ª 2018 by the american college of cardiology …risk of cv mortality. gls was the strongest...

J A C C : C A R D I O V A S C U L A R I M A G I N G V O L . 1 1 , N O . 1 1 , 2 0 1 8

ª 2 0 1 8 B Y T H E AM E R I C A N C O L L E G E O F C A R D I O L O G Y F O UN DA T I O N

P U B L I S H E D B Y E L S E V I E R

2D and 3D Echocardiography-DerivedIndices of Left VentricularFunction and ShapeRelationship With Mortality

Diego Medvedofsky, MD,a Francesco Maffessanti, PHD,b Lynn Weinert, BSC,a David M. Tehrani, MD,a

Akhil Narang, MD,a Karima Addetia, MD,a Anuj Mediratta, MD,a Stephanie A. Besser, MSAS, MSA, MACJC,a

Elad Maor, MD, PHD,c Amit R. Patel, MD,a Kirk T. Spencer, MD,a Victor Mor-Avi, PHD,a Roberto M. Lang, MDa

ABSTRACT

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Ma

OBJECTIVES This study hypothesized that left ventricular (LV) ejection fraction (EF) and global longitudinal

strain (GLS) derived from 3-dimensional echocardiographic (3DE) images would better predict mortality than those

obtained by 2-dimensional echocardiographic (2DE) measurements, and that 3DE-based LV shape analysis may have

added prognostic value.

BACKGROUND Previous studies have shown that both LVEF and GLS derived from 2DE images predict mortality.

Recently, 3DE measurements of these parameters were found to be more accurate and reproducible because of

independence of imaging plane and geometric assumptions. Also, 3DE analysis offers an opportunity to accurately

quantify LV shape.

METHODS We retrospectively studied 416 inpatients (60 � 18 years of age) referred for transthoracic echocardiography

between 2006 and 2010, who had good-quality 2DE and 3DE images were available. Mortality data through 2016

were collected. Both 2DE and 3DE images were analyzed to measure LVEF and GLS. Additionally, 3DE-derived LV

endocardial surface information was analyzed to obtain global shape indices (sphericity and conicity) and regional

curvature (anterior, septal, inferior, lateral walls). Cardiovascular (CV) mortality risks related to these indices were

determined using Cox regression.

RESULTS Of the 416 patients, 208 (50%) died, including 114 (27%) CV-related deaths over a mean follow-up period of

5 � 3 years. Cox regression revealed that age and body surface area, all 4 LV function indices (2D EF, 3D EF, 2D GLS, 3D

GLS), and regional shape indices (septal and inferior wall curvatures) were independently associated with increased

risk of CV mortality. GLS was the strongest prognosticator of CV mortality, superior to EF for both 2DE and 3DE analyses,

and 2D EF was the weakest among the 4 functional indices. A 1% decrease in GLS magnitude was associated with an

11.3% increase in CV mortality risk.

CONCLUSIONS GLS predicts mortality better than EF by both 3DE and 2DE analysis, whereas 3D EF is a better

predictor than 2D EF. Also, LV shape indices provide additional risk assessment. (J Am Coll Cardiol Img 2018;11:1569–79)

© 2018 by the American College of Cardiology Foundation.

L eft ventricular (LV) ejection fraction (EF) is themost commonly used echocardiographicparameter of LV function, known to be an in-

dependent predictor of mortality (1–4), and is

N 1936-878X/$36.00

m the aDepartment of Medicine, University of Chicago Medical Center, Ch

rdiology, Institute of Computational Sciences, Università della Svizzera I

titute, The Chaim Sheba Medical Center, Tel HaShomer, Israel. The aut

evant to the contents of this paper to disclose.

nuscript received June 1, 2017; revised manuscript received August 23, 2

routinely used to guide patient management. Howev-er, 2-dimensional (2D) echocardiographic assessmentof LVEF, both qualitative and quantitative, is depen-dent on reader experience and imaging plane, and

https://doi.org/10.1016/j.jcmg.2017.08.023

icago, Illinois; bCenter for Computational Medicine in

taliana, Lugano, Switzerland; and the cLeviev Heart

hors have reported that they have no relationships

017, accepted August 24, 2017.

https://doi.org/10.1016/j.jcmg.2017.08.023

http://crossmark.crossref.org/dialog/?doi=10.1016/j.jcmg.2017.08.023&domain=pdf

ABBR EV I A T I ON S

AND ACRONYMS

2DE = 2-dimensional

echocardiography

–2LL = –2 logarithmic

likelihood

3DE = 3-dimensional

echocardiography

CI = confidence interval

CV = cardiovascular

EF = ejection fraction

GLS = global longitudinal

strain

HR = hazard ratio

LV = left ventricular

STE = speckle tracking

echocardiography

Medvedofsky et al. J A C C : C A R D I O V A S C U L A R I M A G I N G , V O L . 1 1 , N O . 1 1 , 2 0 1 8

