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