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).