2D and 3D Echocardiographic Predictors of Mortality N O V E M B E R 2 0 1 8 : 1 5 6 9 – 7 9

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its accuracy varies with image quality (5).Newer techniques for quantitative evalua-tion of LV function include speckle trackingechocardiography (STE), which allows mea-surements of myocardial deformation param-eters, such as global longitudinal strain(GLS). The strengths of GLS include betterreproducibility and ability to detect subtlechanges in myocardial function that precedechanges in EF, as reported in a variety of dis-ease states (6–8). Studies have shown thatGLS can also predict mortality, potentiallymore accurately than EF (9–15). Most out-comes studies focusing on LV function wereperformed using 2D echocardiography (2DE).

Three-dimensional echocardiography (3DE)offers better reproducibility and higher ac-

curacy than 2DE for the assessment of LV size andfunction (16) because it avoids apical foreshorteningand is based on direct volumetric measurementswithout geometrical assumptions. Furthermore,because 3DE can track myocardial motion indepen-dently of the imaging plane, 3DE-derived GLS mayalso be more accurate and reproducible (17,18).Recently, LV shape has been gaining interest with theavailability of 3DE analysis tools, and there is growingevidence that it may carry additional diagnosticand prognostic information (19–21). Accordingly, wehypothesized that: 1) 3DE parameters would be betterpredictors of cardiovascular (CV) mortality than 2DE;2) 3D GLS would be a better predictor of CV mortalitythan 3D EF; and 3) 3DE-derived shape indices couldalso predict CV mortality. This study was designed toinvestigate the relationship between these indicesand long-term survival.

SEE PAGE 1580

METHODS

POPULATION AND STUDY DESIGN. We retrospec-tively studied 416 inpatients (60 � 18 years of age; 213men [51%]; body surface area [BSA]: 1.79 � 0.28 m2),referred for a clinically indicated transthoracic echo-cardiography between 2006 and 2010, and who had2DE and 3DE images of sufficient quality to allow bothvolume measurements and STE-based LV deforma-tion analysis. Patients with atrial fibrillation or otherarrhythmias during echocardiographic examinationswere excluded. Clinical characteristics of our cohortare summarized in Table 1. Mortality data, includingCVmortality, were collected from hospital records andthe Social Security Death Index. 2DE and 3DE imageswere used to measure EF and GLS. In addition, 3D

shape analysis was performed to obtain global andregional LV shape indices. The risks for CV mortalityassociated with these indices were determined usingCox regression and Kaplan-Meier analyses. The studywas approved by the Institutional Review Board.

ECHOCARDIOGRAPHIC IMAGING AND ANALYSIS.

2DE and 3DE imaging was performed using commer-cial equipment (Philips iE33 imaging system with afully sampled matrix array transducer, Philips Medi-cal Systems, Andover, Massachusetts). 2DE imagingincluded apical 2-, 3-, and 4-chamber views, fromwhich LVEF was measured using the biplane methodof disks (Xcelera, Philips Medical Systems) and GLSusing 2D STE in all 3 views (Philips QLab). 3DE im-aging included multibeat full-volume datasets whilemaximizing frame rate, which was 18 � 3 Hz. 3DEimages were analyzed using commercial software (4DLV-Function, TomTec Imaging Systems, Unters-chleissheim, Germany) to quantify LVEF by semi-automated detection of the endocardial boundarieswith manual editing as necessary, and GLS by auto-mated 3D STE analysis (Figure 1).

LV shape analysis was performed using customsoftware that uses 3D endocardial surfaces exportedfrom TomTec. Briefly, the endocardial surfacesexpressed as a series of unstructured meshes of con-nected points were used as input for analysis of globaland regional LV shape via an algorithm described indetail previously (19,20) and summarized in Figure 2.Global indices included sphericity and conicity,expressed as numbers between 0 and 1, reflecting thedegree of similarity of the ventricle to a perfectsphere or cone, respectively (Figure 2, top). This wasachieved by sampling along a helical pattern on the3D LV surface and comparing the result with a signalobtained using the same procedure from an idealized,reference 3D shape, either a sphere or a cone.

