Ingram Wendy Marie, Weston Cody, Dar Lu Wei, Hodge Caleb, Poler S Mark, Nahi Fatin, Larson Sharon
Corresponding author: E-mail address -
Geisinger Health System, Danville, Pennsylvania.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:672-679. eCollection 2019.
Electroconvulsive therapy (ECT) is an effective and rapid treatment for severe depression, however predictors of therapeutic outcomes remain insufficiently understood. Ictal duration and postictal suppression are two outcomes that may be correlated with patient response, yet patient and treatment variables which may influence these outcomes have not been thoroughly explored. We collected ECT stimulus metrics, EEG parameters, patient demographics, primary diagnosis, and anesthesia type for retrospective ECTs. Univariate and multivariate mixed-effects linear regression models were used to identify variables associated with ictal duration and postictal suppression index. For both outcomes, multivariate models which included all variables resulted in the best fit, reflecting the complex influences of a variety of factors on the ictal response. These results are an important step forward in elucidating patterns in retrospective ECT clinical data which may lead to new clinical knowledge of modifiable factors to optimize ECT treatment outcomes.
电休克疗法(ECT)是治疗重度抑郁症的一种有效且快速的方法,然而对治疗结果的预测因素仍未得到充分了解。发作期持续时间和发作后抑制是两个可能与患者反应相关的结果,但可能影响这些结果的患者和治疗变量尚未得到彻底探究。我们收集了回顾性电休克治疗的ECT刺激指标、脑电图参数、患者人口统计学资料、主要诊断和麻醉类型。使用单变量和多变量混合效应线性回归模型来识别与发作期持续时间和发作后抑制指数相关的变量。对于这两个结果,包含所有变量的多变量模型拟合效果最佳,反映了多种因素对发作期反应的复杂影响。这些结果是阐明回顾性ECT临床数据模式的重要一步,这可能会带来关于可调整因素的新临床知识,以优化ECT治疗结果。