A novel seizure quality index based on ictal parameters for optimizing clinical decision-making in electroconvulsive therapy. Part 2

• Verfasst von: Kranaster, Laura [VerfasserIn]
• Verfasst von: Jennen-Steinmetz, Christine [VerfasserIn]
• Verfasst von: Sartorius, Alexander [VerfasserIn]
• Titel: A novel seizure quality index based on ictal parameters for optimizing clinical decision-making in electroconvulsive therapy. Part 2
• Titelzusatz: Validation
• Verf.angabe: Laura Kranaster, Christine Jennen-Steinmetz, Alexander Sartorius
• Jahr: 2019
• Jahr des Originals: 2018
• Umfang: 7 S.
• Fussnoten: First Online: 10 December 2018 ; Gesehen am 01.10.2019
• Titel Quelle: Enthalten in: European archives of psychiatry and clinical neuroscience
• Ort Quelle: Darmstadt : Steinkopff, 1868
• Jahr Quelle: 2019
• Band/Heft Quelle: 269(2019), 7, Seite 859-865
• ISSN Quelle: 1433-8491
• DOI: doi:10.1007/s00406-018-0962-7
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Depression
• Sach-SW: Electroconvulsive therapy
• Sach-SW: Outcome
• Sach-SW: Prediction
• K10plus-PPN: 1677985348

Abstract

Early identification of patients who are at a high risk for an unfavorable outcome to ECT during the treatment course might be beneficial because it provides an opportunity for timely intensification or optimization of stimulus conditions. We aimed to validate a previously developed seizure quality index (SQI) that delivers a clinically relevant outcome prediction early in the treatment course and can be used within common clinical setting. Therefore, a prospective study was conducted. Patients (n = 26) below the age of 65 years with a depressive episode and the clinical decision for ECT (right unilateral, brief pulse) were included and several ictal parameters, the SQI for non-response and non-remission, and the clinical outcome of the patients were documented. Logistic regression analysis revealed a statistically significant association between the SQI and non-response (p = 0.035). A significant association between the clinical outcome of non-response and the classified outcome of non-response was detected (p = 0.041). The overall classification accuracy regarding response/non-response was 71.3%, and the model revealed a sensitivity of 84.6% and a specificity of 61.5% for non-response. In this study, we could validate the SQI for the clinical outcome of non-response, but not for non-remission. Based on our data, the SQI might become an interesting clinical tool for early outcome prediction for ECT in patients with depression.