Regional shape indices included curvature of 4 LVwalls (anterior, septal, inferior, lateral) reflecting 3Dcurvedness of the corresponding part of the endo-cardial surface, averaged over the cardiac cycle(Figure 2, bottom). This approach has been previouslyused in the context of 3D analysis of cardiac magneticresonance images (22). To achieve this, a quadraticpolynomial function was fit to the local neighborhoodof each point belonging to the LV endocardial surface.This allowed computing, for each point, the values ofcurvature k1 and k2, each corresponding to the in-verse of the radius of the 2 circles in orthogonalplanes best fitting the local surface. Then, the meannormalized curvature was obtained by averagingthese 2 curvature values and dividing by calculatedcurvature of a sphere having the same volume as the

TABLE 1 Clinical Characteristics of the Study Group

Dyslipidemia 50

Hypertension 69

Diabetes mellitus 26

Smoker 42

Paroxysmal atrial fibrillation 23

Status post ventricular tachycardia 11

Glomerular filtration rate <60 ml/min/1.73 m2 55

Coronary artery disease 44

Status post-myocardial infarction 20

Ischemic cardiomyopathy 25

Nonischemic cardiomyopathy 21

Pulmonary hypertension 26

Valvular cardiomyopathy 9

Congenital cardiomyopathy 6

Implantable cardioverter-defibrillator 18

Pacemaker 19

Values are %.

J A C C : C A R D I O V A S C U L A R I M A G I N G , V O L . 1 1 , N O . 1 1 , 2 0 1 8 Medvedofsky et al.N O V E M B E R 2 0 1 8 : 1 5 6 9 – 7 9 2D and 3D Echocardiographic Predictors of Mortality

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ventricle. Of note, 0 curvature indicates a flat surface,whereas the more positive or negative values signifymore convexity or concavity of the surface, respec-tively, from the perspective of a reference pointoutside the ventricle.

STATISTICAL ANALYSIS. Continuous variables arepresented as mean � SD, and categorical variables asnumbers and percentages. Baseline characteristics ofthe groups were compared using analysis of variancefor continuous variables.When datawere not normallydistributed, groups were compared with the Kruskal-Wallis test. Univariate comparisons were performedusing tests for paired data, including the paired t testfor normally distributed continuous variables, theWilcoxon matched pairs signed rank test for non-normally distributed continuous variables, and thechi-square test for categorical variables.

When collinearity between a pair of variables wasdetected by both Pearson and Spearman (to avoideffects of outliers) correlation analysis, separatemodels were created for multiple regression for eachvariable to determine the strength of the associationand risk of mortality. Cox proportional hazardsmodels were used to calculate hazard ratios (HRs) forCV mortality risk, whereas the non-CV deaths werecensored. Covariates that could influence the survivalrisk were included if found significant at p < 0.05 orconsidered clinically relevant based on previouspublications. The results of these separate analyseswere compared using global measures of model fit,including the –2 logarithmic likelihood (–2LL) test andreceiver-operating characteristic analysis area underthe curve � SD and 95% confidence interval (CI) to

determine the diagnostic accuracy of 2D and 3Dindices in predicting CV death. In addition, thisanalysis was repeated for a subgroup of 322 patients,after excluding non-CV deaths. Finally, the incre-mental contribution of 3D GLS and EF, compared withtheir respective 2D parameters, in predicting the riskof 5-year CV death was evaluated using categoricalnet reclassification improvement approach, usingthe survival data of the subjects with at least 5 yearsof follow-up.

Survival analysis over time included Kaplan-Meiersurvival curves for 5 years because this was the meanfollow-up time of the study. These curves were con-structed for LV function parameters (EF and GLSmeasured by both 2DE and 3DE in the entire cohort)and also for shape indices (in the subgroup thatexcluded the non-CV deaths) that were found signif-icant in the multiple regression analysis. Compari-sons of cumulative events across strata wereperformed using the log-rank test. Thresholds for EF,GLS, and shape indices were determined by dividingthe study group into tertiles for each index andtesting the separation between them.

All analyses were performed using SPSS softwareversion 22 (IBM, Armonk, New York). Statistical sig-nificance was inferred at p < 0.05.

RESULTS

During the mean follow-up of 5 � 3 years, 208 of416 (50%) patients died. Ninety-four patients (23%)died of non-CV causes. Of the remaining 322 pa-tients, 114 died of CV-related causes (35%). Table 2shows the results of all 2DE- and 3DE-derived LVsize, function, and shape parameters for the entirestudy group and for the survivors, and all-causedeaths and CV-related deaths. Both 2DE and 3DEmeasurements showed that LV volumes were lower,whereas LVEF and GLS magnitudes were higher inthe survivors group compared with nonsurvivors,with statistical significance in most comparisons.Global shape indices indicated lower spherical andmore conical LV shape in survivors compared withnonsurvivors, both reflecting a more physiologicalprolate ellipsoid shape. Regional shape analysisindicated that survivors had a significantly lowerinferior wall curvature and higher septal wall cur-vature. All of these differences, except the regionalcurvatures, were even more pronounced whensurvivors were compared with the CV mortalitygroup.

