Electroconvulsive Therapy for Bipolar Depression in Older Adults
Bibliographic record
Abstract
OBJECTIVES: Electroconvulsive therapy (ECT) is a well-established treatment for mood disorders in younger adults and has been consistently shown to be safe and effective in unipolar depression in older adults. However, data on this treatment in older adults with bipolar disorder are limited. In this retrospective study, we report outcomes from all cases of older adults with bipolar depression who received ECT from a large academic institution over a 7-year period. METHODS: We retrospectively identified all patients 65 years and older with bipolar depression who were treated with ECT over a 7-year period. Patients receiving ECT for an episode of bipolar depression were included in the study based on chart review and availability of documented outcome measures. Primary outcomes were changes in Montreal Cognitive Assessment and Clinical Global Impressions scores. RESULTS: We identified 34 patients meeting inclusion criteria. Collectively, patients had statistically significant improvement in Montreal Cognitive Assessment scores and reductions in Clinical Global Impressions severity scores after treatment. Pre- and posttreatment Montgomery-Asberg Depression Rating Scale scores were also available for a subset of 20 patients and demonstrated a similarly significant reduction in severity with treatment. There were no serious adverse effects of treatment, and no patients discontinued treatment. CONCLUSIONS: Electroconvulsive therapy was well tolerated and effective in treating bipolar depression in older adults. Importantly, these findings challenge commonly held worries about cognitive decline in older adults receiving ECT. It should be a regular consideration for management of this challenging illness in a population that may otherwise not respond to pharmacotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".