Selection of patients for programmed ventricular stimulation: a clinical decision-making model based on multivariate analysis of clinical variables.

OBJECTIVE

This study was conducted to assess the utility of clinical variables in predicting the inducibility of sustained ventricular arrhythmias in a heterogeneous group of patients undergoing programmed ventricular stimulation.

METHODS

Variables were considered in a simulated chronologic order to determine the incremental information added by the signal-averaged electrocardiogram (ECG) and left ventricular ejection fraction. All patients undergoing baseline programmed ventricular stimulation for induction of ventricular tachyarrhythmia during a 30-month period were included in the study. Fourteen historical, ECG, signal-averaged ECG and left ventricular wall motion variables were evaluated for their ability in predicting inducibility of a sustained ventricular arrhythmia, a "positive" event, at programmed ventricular stimulation.

RESULTS

On univariate analysis of the clinical variables, comparison between patients with positive or negative results showed significant differences in 10 of the 14 clinical variables: major cardiac diagnosis, history of ventricular tachycardia, myocardial infarction by history or ECG, all five signal-averaged ECG variables, left ventricular ejection fraction and presence of left ventricular aneurysm. On multivariate analysis, five independent variables were determined to be important: history of ventricular tachycardia, historical or ECG evidence of myocardial infarction, history of loss of consciousness, filtered QRS duration on the signal-averaged ECG and left ventricular ejection fraction. However, with sequential multivariate analysis, a model based only on historical and conventional ECG data was found to do as well as a model that included signal-averaged ECG and left ventricular ejection fraction data.

CONCLUSIONS

Routinely available noninvasive historical, ECG, signal-averaged ECG and left ventricular wall motion variables can be used to accurately predict the outcome of programmed ventricular stimulation. The majority of the predictive power was obtained with the routine model, using only historical and ECG data. The signal-averaged ECG and left ventricular wall motion analysis added no significant incremental information.