As expected, strong correlations between 2D EF, 3DEF, 2D GLS, and 3D GLS and multicollinearity were

FIGURE 1 3DE Volume and Deformation Analyses

Time

4Ch 2Ch 3Ch

LV V

olum

e (m

l)

0 100 200 300 400 500 600 700 800 900

100 200 300 400 500 600 700 800

Time

Seg

men

tal L

S (

%)

LV EndocardialCast

Example of 3DE dataset of the left ventricle (top). Endocardial boundaries initialized in 3 cross-sectional views extracted from the 3D dataset

(middle) are used to create a dynamic 3D cast of the ventricle (bottom, center), from which both dynamic volume (bottom, left) and

longitudinal strain (LS) (bottom, right) are calculated. 3DE ¼ 3-dimensional echocardiographic; Ch ¼ channel; LV ¼ left ventricular.



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detected, necessitating 4 separate regression modelsfor the Cox analysis. Each regression model includedage, BSA, and all global and regional indices of LVshape. Table 3 shows the results of the multipleregression for CV mortality; Table 4 summarizes theresults of a subgroup analysis (n ¼ 322), in which non-CV deaths were excluded. Both tables include theresults of both unadjusted (left) and adjusted (right)analyses.

The unadjusted Cox regression revealed that inaddition to age and BSA, all 4 LV function indices(2D EF, 3D EF, 2D GLS, 3D GLS), 1 of the 2 global shapeindices (namely conicity), and 1 of the regional shapeindices (inferior wall curvature) were associated withCV mortality (Table 3). The adjusted regression indi-cated that the global shape indices were no longersignificant, but the remaining indices were indepen-dently associated with CV mortality, although the

FIGURE 2 Global and Regional Shape Indices

.π

Global LV Shape analysis

Sphere

Shape index = 1 –A A

Amax Amax

ConeLV

ϑ ϑ

ϑϑϑ

LV

Regional LV shape analysis

Spherical shape index Conical shape index

LV curvatureLV surface

Concavity

k2 = k1 =

k1 + k2

R2

kn =

1

3 3V

4

R1

1

2

-2

R1

R2

0

Convexity

+2Normalized curvature Kn

Diagrammatic representation of the computation of sphericity and conicity shape indices of the left ventricle (top) and regional curvature of

the 4 different ventricular walls (bottom). Abbreviations as in Figure 1.


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inferior wall curvature was significant only in the 3Dmodels. Of note, reduced EF and GLS were associatedwith increased risk of CV mortality (as reflected byHRs <1 for EF and >1 for GLS).

Similarly, in the subgroup analysis excluding thenon-CV deaths, unadjusted Cox regression analysisshowed that the same indices with the addition ofseptal curvature were associated with increased risksof CV mortality (Table 4). The adjusted regressionanalyses indicated that, similar to the entire studycohort, none of the global shape indices were signif-icant, but the remaining indices were independentlyassociated with CV mortality, with the inferior wallcurvature being significant in all models and septalcurvature only in the 3D models (as reflected by HRs>1 for the inferior wall and <1 for the septum).

Comparisons between the 4 models for bothregression analyses revealed that 3D GLS was thestrongest predictor of CV mortality, reflected by thelowest –2LL value (Tables 3 and 4). Although this wasalso reflected by a higher receiver-operating

characteristic area under the curve value, the 95% CIshowed overlap between these 4 multivariate anal-ysis models of the respective LV function indices.Interestingly, 2D EF was the weakest among the 4indices and GLS was superior to EF for both 2DE and3DE analyses. Additionally, 3DE-derived GLS and EFwere superior to their 2DE counterparts. Importantly,each 1% decrease in GLS magnitude corresponded toan 11.3% increase in CV mortality in the entire cohort(Table 3) (HR: 1.113; 95% CI: 1.08 to 1.15; p < 0.001).Categorical net reclassification improvement analysisshowed an overall similarity between 3D GLS and 2DGLS (þ0.3%), whereas improvement of þ6.7% wasnoted in the accuracy of classification between 3D EFand 2D EF.

