [
]. In patients with
moderate to severe aortic stenosis, MAPSE was as good as
GLS in detecting early LV dysfunction [
]. Similar to a
previous report that showed a positive correlation
between MAPSE and longitudinal strain [
], the present
study also found a good correlation between MAPSEsum
with LVLS (r = 0.82).
M-mode fractional shortening as longitudinal strain
Theoretically, LVLS, CAMMFS and LWFS measures the
same phenomenon – the change in LV length
normalized to its original (end-diastolic) length. In a study that
purported to use MAPSElat/left ventricular length
(MAPSE/L) as an index for LV longitudinal function in
children where adjusting for age-dependent ventricular
length is important, GLS was found to be moderately
correlated with MAPSElat/L even when the study was
not originally designed to investigate the relationship
between the two (r = 0.56) [
The present study demonstrated that LWFS (MAPSEsum/
MMVLtotal) exhibited very good correlation with LVLS in
the training set (r = 0.909) providing supportive evidence
that LWFS and LVLS are two closely related, if not similar,
measurements. Using a separate validation set, LWFS
displayed very good predictive capability (see Fig. 7). The
95% LOA of the difference between LVLSmeas and LVLSpred
was -0.03 ± 4.78%, which was better than the variability
(SD) of longitudinal strain (see Tables 1 and 2) and was
within the test-retest limit reported for longitudinal strain
(2.5% to 5.0%) [
]. Of note, the value of LWFS should not
be taken the same as LVLS. In calculating LWFS, we
assumed that MMVLtotal, which was measured in a straight
line, was the same as the curved ventricular length (Led)
used in LVLS measurement. Theoretically, MMVLtotal is
always less than Led. However, in an average size LV,
MMVLed and the Led differ by less than 10% (see
Additional file 1).
Strengths and weaknesses LWFS compared to strain
Compared to strain measurement, LWFS is less dependent
on image quality. This is especially important in
difficult patients such as obese or critically ill patients.
However, M-mode is angle-dependent, therefore, a good
alignment of the LV axis with the midline of the sector is
necessary for accurate measurement. LWFS does not give
segmental information. Yet, there are other advantages of
LWFS measurement: it can be performed quickly even on
the bedside, requires minimal training, and can be
averaged over several consecutive cardiac cycles, which is very
useful in irregular rhythm. M-mode has a very high
sampling rate, which is typically between 1000 to 2000
samples per second, and provides superior temporal
]. Good intra- and inter-rater agreement and
reliability makes follow-up and cross-platform M-mode
studies comparable. Special software is not required for
LWFS measurements and can be performed using any
On the other hand, strain relies on optimal image
quality which is often not obtainable from every patient
or view. Speckle tracking also relies on high frame rate,
while slow frame rate or high heart rate may limit
tracking accuracy. Sampling rate is not an issue with M-mode
(see above). Unlike M-mode, averaging over consecutive
cardiac cycles is time-consuming and impossible in
irregular cardiac rhythm. However, speckle-tracking
strain measurement is less susceptible to angle and
translational artefacts and also gives segmental
information. That said, false positives of segmental wall
information have been described [
]. Inter-vendor differences
in speckle-tracking algorithm is also a major concern
]. Finally, special costly software is usually required
for speckle tracking.
This study implies that LWFS measurement offers an
alternative measurement or method of estimating LVLS.
As LWFS measures the longitudinal motion of the left
ventricle, in theory, it may act as a prognostic tool and
offers similar sensitivity in detecting early LV systolic
dysfunction as LVLS. It has the potential to be used as a
follow-up tool for subclinical myocardial dysfunction
and to evaluate the treatment effects. In this regard,
LWFS can be useful in deciding when to initiate or
Limitations of the study
Inter-vendor inconsistencies are known to be a major
issue in strain measurements and may affect the
applicability of the prediction model (equation). The
inconsistencies are mainly due to different definitions and
algorithms used by different vendors [
]. Even with
the same vendor, different versions of speckle-tracking
software have been shown to yield different GLS values
. Therefore, the prediction model used in this study
may not be applicable to different system or versions of
software. However, when used as index itself, LWFS
does not suffer from such problems.
