Efficacy and Safety of Electroconvulsive Therapy in Patients With Deep Brain Stimulation
Bibliographic record
Abstract
AIM: Deep brain stimulation (DBS) has proven to be an effective therapy of some treatment-resistant psychiatric disorders and movement disorders. Comorbid depressive symptoms are common and difficult to manage. Treatment with electroconvulsive therapy (ECT) may be required. There are few published cases describing the safety and efficacy of ECT for patients with DBS implants, and there are no available guidelines for administration of ECT in patients with DBS and mood disorders. The current study had 3 aims: (i) to conduct a systematic review of case reports on patients with DBS implants who received ECT; (ii) to report the case of a 69-year-old man with a DBS implant for essential tremor, who required ECT; and (iii) to provide practical recommendations for ECT in patients with DBS implants. METHODS: We conducted a systematic review, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, of existing case reports on patients with DBS implants administered ECT for psychiatric disorders. RESULTS: Our search yielded 25 cases of ECT in patients implanted with DBS systems. In addition, we here describe successful ECT management of major depressive disorder in a patient treated by DBS. We also set forth ECT management guidelines based on points of consensus. The 2 most important practical recommendations are to make sure the DBS system is set to 0 V and turned off before ECT, and to avoid sites near the DBS electrodes. CONCLUSIONS: Electroconvulsive therapy may be an effective and safe treatment for DBS patients with MDD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".