These findings were confirmed by Kaplan-Meiercurves (Figure 3), which demonstrated that both 2Dand 3D EF were not able to differentiate normal frommild-to-moderately reduced EF groups (left panels),whereas both 2D and 3D GLS were able to differen-tiate all these groups (right panels). Figure 4 shows

TABLE 2 Summary of Results of All 2DE- and 3DE-Derived LV Size, Function, and Shape Parameters for Entire Study Group and Survivors

and All-Cause and CV-Related Deaths

Total(N ¼ 416)

Survivors(n ¼ 208)

All-Cause Mortality(n ¼ 208)

CV Mortality(n ¼ 114)

p Value

Survivors vs.All-Cause Mortality

Survivors vs.CV Mortality

Age, yrs 60 � 18 54 � 17 65 � 17 67 � 17 <0.01 <0.01

2DE

Biplane EDV, ml 166 � 85 160 � 76 172 � 89 184 � 104 0.12 0.03

Biplane ESV, ml 88 � 77 79 � 66 96 � 82 111 � 97 0.01 <0.01

Biplane EF, % 52 � 16 54 � 14 50 � 17 46 � 18 <0.01 <0.01

3DE

EDV, ml 187 � 89 182 � 81 191 � 92 206 � 107 0.22 0.03

ESV, ml 99 � 84 88 � 75 109 � 87 128 � 103 0.01 <0.01

EF, % 52 � 17 55 � 15 48 � 17 44 � 18 <0.01 <0.01

2DE

GLS, % –15.6 � 4.9 –16.8 � 4.7 –14.4 � 4.6 –13.4 � 4.5 <0.01 <0.01

3DE

GLS, % –17.2 � 6.6 –19.0 � 6.1 –15.6 � 6.4 –14.0 � 6.6 <0.01 <0.01

Shape

Sphericity 0.64 � 0.06 0.63 � 0.05 0.65 � 0.06 0.65 � 0.06 0.01 0.01

Conicity 0.79 � 0.03 0.79 � 0.03 0.78 � 0.03 0.78 � 0.03 <0.01 <0.01

Anterior curvature 1.02 � 0.13 1.02 � 0.12 1.03 � 0.14 1.02 � 0.13 0.32 0.66

Septal curvature 0.98 � 0.13 1.00 � 0.11 0.96 � 0.15 0.98 � 0.14 0.01 0.15

Inferior curvature 1.00 � 0.14 0.98 � 0.14 1.02 � 0.13 1.02 � 0.14 <0.01 0.03

Lateral curvature 0.98 � 0.05 0.98 � 0.05 0.98 � 0.05 0.98 � 0.05 0.65 0.60

Values are mean � SD. Bold indicates p < 0.05.

2DE ¼ 2-dimensional echocardiographic; 3DE ¼ 3-dimensional echocardiographic; CV ¼ cardiovascular; EDV ¼ end-diastolic volume; EF ¼ ejection fraction;ESV ¼ end-systolic volume; GLS ¼ global longitudinal strain; LV ¼ left ventricular.

TABLE 3 Results of Cox Regression Analyses of LV Function and Shape Indices From 2DE and 3DE Images: Risk Assessment of Long-Term CV Mortality in Patients

Referred for Echocardiographic Examination

Cox Regression: CV Mortality (N ¼ 416)

Unadjusted Adjusted

p Value HR 95% CI

2D EF Model1,140.9*

0.628 � 0.32 (0.565-0.691)†

2D GLS Model1,134.0*

0.676 � 0.28 (0.620-0.731)†

3D EF Model1,133.1*

0.658 � 0.31 (0.597-0.719)†


0.690 � 0.29 (0.633-0.747)†

p Value HR 95% CI p Value HR 95% CI p Value HR 95% CI p Value HR 95% CI

Age <0.01 1.036 1.02–1.05 <0.01 1.032 1.02–1.05 <0.01 1.028 1.02–1.04 <0.01 1.033 1.02–1.05 <0.01 1.032 1.02–1.05

BSA 0.031 0.474 0.24–0.93 0.031 0.431 0.20–0.93 0.021 0.406 0.19–0.87 0.063 0.508 0.25–1.04 0.033 0.462 0.23–0.94

Sphericity 0.113 11.6 0.56–242 0.063 0.000 0.00–1.76 0.201 0.001 0.00–47.4 0.069 0.000 0.00–2.19 0.105 0.000 0.00–6.46

Conicity 0.031 0.002 0.00–0.58 0.208 0.000 0.00–1.5K 0.332 0.000 0.00–36K 0.385 0.000 0.00–100K 0.284 0.000 0.00–9.3K

Anterior 0.661 1.415 0.30–6.67 0.875 1.168 0.17–8.10 0.916 0.899 0.12–6.51 0.933 1.086 0.16–7.47 0.964 0.956 0.14–6.63

Septum 0.151 0.351 0.08–1.47 0.262 0.328 0.05–2.30 0.427 0.465 0.07–3.07 0.234 0.312 0.05–2.13 0.270 0.338 0.05–2.32

Inferior 0.027 4.835 1.20–19.5 0.058 4.959 0.95–25.9 0.109 3.827 0.74–19.8 0.039 5.47 1.09–27.4 0.045 4.939 1.03–23.6

Lateral 0.646 2.240 0.07–70.3 0.123 27.70 0.41–1.9K 0.820 1.655 0.02–126 0.240 12.48 0.19–838 0.383 6.47 0.10–431

2D EF <0.01 0.975 0.96–0.99 <0.01 0.963 0.95–0.98

2D GLS <0.01 1.123 1.08–1.17 <0.01 1.140 1.09–1.19

3D EF <0.01 0.973 0.96–0.98 <0.01 0.961 0.95–0.97

3D GLS <0.01 1.091 1.06–1.12 <0.01 1.113 1.08–1.15

*–2LL AUC. †AUC ROC � SE (95% CI). Bold indicates p < 0.05.

