Statistical analysis
Continuous variables are expressed as mean ± SD. Categorical data are
summarized as frequencies and percentages. Median QTc baseline (first
hour post CV), median QTc during the second hour (conventional
monitoring), and the median QTc every 4 consecutive hours (Holter
monitoring) were displaced in a graph with 25%-75% confidence
interval, where the Y axis is the QTc, and X axis is time from CV.
We included 18 potential clinical, electrocardiographic,
echocardiographic and laboratory binary risk factors for QTc
prolongation (online supplemental eTable A). 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 value.
Univariate relationships between candidate covariates and a further
event were assessed by t tests (2 for binary responses). The covariates
with values of P<0.10 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). Model selection was
repeated after unselected factors were dropped, one at a time, to
minimize the effects of missing data.
Detection rates were calculated as a fraction of all patients who had
received 7-day Holter monitoring. The cumulative probability of AF was
displayed according to the Kaplan-Meier method. The differences between
detection rates for different monitoring intervals were tested using
McNemar’s test as appropriate. 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).