Methods
PopulationIn total, 782 patients undergoing isolated CABG at Gentofte Hospital
from January 2006-May 2011 were screened in this retrospective cohort.
Patients were not included if they had rescue CABG performed, did not
have an echocardiogram available nor if they had significant valvular
disease defined as at least moderate mitral regurgitation or aortic
valve stenosis. Of the remaining 700 patients, those with known AF were
excluded (n=54), so were patients already on anticoagulation for other
reasons (n=2), and 13 patients with postoperative AF (defined as AF
occurring within 14 days)
(9). Patients in whom
LA measures could not be performed were also excluded (n=20). In total,
611 patients were left for inclusion in the present analysis.
Baseline data on medicine, comorbidities, laboratory and angiographic
findings were recorded by reviewing the hospital charts.
EndpointThe endpoint was the development of any form of AF, which was obtained
through diagnostic codes (ICD10: I48.9) from the Danish National Patient
Registry.
Biochemical analysisThe patients had blood samples drawn when admitted to the hospital.
These samples were analyzed for hemoglobin, creatine kinase MB,
C-reactive protein and creatinine.
EchocardiographyEchocardiography was performed at a median of 15 (IQR: 8;32) days prior
to surgery. Examinations were performed using a Vivid Dimension (GE
Healthcare; Horten, Norway) with a 3.5-MHz transducer. Analysis of the
echocardiographic images was done offline as post-processing analysis
(EchoPAC BT 11.1.0. GE Vingmed Ultrasound AS) by a single, experienced
analyst blinded to follow-up information.Conventional echocardiographyLV dimensions (interventricular septal thickness, left posterior wall
diameter and internal LV diameter) were measured in the parasternal
long-axis view at end-diastole and used to calculate the LV mass index
(LVMI) by Deveraux’s formula
(10). LAVmin and
LAVmax were measured by the biplane area-length method and corrected for
body surface area (BSA). LAEF was calculated by\(LAEF=\ \frac{LAVmax-LAVmin}{\text{LAVmax}}\bullet 100\%\ \). Left
ventricular ejection fraction (LVEF) was measured using the biplane
Simpson’s model.
Transmitral inflow patterns (E-wave, A-wave, E/A, E-wave deceleration
time) were measured by pulsed-wave Doppler imaging with the sample
placed at the mitral valve leaflets. Pulsed-wave Doppler imaging was
used to measure the myocardial relaxation velocity (e’) in the septal
and lateral walls.
StatisticsStatistical analysis was performed using STATA 14 (StataCorp LP, College
Station, TX). Stratification was done by the outcome of AF.
Categorical variables are displayed as total numbers and percentages and
were compared using \(\chi^{2}\)-test. Continuous variables with a
Gaussian distribution are displayed as a mean ±SD and were compared
using Student t test. Continuous variables showing non-Gaussian
distribution are displayed as median with interquartile ranges (IQR) and
were compared using Wilcoxon rank sum test.
Univariable Cox proportional hazards regression analyses were used for
the three measurements of the LA: LAVmin, LAVmax, and LAEF.
Multivariable Cox regression analyses were also applied to adjust for
confounders and to obtain fitted hazard ratios. This was performed in
two models: (1) Adjusting for potential confounders identified within
the cohort: gender, age, heart rate and hemoglobin, (2) adjusting for
CHADS2 score.
The association between LA measurements and outcome were tested for
interaction with gender and hypertension.
Stratified analysis was performed for patients based on a normal LA
(LAVmax<34 mL/m2). Univariable as well
multivariable Cox regressions were applied in the same way as for the
entire population.
Cox proportional-hazard models were constructed for this subgroup based
on incidence rate of AF stratified by low and high LAEF and low and high
LAVmin.
Harrell’s C-statistics were calculated from univariable Cox regression
to estimate the predictive value of the LA measurements.
A p-value of <0.05 was cut off point for significance in the
analyses.
ResultsDuring a median follow up time of 3.7 years (IQR: 2.6;4.9), 52 patients
(9 %) developed AF. Baseline characteristics of the patients are
displayed in table 1. The mean age was 67 years, LVEF was 50 %, 84 %
were male, 68 % were hypertensive and 26 % were diabetic.
The patients who developed AF showed a trend towards being older (70
years vs 67 years) but were otherwise similar with respects to clinical
characteristics compared to the group free of AF.
LAEF was the only echocardiographic measure that differed significantly
between the outcome groups at baseline.
Predictive value of the
atrial functional measurementsUni- and multivariable Cox regressions are displayed in Table 2. LAEF
was the only significant predictor in the univariable and multivariable
model 1. No other LA measures were significantly associated with AF. No
effect modification was found for hypertension nor gender with respects
to the association between AF and any of the three LA measures
(p>0.05 for all).
