Statistical analysis
Continuous variables are expressed as mean ± SD. Categorical data are
summarized as frequencies and percentages. Baseline clinical
characteristics were compared between patients with ICD, low risk CRT-D
and high risk CRT-D, using Wilcoxon ranked sum test for continuous
variables and Chi2 - test for dichotomous variables,
as appropriate.
Step 1- covariate selection: We included 29 potential clinical,
electrocardiographic, and laboratory binary risk factors for VTA (Listed
in eTable A in the Online Appendix). Numeric variables were made binary
by the use of cut points with the goal of finding a simple, easily
implemented predictors to be derived from them. Thresholds for
categorization of numeric variables were based on the mean or clinical
relevance. Univariate relationships between candidate covariates and a
further event were assessed by t tests (2 for binary responses). The
covariates with values of P<0.20 were further evaluated by
carrying out a best-subset regression analysis, examining the models
created from all possible combinations of predictor variables, and using
a penalty of 3.84 on the likelihood ratio 2 value for any additional
factor included (corresponds to a P of 5% for a 1-df 2 test).
Step 2 – Creating the groups: For the main analysis,
CRT-D patients were grouped in two groups based on the presence or
absence of RF. In a secondary analysis, we further grouped CRT-D
patients into 3 groups; (i) No RF, (ii) One RF, and (iii) two or more
RF. Since the identified CRT-D RF were not associated with outcomes in
the ICD group (online supplemental Figure A), ICD patients were not
further grouped and were included as a control group
Step 3 – Outcomes by score analysis: Kaplan–Meier
estimates for any VTA, VTA or death, and appropriate shock in patients
with ICD and CRT-D, stratified by their risk, were determined and
statistically evaluated with the log-rank test. Multivariate Cox
proportional hazards regression analyses were carried out in the
subgroups for the assessment of the primary and secondary end points.
The following covariates were included in the Cox regression models:
age, gender, QRS width, New York Heart Association class NYHA,
creatinine, left ventricular ejection fraction, diabetes, and ischemic
origin.
All statistical tests were two-sided, a p-value of <0.05 was
considered statistically significant. Analyses were carried out with SAS
software (version 9.4, SAS institute, Cary, North Carolina).