As this was a retrospective study, we were unable to
determine the true feasibility of measuring LWFS in ICU
patients. Many echocardiograms (37%) were not optimized
for the purpose of measuring LWFS and LVLS in this
study. As a result, they were excluded due to inadequate
image optimization, low frame rate, deviated heart
axis and foreshortened apical view. Although patient
characteristics played a contributory role in image
quality, the experience of the echocardiographers,
some of whom were receiving basic level critical care
echo training, also contributed. We expect the
feasibility of measuring LWFS should improve with
experience. Also, as the apical 2 and 3 chamber views
were not optimized for strain or M-mode
measurement purposes, we were not able to obtain GLS and
a “global” LWFS value for comparison. Theoretically,
“global” LWFS should have a similar predictability
capacity as LWFS in this study. Unfortunately, we
were unable to determine the true feasibility of LWFS
measurements when compared to LVLS, which is best
done in a prospective study on consecutive patients.
We were also unable to track the changes in LV
systolic function with treatment.
For the purpose of this association study, the patients
(studies) included in this study were not randomly
selected and hence subject to selection bias. First, we
selected only those studies which were optimal for LVLS
and M-mode measurements, hence might have excluded
those very sick and difficult patients. Second, to extend
the predictable range, we deliberately included a large
proportion of patients with abnormal LVEF
(approximately 40%), thereby creating two distinct populations.
These patients were unlikely to represent the usual mix
of ICU patients. We omitted presenting the clinical data
and treatment data which could be biased and
misleading in this study. Of note, this selection bias did not
affect the validity of the model as diagnostic tests on the
assumptions of normality, equal variance and linearity
were not violated.
This study demonstrated that LWFS is an unbiased
predictor of LVLS. In fact, indices that measured LV
longitudinal function, namely MAPSE, CAMMFS and
LWFS, displayed good correlations with longitudinal
strain in this study. Compared to speckle-tracking strain
measurements, LWFS only requires simple M-mode
measurements which are reproducible and reliable,
requires minimal training and are available in all
machines. LWFS could potentially be a useful index for
clinical use. Research into the clinical utility of LWFS is
Additional file 1: The difference between the M-mode ventricular
length (MMVL) and LV length based on a semi-elliptical model. Theoretical
(mathematical) consideration of the difference of ventricular length between
conventional M-mode and a semi-elliptical model. (PDF 2953 kb)
CAMMFS: Curved-anatomical M-mode fractional shortening; CAMML: Curved
anatomical M-mode ventricular length; GLS: Global longitudinal strain;
ICC: Intraclass correlation coefficient; LOA: Limits of agreement; LV: Left ventricle;
LVEDV: LV end-diastolic volume; LVEF: LV ejection fraction; LVLS: LV longitudinal
strain; LWFS: Longitudinal wall fractional shortening; MAPSE: Mitral annular
plane systolic excursion; MMVL: M-mode ventricular length; MSE: Mean squared
error; MSPE: Mean squared prediction error
Availability of data and materials
The data sets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
SJH was responsible for conceptualization and design of the study. SJH and IT
screened the studies. SJH, IT and AMH performed data collection and statistical
analysis. SJH and ASM drafted the manuscript. MS and ASM critically revised the
manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study was approved by the Nepean and Blue Mountain Local Health
District Human Research and Ethics Committee as a low and negligible risk
study (study no. LNR/14/NEPEAN/80), and no informed consent was required
for a low-risk retrospective study.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
1. Dandel M , Lehmkuhl H , Knosalla C , Suramelashvili N , Hetzer R . Strain and strain rate imaging by echocardiography - Basic concepts and clinical applicability . Curr Cardiol Rev . 2009 ; 5 : 133 - 48 .