In patients with a normal-sized LA (n=531 with 49 events), both LAVmin
and LAEF were significant predictors of AF in the univariable model,
table 2), whereas LAVmax was not.
In the multivariable model, both LAEF and LAVmin remained significant
predictors of AF when adjusted for potential confounders (gender, age,
heart rate, and hemoglobin) (LAEF: HR=1.02 (1.01-1.04), p=0.007, per %
decrease and LAVmin: HR=1.08 (1.02-1.14), p=0.005, per
mL/m2 increase). When adjusting for the
CHADS2 score, both LAEF and LAVmin remained independent
predictors of outcome (LAEF: HR: 1.02 (1.00-1.03, p= 0.023 and LAVmin:
HR=1.07 (1.01-1.13), p=0.014, per mL/m2 increase).
LAVmin had the highest C-statistic and even higher than the
CHADS2 score (0.60 vs 0.58), although this difference
was not statistically significant.
In patients with normal size LA, high LAEF (>47%) were not
statistically less likely to develop AF during follow up time (figure
1).
However, we found a significant association between high LAVmin
(>11 mL/m2)and risk of AF in this
subgroup, such that high LAVmin posed an increased risk of AF: HR= 1.95
(1.08-3.51), p for log rank =0.02 (figure 2).
DiscussionThe main finding from the present study is that no echocardiographic
measurement independently predict AF after CABG. However, LAVmin and
LAEF are independent predictors of outcome in patients with a
normal-sized LA – and both were better than the conventionally used
LAVmax. These echocardiographic measurements may be useful in predicting
AF in patients with normal sized LA who are at higher risk of AF. This
may be because LAVmax is a measure of LA structure rather than LA
function. In contrast to both LAEF and LAVmin are more related to LA
function, since LAEF is an indirect measurement of the atrial ability
eject blood into the LV and a larger LAVmin is equivalent to a stiffer
LA without the ability to contract in the diastole leaving a large
residual volume, which is hypothesized to be a direct contributing
factor to developing AF (11)
(12).
LAVmax has been shown to be a significant predictor of AF, however, we
found in the present CABG cohort that LAVmax was not associated with
subsequent AF (13). Since it is already known that LA dilation can lead
to AF, it is important to have a tool that can identify patients at
risk, who do not have this trademark (12). A large percentage of
patients who develop AF in the present study had a normal-sized LA as
determined by the LAVmax (n=14 equivalent to 27 % of AF events) which
emphasizes the need to identify measures of more subtle LA impairment
which are also associated with an increased risk of AF. Other
echocardiographic measurements of both structure and function have
previously proven significant in predicting AF in CABG patients, such as
LA diameter, epicardial fat and LA expansion index – solidifying the
fact that a pre-operative echocardiography is an important measure to
risk stratify patients for post-OP AF (14, 15, 16).
LAEF was a significant predictor of outcome in uni- and multivariable
models in this subgroup of patients with normal-sized LA. LAEF has
previously been proven to be a significant predictor of AF in patients
with ischemic stroke and patients undergoing radiofrequency catheter
ablation (17, 18). Unfortunately, the aforementioned studies did not
present data on LAVmin.
LAVmin has been shown to result in the highest predictive performance,
as determined from the C-statistic, of the functional measurements when
added to the CHADS2 score
(19). The
CHADS2 score is constructed from categorical variables,
which makes it easier for clinicians to use, but this also simplifies
the risk factors for developing AF. The added value beyond clinical
parameters may imply that we will be able to identify patients at risk
of AF at an even earlier point and better prevent its associated
complications.
Clinical perspectiveAF is associated with increased mortality due to increased risk of
cardioembolic stroke (20,21). When also considering the increasing
prevalence of AF, it is important to accurately predict the risk of AF
in the individual patient (22). Because LAVmin and LAEF significantly
predict AF in patients with normal LAVmax, they may supplement LAVmax.
Since echocardiography is already used routinely for patients undergoing
CABG, further assessing LA function could be a time-efficient approach
to provide an accurate risk assessment of AF can consequently prevent
stroke.
LimitationsWe do not have insight as to the monitoring process of the patients as
the endpoint was drawn from patient registers. Also, patients initially
excluded due to postoperative AF events could have developed clinical AF
later, and we did not account for this in the present study.
As this was a retrospective study, we cannot exclude the possibility of
residual confounding.
We did not measure the LA volume at the p-wave, and can therefore not
exclude that more detailed information on passive versus active LAEF
could provide valuable information on the risk of AF.
It should also be kept in mind that the CHADS2 score was
originally developed to predict stroke in AF patients and is therefore
not optimized for AF prediction, but has nonetheless been proposed as a
clinical prediction tool for AF
(23).
ConclusionNo echocardiographic LA measurement was an independent predictor of AF
for the entire population. However, for the subgroup with normal LA
volume, LAVmin and LAEF were significant predictors of AF. These
findings should be investigated further in prospectively designed
studies.