| Title: |
Prediction of electroconvulsive therapy outcome: A network analysis approach |
| Authors: |
Blanken, Tessa F.; Kok, Rob; Obbels, Jasmien; Lambrichts, Simon; Sienaert, Pascal; Verwijk, Esmee |
| Source: |
ISSN:0001-690X ; ISSN:1600-0447 ; Acta Psychiatrica Scandinavica, vol. 151 (4), (521-528. |
| Publisher Information: |
Wiley |
| Publication Year: |
2025 |
| Subject Terms: |
Science & Technology; Life Sciences & Biomedicine; Psychiatry; depression; electroconvulsive therapy; network analysis; prediction; COGNITIVE-BEHAVIORAL THERAPY; MAJOR DEPRESSION; RESOLUTION; SYMPTOMS; EFFICACY; IDEATION; SCALE; ECT; 11 Medical and Health Sciences; 17 Psychology and Cognitive Sciences; 32 Biomedical and clinical sciences; 42 Health sciences; 52 Psychology |
| Description: |
OBJECTIVE: While electroconvulsive therapy (ECT) for the treatment of major depressive disorder is effective, individual response is variable and difficult to predict. These difficulties may in part result from heterogeneity at the symptom level. We aim to predict remission using baseline depression symptoms, taking the associations among symptoms into account, by using a network analysis approach. METHOD: We combined individual patient data from two randomized controlled trials (total N = 161) and estimated a Mixed Graphical Model to estimate which baseline depression symptoms (corresponding to HRSD-17 items) uniquely predicted remission (defined as either HRSD≤7 or MADRS |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://lirias.kuleuven.be/handle/20.500.12942/768105; https://doi.org/10.1111/acps.13770; https://pubmed.ncbi.nlm.nih.gov/39529486 |
| DOI: |
10.1111/acps.13770 |
| Availability: |
https://lirias.kuleuven.be/handle/20.500.12942/768105; https://hdl.handle.net/20.500.12942/768105; https://lirias.kuleuven.be/retrieve/05522965-de31-496b-848c-4506c5b19173; https://doi.org/10.1111/acps.13770; https://pubmed.ncbi.nlm.nih.gov/39529486 |
| Rights: |
info:eu-repo/semantics/openAccess ; public ; https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.243DA9D3 |
| Database: |
BASE |