2. Tops LF , Delgado V , Marsan NA , Bax JJ . Myocardial strain to detect subtle left ventricular systolic dysfunction . Eur J Heart Fail . 2016 ; 19 : 307 - 13 .
3. Dalla K , Hallman C , Bech-Hanssen O , Haney M , Ricksten S-E . Strain echocardiography identifies impaired longitudinal systolic function in patients with septic shock and preserved ejection fraction . Cardiovasc Ultrasound . 2015 ; 13 : 30 . Available from: doi:10.1186/s12947-015-0025-4.
4. Romano S , Mansour IN , Kansal M , Gheith H , Dowdy Z , Dickens CA , et al. Left ventricular global longitudinal strain predicts heart failure readmission in acute decompensated heart failure . Cardiovasc Ultrasound . 2017 ; 15 : 2388 .
5. Cha MJ , Kim HS , Kim SH , Park JH , Cho GY . Prognostic power of global 2D strain according to left ventricular ejection fraction in patients with ST elevation myocardial infarction . PLoS One . 2017 ; 12 : e0174160 . Lionetti V , editor.
6. Voigt JU , Pedrizzetti G , Lysyansky P , Marwick TH , Houle H , Baumann R , et al. Definitions for a common standard for 2D speckle tracking echocardiography: consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging . Eur Heart J Cardiovasc Imag . 2015 ; 16 : 1 - 11 .
7. Huang SJ , Orde S. From speckle tracking echocardiography to torsion: research tool today, clinical practice tomorrow . Curr Opin Crit Care . 2013 ; 19 : 250 - 7 .
8. Reisner SA , Lysyansky P , Agmon Y , Mutlak D , Lessick J , Friedman Z. Global longitudinal strain: a novel index of left ventricular systolic function . J Am Soc Echocardiogr . 2004 ; 17 : 630 - 3 .
9. Castel A-L , Menet A , Ennezat P-V , Delelis F , Le Goffic C , Binda C , et al. Global longitudinal strain software upgrade: implications for intervendor consistency and longitudinal imaging studies . Arch Cardiovasc Dis . 2016 ; 109 : 22 - 30 . Available from: doi:10.1016/j.acvd. 2015 . 08 .006.
10. Feigenbaum H . Role of M-mode technique in today's echocardiography . JASE Am Soc Echo . 2010 ; 23 : 240 - 57 .
11. Voigt JU , Lindenmeier G , Exner B , Regenfus M , Werner D , Reulbach U , et al. Incidence and characteristics of segmental postsystolic longitudinal shortening in normal, acutely ischemic, and scarred myocardium . J Am Soc Echocardiogr . 2003 ; 16 : 415 - 23 .
12. Bland JM , Altman DG . Measuring agreement in method comparison studies . Stat Meth Med Res . 1999 ; 8 : 135 - 60 .
13. Wasserstein RL , Lazar NA . The ASA's statement on p-values: context, process, and purpose . Am Stat . 2016 ; 72 : 129 - 33 .
14. Curran-Everett D. CORP : Minimizing the chances of false positives and false negatives . J Appl Physiol . 2017 ; 122 : 91 - 5 .
15. Cohen J . Statistical power analysis for the behavioural sciences . 2nd ed. Cohen J, editor. New York: Psychology Press; 1988 .
16. Cinotti R , Delater A , Fortuit C , Roquilly A , Mahé P-J , Demeure-dit- Latte D , et al. Speckle-tracking analysis of left ventricular systolic function in the intensive care unit . Anaesthesiol Intensive Ther . 2015 ; 47 : 482 - 6 .
17. Orde SR , Pulido JN , Masaki M , Gillespie S , Spoon JN , Kane GC , et al. Outcome prediction in sepsis: Speckle tracking echocardiography based assessment of myocardial function . Crit Care . 2014 ; 18 : R149 .
