Malkin R A, Pilkington T C, Ideker R E
University of Memphis, Department of Biomedical Engineering, Herff College of Engineering, TN 38152, USA.
IEEE Trans Biomed Eng. 1996 Jan;43(1):69-78. doi: 10.1109/10.477702.
It is frequently necessary, both clinically and in the laboratory, to estimate how strong a stimulus is required to defibrillate. Current techniques for forming such estimates require the repeated induction of ventricular fibrillation (VF) and subsequent attempts at defibrillation (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 correlated with defibrillation efficacy. A Bayesian statistical model of combined ULV/DF testing is presented which is both powerful and concise. The model is used in two examples to design minimum rms error protocols and estimators for the DF95 (the stimulus strength which defibrillates 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 DF 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.
无论是在临床还是实验室环境中，经常需要估算除颤所需刺激的强度。目前用于形成此类估算的技术需要反复诱发心室颤动（VF）并随后进行除颤尝试（DF测试）。DF测试可能很耗时，并且在手术室中可能会增加患者风险。本文提出了一种将DF测试与易损性上限（ULV）测试相结合的新方案。ULV测试是一种相对安全的程序，其产生的数据与除颤效果密切相关。本文提出了一种结合ULV/DF测试的贝叶斯统计模型，该模型既强大又简洁。该模型在两个示例中用于设计最小均方根误差协议和DF95（95%时间能除颤的刺激强度）的估计器。对一个示例解决方案进行的人体模拟表明，单次ULV/DF联合测试的VF发作（均方根误差为平均DF95的23%）优于仅进行两次DF测试的VF发作（25%）。第二个示例的模拟结果与六头猪的实验室结果直接比较，结果显示模拟均方根误差与测量均方根误差之间的平均差异小于1.0%。
