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).