Methods
The study population is part of the Israel Cardio-Oncology Registry
(ICOR) – a prospective registry enrolling all patients evaluated in the
cardio-oncology clinic at Tel Aviv Sourasky Medical Center.
All patients signed an informed
consent at the first visit in the clinic and are then followed
prospectively. The registry was approved by the local ethics committee
(Identifier: 0228-16-TLV) and is registered in clinicaltrials.gov
(Identifier: NCT02818517).
This cohort evaluated patients diagnosed with breast cancer planned for
ANT based therapy. All patients performed at least 2 echocardiographic
evaluations, including GLS; at baseline before chemotherapy (T1) and
during Doxorubicin therapy (T2) for the assessment of correlation
between Dst and the standard diastolic parameters. A
3rd echocardiography exam (T3), assessed after the
completion of Doxorubicin therapy, within 3 months, was evaluated for
the development of GLS relative reduction between T1 and T3.
The exclusion criteria included LVEF<53% at T1 and
significant GLS relative reduction at T2≥15%.
A complete past medical history, cardiac risk factors and medical
treatment were noted.
Diastolic strain was evaluated by the time of lengthening (Dst) (ms) as
shown in Fig. 1. The change in Dst was assessed between T1 to T2. A
clinically significant reduction in GLS was considered as a relative
reduction of ≥15% from T1 echo to T3, adhered to the standard benchmark
set by previous studies [15].
Three standard apical views (4-chamber, 2-chamber, and apical long-axis)
were recorded using a General Electric system, model Vivid S70
echocardiogram and were performed by the same vendor, technician and
interpreting cardiologist. Routine Left ventricle (LV) echocardiographic
parameters included LV diameters, and LVEF [16]. Early trans-mitral
flow velocity (E), late atrial contraction (A) velocity, deceleration
time (DT) and early diastolic mitral annular velocity (medial and
lateral e’) were measured in the apical 4-chamber view to provide an
estimate of LV diastolic function [17]. The peak E/e’ ratio was
calculated (septal, lateral and average mitral E/e’ ratio). Left atrium
volume index (LAVI) was calculated using the biplane area length method
at end-systole [16]. Images were acquired using high frame rate
(>50 frames/s) [18], and thereafter stored digitally
for offline analysis. GLS was measured using STE software and tracking
within an approximately 5 mm wide region of interest. An end-systolic
frame was used to initialize LV boundaries which were then automatically
tracked throughout the cardiac cycle. Manual corrections were performed
to optimize boundary tracking as needed. Optimization of images for
endocardial visualization through adjustment of gain, compress, and
time-gain compensation controls was done prior to acquisition. Ds were
evaluated by measuring the time of lengthening (ms) of the myocardium
during diastole, from the point of aortic valve closure (AVC) to the
early peak of the curve for each segment (Fig. 1). Dst was assessed in
three apical views (2, 3 and 4 chamber), with 6 segments measured per
each view, with a total of 18 segments per each exam.
Assessment of the relationship between accepted echocardiographic
diastolic parameters and the speckle strain derived parameters was done
using repeated measures mixed linear regression models with e’ or E/e’
as the dependent variable, strain measurements as fixed independent
variables, and patient ID as the random variable. To assess the
predictive ability of diastolic strain parameters on significant GLS
reduction, individual logistic regression models were built with
significant GLS reduction as the dependent variable and relative
decrease in each diastolic strain parameter between T2 and echo T1 as
independent variables. Using the results of the above models the best
predictor of significant GLS reduction was used in a multivariate model
to assess its ability to independently predict significant GLS
reduction. First, a multivariate model was built with covariates
including relative GLS reduction between T2 and T1, baseline cardiac
risk factors, cardiotoxic chemotherapy used and cardioprotective
medication used. The above primary model was then narrowed using a
stepwise forward and backwards Akaike information criterion (AIC) based
method in order to select the best predictive model which has lowest
AIC. To further illustrate the diagnostic predictive power of Diastolic
strain alone or in combination with the other model covariates
receptor-operator (ROC) curves were built and AUC with 95% CI and
Youden indexes were calculated. Comparison between AUC of ROC curves was
done using the DeLong & DeLong method. To detect whether adding
Diastolic strain data contributed to the multivariate model predictive
ability, net classification index (NRI) was calculated for a logistic
model with and without the added variable, 95% confidence interval for
NRI (and its positive and negative components) was calculated using a
bootstrapping method. Continuous variables are shown as mean±SD, while
discrete variable as n(%). Results were considered significant when
p<0.05. As this is a primary proof of concept investigation,
all assessments were considered hypothesis generating and were not
corrected for multiple comparisons. All calculations were done using R
version 3.5.0, R Foundation for Statistical Computing, Vienna, Austria.