–2LL ¼ –2 logarithmic likelihood; AUC ROC ¼ area under curve in receiver-operating characteristic analysis; BSA ¼ body surface area; CI ¼ confidence interval; HR ¼ hazard ratio.



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TABLE 4 Results of Subgroup Analyses Excluding Noncardiovascular Deaths

Cox Regression: CV Mortality (N ¼ 322)

Unadjusted Adjusted

p Value HR 95% CI

2D EF Model1,092.5*

0.635 � 0.34 (0.569-0.701)†


0.698 � 0.30 (0.640-0.756)†

3D EF Model1,082.1*

0.672 � 0.32 (0.608-0.735)†


0.711 � 0.30 (0.652-0.769)†

p Value HR 95% CI p Value HR 95% CI p Value HR 95% CI p Value HR 95% CI

Age <0.01 1.038 1.03–1.05 <0.01 1.034 1.02–1.05 <0.01 1.029 1.02–1.04 <0.01 1.035 1.02–1.05 <0.01 1.034 1.02–1.05

BSA 0.012 .428 0.22–0.83 0.009 0.363 0.17–0.78 0.003 0.318 0.15–0.68 0.013 0.408 0.20–0.83 0.008 0.384 0.19–0.78

Sphericity 0.069 18.0 0.80–403 0.075 0.000 0.00–2.67 0.258 0.002 0.00–95.3 0.059 0.000 0.00–1.49 0.095 0.000 0.00–4.68

Conicity 0.017 .001 0.00–0.29 0.198 0.000 0.00–1.0K 0.414 0.000 0.00–14K 0.275 0.000 0.00–6.4K 0.211 0.000 0.00–1.1K

Anterior 0.429 1.913 0.38–9.56 0.809 1.268 0.18–8.71 0.991 1.011 0.15–6.95 0.923 1.098 0.17–7.29 0.812 0.791 0.11–5.46

Septum 0.039 .215 0.05–0.93 0.061 0.160 0.02–1.09 0.077 0.187 0.03–1.20 0.026 0.117 0.02–0.77 0.046 0.145 0.02–0.97

Inferior 0.005 7.099 1.80–27.9 0.017 7.523 1.43–39.5 0.039 5.723 1.10–29.9 0.008 9.40 1.82–48.6 0.012 7.631 1.57–37.0

Lateral 0.912 1.217 0.04–39.9 0.163 27.09 0.26–2.8K 0.566 3.857 0.04–389 0.107 44.57 0.44–4.5K 0.193 21.30 0.21–2.1K

2D EF <0.01 .976 0.96–0.99 <0.01 0.962 0.95–0.98

2D GLS <0.01 1.124 1.08–1.17 <0.01 1.154 1.10–1.21

3D EF <0.01 .974 0.96–0.98 <0.01 0.960 0.95–0.97

3D GLS <0.01 1.089 1.06–1.12 <0.01 1.116 1.08–1.15

Cox regression of LV function and shape indices obtained from 2DE and 3DE images: assessment of risks of long-term CV mortality. *–2LL AUC. †AUC ROC � SE (95% CI). Bold indicates p < 0.05.

Abbreviations as in Tables 2 and 3.


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the survival curves for the 2 regional curvatureindices (inferior and septal walls) that were found tobe significant in the subgroup Cox regression anal-ysis. Survival curves were significantly different forthe patients with the upper-tertile inferior wall cur-vatures (left panel), and for those with the lower-tertile septal curvatures (right panel), indicating thatthese patients were at higher risk of CV death. Ofnote, the division into tertiles resulted in slightlydifferent cutoffs for both EF and GLS in 2D and 3D.

DISCUSSION

LV function has important implications for clinicalmanagement and clinical trials. The parameter mostfrequently used to assess LV function is EF, which hasbeen shown to correlate with morbidity and mortalityand thus is used as a guide for the management of theindividual patient. Another parameter of LV functionthat emerged in the past decade is GLS. The recentguidelines emphasize recommendations for LV func-tion quantification by measuring both EF and GLS(23); however, the relative prognostic value of these 2indices has not been determined in the context of3DE, which was the focus of this study.

