Lebedeva Viktoria K, Klitcenko Olga A, Lebedev Dmitry S, Lyubimtseva Tamara A
Arrhythmology Department, National Almazov Medical Research Centre, 2 Akkuratova St., St. Petersburg, 197341, Russian Federation.
North-Western State Medical University Named After I.I Mechnikov., 41 Kirochnaya St., St. Petersburg, 191015, Russian Federation.
Indian Pacing Electrophysiol J. 2019 Mar-Apr;19(2):57-62. doi: 10.1016/j.ipej.2018.11.011. Epub 2018 Nov 25.
Clinical data analysis of 83 patients with implantable cardioverter-defibrillators (ICDs) for sudden cardiac death (SCD) primary prevention has been done. We revealed 5 parameters associated with the detection of life-threatening ventricular arrhythmias. These parameters formed the basis for constructing a logistic regression model. The model makes it possible to obtain the probability of occurrence of a specific event depending on the severity of the predictive parameters and the degree of its influence (risk of true ventricular arrhythmias detection). Estimating the potential risk of the life-threatening arrhythmias, individual programming options are required in implantable cardioverter-defibrillators (ICDs) to reduce the amount of unnecessary electrotherapy, as well as more accurate monitoring of the patient's drug therapy.
对83例植入式心脏复律除颤器(ICD)用于心脏性猝死(SCD)一级预防的患者进行了临床数据分析。我们发现了5个与危及生命的室性心律失常检测相关的参数。这些参数构成了构建逻辑回归模型的基础。该模型能够根据预测参数的严重程度及其影响程度(检测真正室性心律失常的风险)获得特定事件发生的概率。为了估计危及生命的心律失常的潜在风险,植入式心脏复律除颤器(ICD)需要采用个体化编程选项,以减少不必要的电治疗次数,并更准确地监测患者的药物治疗情况。