Statistical analysis
Continuous data were expressed as mean ± standard deviation (SD) or
median and interquartile range (IQR). BSA was calculated using Haycock’s
method 7. To build the z-model of a parameter (i.e.,
ESV, EDV, and SV), we selected an optimum exponent, α, of the index
parameter (parameter/BSAα) such that: 1) The index
parameter satisfactorily follows a normal distribution and 2)The index
parameter does not depend upon BSA. Z-score was then calculated as
\(Z=\frac{[\ \left(\frac{\text{parameter}}{\text{BSA}^{\alpha}}\right)-\left(\text{mean\ value\ of\ indexed\ parameter}\right)]}{\text{SD\ of\ indexed\ parameter}}\).6Normality of an indexed parameter was evaluated using Shapiro-Wilk and
Kolmogorov-Smirnov tests, Q-Q plot, skewness and kurtosis. Dependence of
the indexed parameter on BSA was evaluated with a test of the slope of
the linear regression of the indexed parameters on BSA. We conducted
grid search with a 0.001 step size to find the optimum exponent, α, and
chose the one that maximized the sum of p-value for Shapiro-Wilk test
and the p-value of testing the slope of index parameter vs. BSA. During
the model development, diagnostic analysis was conducted using
leave-one-out method. Few data points with extreme values that
influences the distribution of indexed parameter were excluded from the
final z-model development. After the optimum z-score model has
determined, association of indexed parameter with age and gender were
further examined with respectively linear regression and Student t-test.
Gender specific z-scores model hence developed because there was a
difference between genders in indexed parameter. A two-sided p-value
<0.05 was considered statistically significant. Intraobserver
and interobserver variability for LV EDV, ESV, and SV were calculated
using intraclass coefficient (ICC). Repeatability coefficient (RC) was
used to assess intraobserver and interobserver variability for LV EF.
Confidence interval (CI) for RC was calculated using percentile method
with 5000 Bootstrap samples. Statistical analysis was performed using
SAS version 9.4 (SAS institute, Cary, NC).