Predictors of Electroconvulsive Therapy Use in a Large Inpatient Psychiatry Population
Bibliographic record
Abstract
OBJECTIVE: There is limited research on reliable and clinically useful predictors of electroconvulsive therapy (ECT) use. We aimed to examine factors that predict ECT use in an inpatient psychiatric population. DESIGN: Retrospective analysis of provincial database for inpatient psychiatry. METHODS: This study is a retrospective analysis of a provincial database for inpatient psychiatry. The study includes all psychiatric inpatients 18 years or older in Ontario, Canada, assessed with the Resident Assessment Instrument for Mental Health (RAI-MH) within the first 3 days of admission between 2009 and 2014 (n = 153,023). The RAI-MH is a validated assessment tool which includes a breadth of information on symptoms, self-harm, functioning, social support, comorbid medical diagnoses, and risk appraisal. Multivariable analyses were performed using SAS. RESULTS: One hundred forty-five thousand seven hundred (95.2%) of patients admitted had no history of ECT treatment and were not scheduled to receive ECT. A total of 7323 (or 4.8% of the patient population) had either a history of ECT use or were scheduled to receive ECT. Overall rate of ECT use was highest in patients with a provisional diagnosis of mood disorder (7.2%) compared with schizophrenia/other psychotic disorder (3.1%) or substance-related disorder (1.7%). Women were more likely to receive ECT compared with men (overall rates of ECT use 6.2% and 3.4%, respectively). Overall rate of ECT use increased significantly with increasing age. Number of prior hospitalizations was also a strong predictor of ECT use. Conversely, patients with elevated Risk of Harm to Others, schizophrenia, or a substance use disorder were all significantly less likely to receive ECT. All variables examined were statistically significant (P < 0.0001). Higher Severity of Self Harm Scores predicted past use, but not scheduled use of ECT. CONCLUSIONS: This is the largest study to date on predictors of ECT use. Utilization of RAI-MH is a novel and clinically useful method for evaluating predictors of ECT use. Predictors of ECT use within an inpatient population include: presence of a mood disorder, female sex, older age, low risk of harm to others, number of lifetime hospitalizations, lack of substance use disorder, and inability to care for self.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".