The anticholinergic burden is not associated with cognitive impairments in patients treated by electroconvulsive therapy for treatment-resistant depression
Bibliographic record
Abstract
BACKGROUND: Electroconvulsive therapy (ECT) is the most effective non-pharmacological treatment for treatment-resistant depression (TRD) but can expose to transient cognitive impairments. Understanding factors underlying these cognitive side effects is important. This study investigated the impact of anticholinergic treatments on cognitive performances after ECT courses for TRD in naturalistic condition. METHODS: Impact of anticholinergic burden (Anticholinergic Impregnation Scale, AIS) on cognitive changes (Montreal Cognitive Assessment, MoCA) adjusted on depression level (Montgomery and Asberg Depression Scale, MADRS) was investigated in 42 patients who received an ECT course between 2017 and 2020 for unipolar or bipolar TRD. Collection of daily treatments given during ECT was carried out via the computerized traceability of treatments validated by nurses. RESULTS: Among the 31 treatments identified with an anticholinergic score, which represent only 38% of total treatments, the three most frequently given treatments were Lorazepam (47%), Venlafaxine (36%) and Cyamemazine (26%). Delayed recall was the most frequently impaired cognitive function after ECT courses. Using logistic regression, we found no association between the anticholinergic burden and the decrease in cognitive scores after ECT courses, adjusted on MADRS score evolution (p > 0.1). Conversely, improvement in MADRS scores were correlated with improvement in attention MoCA subscores. LIMITATIONS: This is a retrospective monocentric study with a moderate sample size using anticholinergic scales to calculate the anticholinergic burden without plasma dosage. CONCLUSION: Anticholinergic treatments did not seem to explain ECT-related cognitive impairments. This warrants further large prospective investigations including different measures of anticholinergic burden.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".