This study was possible because of the availabilityof a unique 3DE database including hundreds ofstudies dating from 2006, which allowed us an up todecade-long follow-up to investigate the relationshipbetween 3DE-derived indices of LV function and

long-term mortality. The inclusion criteria for thisstudy were the availability of good-quality 2DE and3DE images performed in any inpatients referred for aclinically indicated echocardiography study. This re-flects the feasibility of acquiring 3D LV full-volumedatasets of adequate quality as early as 2006, whichprovided the basis for the new chamber quantifica-tion guidelines (23) that recommend this methodol-ogy whenever possible. We also followed theseguidelines’ recommendation for 2D GLS measure-ments using 2-, 3-, and 4-chamber views, whichunderscores the true superiority of 3D over 2D GLSmeasurements.

One might question the high all-cause mortalityrate in our patient cohort, which was approximately50% over a 10-year period. We believe that this re-flects the high acuity of inpatients referred for cardiacultrasound examinations in a tertiary referral hospitaland the long-term follow-up in our study. This mayalso reflect that our hospital is located in an under-served urban area, where patient noncompliance isknown to be high because of socioeconomic factors,as well as the high prevalence of comorbidities(Table 1).

Our findings confirmed that both EF and GLS canbe used to predict CV mortality and that the predic-tive power of GLS is better than that of EF measuredby 2DE (1–4,9–15). In addition, to our knowledge, thisstudy is the first to show several clinically importantnew findings: 1) GLS was a better predictor of CV

FIGURE 3 Results of Survival Analysis for EF and Strain

p = 0.046

p < 0.01

p = 0.32

p < 0.01

p = 0.01

p = 0.02

p < 0.01

p = 0.12

p = 0.01

p < 0.01

p < 0.01

p = 0.01

2D EF

3D EF

2D GLS

3D GLS

Years

1.0

0.8

0.6

0.4

0.2

0.0

0 1 2 3 4 5

Cum

ulat

ive

Surv

ival

≥ 66.7% 47.4 to 66.7% ≤ 47.4%

1.0

0.8

0.6

0.4

0.2

0.0

0 1 2 3 4 5

Cum

ulat

ive

Surv

ival

Years139 93 81 68 54 48Higher138 86 74 65 54 44Mid139 89 70 59 52 39Lower

≥ 62.8% 46.6 to 62.8% ≤ 46.6%

1.0

0.8

0.6

0.4

0.2

0.0

0 1 2 3 4 5

Cum

ulat

ive

Surv

ival


≤ –18.4% –13.8 to -18.4% ≥ –13.8%

1.0

0.8

0.6

0.4

0.2

0.0

0 1 2 3 4 5

Cum

ulat

ive

Surv

ival


≤ –21% –14.5 to –21% ≥ –14.5%

138 84 75 65 51 43Higher139 91 78 69 58 47Mid139 93 72 58 51 41Lower

Kaplan-Meier survival curves for 2DE- and 3DE-based LVEF and GLS, stratified by tertiles both for EF and GLS. 2DE ¼ 2-dimensional

echocardiography; 3DE ¼ 3-dimensional echocardiographic; EF ¼ ejection fraction; GLS ¼ global longitudinal strain.



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mortality than EF not only by 2DE, but also by 3DE;2) 3DE is a better technique than 2DE to predict CVmortality for EF, although only minimally better forGLS; and 3) although global sphericity and conicityindices did not independently predict CV mortality,regional shape indices, namely curvature of the septaland inferior walls, were independent predictors ofmortality in addition to the previously mentioned LVfunction parameters.

Our finding that the predictive power of GLS isbetter than that of EF may be related to its superior

accuracy and reproducibility, when measured by both2DE (6–8) and 3DE (17,18). A possible reason for theimproved reproducibility and accuracy comparedwith EF may be related to the methodology of thesemiautomated speckle tracking algorithm. This isbecause 1 source of variability of the conventionaltechnique for EF measurement is the need to visuallyidentify the end-diastolic and end-systolic frames foranalysis, which is not perfectly reproducible. Eventhe chamber quantification guidelines from theAmerican Society of Echocardiography are ambiguous

FIGURE 4 Results of Survival Analysis for Regional Shape Indices

1.0

0.8

0.6

0.4

0.2

0.0

0 1

High tertile >1.06

2 3 4 5

Cum

ulat

ive

Surv

ival

Years222 161 141 126 112 99110 64 57 50 41 32

Inferior wall

p < 0.01

1.0

0.8

0.6

0.4

0.2

0.0

0 1

2 Higher Tertiles Low Tertile2 Lower Tertiles Higher Tertile

Low tertile <0.94

2 3 4 5Cu

mul

ativ

e Su

rviv

al

Years221 157 135 122 108 95111 68 63 54 45 36

Septal wall

p = 0.049

Kaplan-Meier survival curves for regional shape indices: inferior wall (left) and septal wall (right) curvature, stratified by tertiles for

each index.


