2.3. Statistical analysis
non-DCM
REGRESSION
Statistical analyses were performed with IBM SPSS version 26.0 (SPSS Inc., Chicago, IL) and R version 4.1.3. The patients were randomly divided into a training dataset (80%) and a validation dataset (20%) using the “caret” package in R 4.1.3. The training dataset was used to construct the nomogram and the validation dataset was used for external validation. Continuous variables were reported as means ± standard deviations (SDs) or medians (minimum value, maximum value) depending on the data distribution. Categorical variables were reported as frequencies (proportions). Differences between DCM and non-DCM groups were tested by independent sample t-test or Mann–Whitney–Wilcoxon test for continuous variables and chi-square test or Fisher’s exact test for categorical variables. Univariate analyses of ECG variables were performed to identify potential indicators screening DCM. Forward stepwise logistic regression was performed to determine the indicators. Multivariate binary logistic regression analysis with indicators was conducted by the “glm2” package and used to construct the final screening model, which was performed as a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate discrimination and screening ability of the model. The optimal cutoff value was determined using Youden index. Hosmer-Lemeshow test was conducted by the “generalhoslem” package. The decision curve analysis was conducted by the “ggDCA” package and applied to evaluate the clinical effects under the optimal cutoff value of the validation dataset. A 2-sided 