Statistical Analysis
Assessment of the relationship between the routine echocardiographic diastolic parameters and 2D-STE derived parameters was done using repeated measures mixed linear regression models with e’ or E/e’ as the dependent variable, strain measurements as fixed independent variables, and patient serial number as the random variable. To assess the predictive ability of Dst on significant GLS reduction, individual logistic regression models were built with significant GLS reduction as the dependent variable and relative change in each Dst segment between T1 and T2 as independent variables. Using the results of the above models the best predictor of significant GLS reduction was used in a multivariate model to assess its ability to independently predict significant GLS reduction. First, a multivariate model was built with covariates including relative GLS reduction between T1 and T2, baseline cardiac risk factors, cardiotoxic chemotherapy used and cardio-protective medication used. The above primary model was then narrowed using a stepwise forward and backwards Akaike information criterion (AIC) based method in order to select the best predictive model which has lowest AIC. To further illustrate the diagnostic predictive power of Dst alone or in combination with the other model covariates receptor-operator (ROC) curves were built and AUC with 95% CI and Youden indexes were calculated. Comparison between AUC of ROC curves was done using the DeLong & DeLong method. To detect whether adding Dst data contributed to the multivariate model predictive ability, net classification index (NRI) was calculated for a logistic model with and without the added variable, 95% confidence interval for NRI (and its positive and negative components) was calculated using a bootstrapping method. Continuous variables are shown as mean±SD, while discrete variable as n(%). Results were considered significant when p<0.05. As this is a primary proof of concept investigation, all assessments were considered hypothesis generating and were not corrected for multiple comparisons. All calculations were done using R version 3.5.0, R Foundation for Statistical Computing, Vienna, Austria.