1577

with regard to this frame selection (23). In contrast,GLS measurements are based on speckle trackingtechnology, which follows the myocardiumthroughout the cardiac cycle and thus includes everysingle frame in each analysis, avoiding this source ofvariability.

The changes in regional LV curvature that wereassociated with CV mortality may be a result of septalshift toward the left ventricle in patients with rightventricular pressure overload that is known to affectsurvival (24); such patients were relatively commonin our study cohort (26%). HR <1 for the septal wallindicated that the lower the curvature (the flatter theseptum) was, the higher the likelihood of CV mortal-ity would be. Conversely, HR >1 for the inferior wallindicated that the higher the curvature (the moreconvex the inferior wall) was, the higher the likeli-hood of CV mortality would be.

Interestingly, Kaplan-Meier curves (Figure 3)showed that both 2D and 3D EF were unable todifferentiate normal from mild-to-moderatelyreduced EF groups. Importantly, both 2D and 3DGLS were able to differentiate among all 3 GLS ter-tiles, reflecting the added predictive value of this in-dex over EF. This finding may reflect the improvedsensitivity of GLS, which may manifest itself in betterpredictive power for even lower degrees of LV

dysfunction. This is as opposed to EF, which wasassociated with higher mortality only in the severe LVdysfunction group.

One might question whether the small intergroupdifferences in global shape indices are meaningful.Although these differences were statistically signifi-cant because of extremely low intersubject variabilityreflected by the very small SDs (Table 2), whetherthese indices can be clinically significant remains tobe determined. Of note, this study showed that thesesmall differences were insufficient to give theseindices predictive power for CV mortality.

Our choices of statistical methodology used in thisstudy need explanation. The 4 LV function indices weaimed at comparing with respect to predictive power,namely 2D and 3D LVEF and GLS, are not indepen-dent of each other, but instead are strongly correlatedbetween them, which was confirmed by collinearitytests. One way to overcome this limitation anddetermine which parameter is the best was tocompare separate regression models that included 1of these parameters at a time. The strength of thesemodels was compared using advanced statisticalmeasures specifically designed for such circum-stances. The reason we performed a subgroup anal-ysis excluding the non-CV deaths was to eliminatedata “contamination” by irrelevant factors.

PERSPECTIVES

COMPETENCY IN MEDICAL KNOWLEDGE: We

compared the value of LVEF and GLS measured using

both 2DE and 3DE for predicting long-term mortality

in a cohort of inpatients who underwent clinically

indicated echocardiographic examinations. We found

that 3D measurements of LVEF predicted 5-year

mortality better than 2D LVEF, and that GLS was a

better predictor than EF. We also found that 3D

analysis of LV shape may provide additional risk

assessment.

TRANSLATIONAL OUTLOOK: Our study focused

on inpatients with good-quality images, thus limiting

the generalizability of our findings to consecutive

patients, outpatients with a wide range of image

quality, or patients with atrial fibrillation or other

types of arrhythmia during echocardiographic

examinations.



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STUDY LIMITATIONS. One limitation is the retro-spective nature of this study, which might havebiased the patient selection. For example, we focusedon inpatients with good-quality images, thus limitingthe generalizability of our findings. Morbidly obesepatients and those with suboptimal images because ofother conditions, such as lung disease, may not beadequately represented in our cohort. Accordingly,our results cannot be extrapolated to consecutivepatients or outpatients with a wide range of imagequality. Also, we cannot estimate the feasibility of 3Danalysis in this historical cohort of patients who wereselected on the basis of image quality over a decadeusing imaging equipment available at the time.Finally, this study only analyzed patients in sinusrhythm during imaging; thus, these results alsocannot be extrapolated to patients with atrial fibril-lation or other types of arrhythmia.

CONCLUSIONS

In summary, our results are the first to demonstrate asuperior predictive ability of 3D EF over 2D EF, andalso the value of adding to established risk factorsGLS, which was found to be a superior survivalprognostic factor. This study adds weight to the useof 3DE functional indices in echocardiographic ex-aminations for prognostic and not only diagnosticpurposes.

ADDRESS FOR CORRESPONDENCE: Dr. Roberto M.Lang, Department of Medicine, University of ChicagoMedical Center, 5758 South Maryland Avenue, MC9067 Room 5509, Chicago, Illinois 60637. E-mail:[email protected].

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KEY WORDS left ventricular function, leftventricular shape, outcomes, risk assessment
















































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