Online Appendix for the following JACC article
TITLE: Reproducibility of Echocardiographic Techniques for Sequential Assessment
of Left Ventricular Ejection Fraction and Volumes: Application to Patients
Undergoing Cancer Chemotherapy
AUTHORS: Paaladinesh Thavendiranathan, MD, MSc, Andrew D. Grant, MD,
Tomoko Negishi, MD, Juan Carlos Plana, MD, Zoran B. Popović, MD, PhD, Thomas H.
Marwick, MD, PhD, MPH
APPENDIX
Supplementary Methods
Expanded Statistical Analysis
To calculate the standard error of the measurement (SEM) for each technique for the
entire follow-up period one way analysis of variance (ANOVA) was used. The EF or
volumes from each technique was used as the dependent factor while patient (each
patient was provided an ID from 1 to 56) was used as fixed factor. The square root of
the error term was used as the measure of the temporal variability in EF for each
technique.
Intra-observer and inter-observer variability were determined using the approach
described by Eliasziw et al. (1). First, two way ANOVA was performed using EF or
volumes as the dependent factor, with observers and patients as fixed factors. From
this analysis, the mean squared error for observers, patients, observer-patient
interaction, and residuals was used as described by Eliasziw et al. to calculate the
inter-observer and intra-observer mean square error (MSE). In this analysis the
observers were treated as random factors. The square root of the MSE was the
calculated variability. The intra-observer variability consisted of the overall
(“average”) intra-observer variability across the two observers, while the interobserver variability consisted of the variability among observers’ measurements and
the variability within observers’ measurements. This inter-observer variability is more
clinically relevant as it illustrates the disagreement between the observers as well as
the imprecision with which each observer makes the measurements. Traditionally
inter-observer variability estimates have only provided the variability between the two
observers assuming that the measurement by each observer is error free (an
assumption that is not necessarily correct).
The inter-observer test-retest variability was also calculated using two-way ANOVA.
EF or volumes were used as the dependent factor while patients and observers were
used as fixed factors. The Eliasziw et al. (1) method as described above was
subsequently used to calculate the inter-observer test-retest variability. This measure
consists of variability within observers, between observers, and over time. All
statistical analysis was performed using SPSS (ver 19.0.0, IBM Corporation, Chicago,
IL).
Supplementary Results
Table A: Temporal variability represented as coefficient of variation (COV) and 95%
CI for all methods for all follow-up periods.
Method
EF COV (95% CI), %
EDV COV (95% CI), %
ESV COV (95% CI), %
7.4 (6.2 - 9.1)
16.2 (13.7 – 20.0)
22.0 (18.5-27.0)
8.4 (7.0 – 10.5)*
16.0 (13.3 – 20.0)
23.6 (19.7 – 29.6)
9.4 (7.9 – 11.5)
23.0 (19.4 – 28.2)*
26.2 (22.1 – 32.3)
9.4 (7.8 – 11.8)
20.1 (16.7 – 25.2)
23.6 (19.7 – 29.7)
3D
4.0 (3.3 – 4.9)
11.9 (10.0 – 14.7)
13.2 (11.1 – 16.2)
3D + Contrast
7.2 (6.0 - 9.1)*
16.6 (13.8 – 20.9)*
20.0 (16.5 – 25.1)*
Bi-Plane
Bi-Plane +
Contrast
Triplane
Triplane +
Contrast
Non-contrast 3D had the lowest temporal variability based on COV for EF, EDV, and
ESV compared to all other methods (p<0.01 for all). *statistically different when
compared to the respective non-contrast method (p<0.05).
Table B: Temporal variability and 95% CI for first 3 visits only where data were
available for all patients (N=56).
Non-contrast 3D had the lowest temporal variability for EF, EDV, and ESV compared
Method
EF SEM (95% CI)
EDV SEM (95% CI), ml
ESV SEM (95% CI), ml
Bi-Plane
0.050 (0.045 – 0.055)
19.1 (17.2 – 21.3)
7.7 (7.0 – 8.7)
Bi-Plane + Contrast
0.057 (0.051 – 0.065)
16.2 (14.4 – 18.4)*
7.9 (7.0 – 8.9)
Triplane
0.061 (0.055 – 0.069)
15.4 (13.9 – 17.3)
8.0 (7.2 – 9.0)
0.069 (0.061-0.078)
19.7 (17.5 – 22.2)*
8.8 (7.8 – 9.9)
3D
0.027 (0.024 – 0.031)
10.0 (8.9 – 11.2)
4.5 (4.0 – 5.0)
3D + Contrast
0.052 (0.046 – 0.060)*
17.0 (15.40– 19.3)*
7.6 (6.7 – 8.6)*
Triplane + Contrast
to all other methods (p<0.01 for all). *statistically different when compared to the
respective non-contrast method (p<0.05).
Table C: Temporal variability and 95% CI for any 3 visits where data from all
techniques were available at each visit (N=32)
Non-contrast 3D had the lowest temporal variability for EF, EDV, and ESV compared
Method
EF SEM (95% CI)
EDV SEM (95% CI), ml
ESV SEM (95% CI), ml
0.049 (0.043-0.057
13.9 (12.1 – 16.1)
8.5 (7.4 – 9.8)
Bi-Plane + Contrast
0.051 (0.044 – 0.059)
15.0 (13.0 – 17.3)
7.1 (6.2 – 8.3)*
Triplane
0.063 (0.055 – 0.073)
21.0 (18.3 – 24.3)
9.8 (8.6 – 11.4)
Triplane + Contrast
0.061 (0.053 – 0.070)
21.0 (18.3 – 24.3)
10.7 (9.4 – 12.4)
0.029 (0.025-0.033)
10.1 (8.8 – 11.7)
4.5 (3.9 – 5.2)
18.9 (16.5 – 21.9)*
8.2 (7.2 – 9.5)*
Bi-Plane
3D
3D + Contrast
0.055 (0.048 –
0.064)*
to all other methods (p<0.01 for all). *statistically different when compared to the
respective non-contrast method (p<0.05).
References
1.
Eliasziw M, Young SL, Woodbury MG, Fryday-Field K. Statistical
methodology for the concurrent assessment of interrater and intrarater
reliability: using goniometric measurements as an example. Physical therapy
1994;74:777-88.