2.3 Statistical Analysis
Continuous variable distributions are expressed as means ± standard deviation and median with interquartile range (IQR) and compared with Student’s t-test or Mann-Whitney U test. Categorical variables are summarized as frequency and percentage and compared with the Chi-square test or Fisher’s exact test. Patients lost to follow-up and missing values were excluded from the analysis. Pacing burden showed bimodal distribution and was categorized into three tertiles and analysed with the lowest tertile as the reference group. Multivariable analysis was done using binary logistic regression by selecting variables with p-value < 0.05 on univariate analysis. Potential confounders previously found to have been associated with heart failure i.e. age, sex, presence of diabetes, presence of hypertension, beta-blocker use, and angiotensin-converting enzyme inhibitor use were also added to the model. The point estimates are reported as odds ratio (OR) and 95% confidence interval (CI). Receiver operator characteristics (ROC) analysis was performed on continuous variables found to be statistically significant on multivariable analysis. Categories were created based on the 90% specificity cut-off values of these variables. In the case of pacing burden, the 33rd percentile value separating the lowest tertile from the upper two tertiles was used as a cut-off. After these factors were identified, patients were categorized into those with no risk factors, one risk factor, and two or more risk factors. Binary logistic regression was performed with patients having none of the risk factors as the reference group. Interactions between these factors were not tested. A two-sided p-value of less than 0.05 was considered statistically significant. Statistical analysis was done with SPSS ® software (Ver. 16.0, IBM, USA).
Results