18. De Geer L , Engvall J , Oscarsson A . Strain echocardiography in septic shock - a comparison with systolic and diastolic function parameters, cardiac biomarkers and outcome . Crit Care . 2015 ; 19 : 122 .
19. Zaky A , Gill EA , Paul CP , Bendjelid K , Treggiari MM . Characteristics of sepsisinduced cardiac dysfunction using speckle-tracking echocardiography: a feasibility study . Anaesth Intensive Care . 2016 ; 44 : 65 - 76 .
20. Hamilton WF , Rompf JH . Movements of the base of the ventricle and the relative constancy of the cardiac volume . Am J Physiol . 1932 ; 102 : 559 - 65 . Available from: http://ajplegacy.physiology.org/content/102/3/559.short.
21. Zaky A , Grabhorn L , Feigenbaum H . Movement of the mitral ring: a study in ultrasound cardiography . Cardiovasc Res . 1967 ; 1 : 121 - 31 .
22. Simonson JS , Schiller NB . Descent of the base of the left ventricle: an echocardiographic index of left ventricular function . J Am Soc Echocardiogr . 1989 ; 2 : 25 - 35 .
23. Emilsson K , Alam M , Wandt B. The relation between mitral annulus motion and ejection fraction: a nonlinear function . J Am Soc Echocardiogr . 2000 ; 13 : 896 - 901 .
24. Wandt B. Long-axis contraction of the ventricles: a modern approach, but described already by Leonardo da Vinci . J Am Soc Echocardiogr . 2000 ; 13 : 699 - 706 .
25. Adel W , Roushdy AM , Nabil M. Mitral annular plane systolic excursionderived ejection fraction: a simple and valid tool in adult males with left ventricular systolic dysfunction . Echocardiography . 2015 ; 33 : 179 - 84 .
26. Hu K , Liu D , Herrmann S , Niemann M , Gaudron PD , Voelker W , et al. Clinical implication of mitral annular plane systolic excursion for patients with cardiovascular disease . Eur Heart J Cardiovasc Imag . 2013 ; 14 : 205 - 12 . Available from: http://ehjcimaging.oxfordjournals.org/cgi/doi/ 10.1093/ehjci/jes240.
27. Xiao HB , Kaleem S , McCarthy C , Rosen SD . Abnormal regional left ventricular mechanics in treated hypertensive patients with 'normal left ventricular function' . Int J Cardiol . 2006 ; 112 : 316 - 21 .
28. Luszczak J , Olszowska M , Drapisz S , Plazak W , Kaznica-Wiatr M , Karch I , et al. Assessment of left ventricle function in aortic stenosis: mitral annular plane systolic excursion is not inferior to speckle tracking echocardiography derived global longitudinal peak strain . Cardiovasc Ultrasound . 2013 ; 11 : 45 .
29. Wenzelburger FWG , Tan YT , Choudhary FJ , Lee ESP , Leyva F , Sanderson JE . Mitral annular plane systolic excursion on exercise: a simple diagnostic tool for heart failure with preserved ejection fraction . Eur J Heart Fail . 2014 ; 13 : 953 - 60 .
30. Terada T , Mori K , Inoue M , Yasunobu H . Mitral annular plane systolic excursion/left ventricular length (MAPSE/L) as a simple index for assessing left ventricular longitudinal function in children . Echocardiography . 2016 ; 33 : 1703 - 9 .
31. Mirea O , Pagourelias ED , Duchenne J , Bogaert J , Thomas JD , Badano LP , et al. Variability and reproducibility of segmental longitudinal strain measurement: A report from the EACVI-ASE strain standardization task force . JACC Cardiovasc Imag . 2017 . doi: 10 .1016/j.jcmg. 2017 . 01 .027.
32. Feigenbaum H , Mastouri R , Sawada S. A practical approach to using strain echocardiography to evaluate the left ventricle . Circ J . 2012 ; 76 : 1550 - 5 .