ESTIMATING DEFIBRILLATION EFFICACY USING COMBINED UPPER LIMIT OF VULNERABILITY AND DEFIBRILLATION TESTING

It is frequently necessary, both clinically and in the laboratory, to estimate how strong a stimulus is required to defibrillate. Current te chniques for forming such estimates require the repeated induction of ventricular fibrillation (VF) and subsequent attempts at defibrillatio n (DF testing). DF testing can be time consuming and in the operating room may increase the patient risks. A novel scheme is presented which combines DF testing with upper limit of vulnerability (ULV) testing, ULV testing is a relatively safe procedure which yields data well corr elated with defibrillation efficacy. A Bayesian statistical model of c ombined ULV/DF testing is presented which is both powerful and concise . The model is used in two examples to design minimum rms error protoc ols and estimators for the DF95 (the stimulus strength which defibrill ates 95% of the time), A simulation for humans of one example solution shows that a single VF episode of combined ULV/DF testing (rms error = 23% of the mean DF95) is better than two VF episodes with DP testing alone (25%). The simulation results for a second example are directly compared with laboratory results from six pigs, showing a less than 1 .0% average difference between the simulated and measured rms errors.