T246. RISK FACTORS FOR PSYCHOTIC RELAPSE IN CHRONIC SCHIZOPHRENIA AFTER DOSE-REDUCTION OR DISCONTINUATION OF ANTIPSYCHOTICS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Patients are often treated with high doses or combinations of antipsychotics. High doses are associated with more severe side effects and reduction of motivation and drive, which may hamper recovery. Nevertheless, dose-reduction (DR) or discontinuation of antipsychotic medication in chronic patients, carries the risk of psychotic relapse. In order to identify risk factors of psychotic relapse after DR or discontinuation, we performed a meta-analysis, aimed (i) to determine the rate of relapse after DR or discontinuation in patients with chronic schizophrenia, and (ii) to assess risk factors for psychotic relapse. Methods We searched PubMed, EMBASE and PsycINFO for studies on dose-reduction of antipsychotics from January 1950 through June 2019. We extracted data and calculated event rates (ER=relapse rate) per person-years including 95% confidence intervals (95%CI). The following data were extracted: (1) patient characteristics (age, percentage of male subjects, setting, duration of illness), (2) dose-reduction/discontinuation characteristics (start-dose before dose-reduction, end-dose after dose-reduction, dose-reduction in milligrams, dose-reduction as percentage of start-dose, time period of dose-reduction), (3) follow-up characteristics (time after dose-reduction), and (4) study characteristics (blinding, year of publication and relapse definition). To account for sample variation we pooled the results following the Dersimonian and Laird random effects method (CMA; Borenstein et al 2009). Between-study heterogeneity was assessed with Cochran’s I2-statistic. We examined the risk of bias in the included studies based on five aspects that could affect the association between exposure and outcome from the Newcastle-Ottawa scale (NOS). Results 46 unique cohorts, presenting 1677 patients in which doses were reduced/discontinued were included in meta-analysis. Most included patients were man at middle age, and with a mean duration of illness of 15 years. There was a considerable risk of bias in studies (48% of studies with NOS≤3). We found an overall event rate (ER) per person-years on psychotic relapse of 0.55 (CI95% 0.46–0.65; p<0.0001; I2 =79). We present various variables that influence event rates. Highest rates were found for inpatients with a short duration of illness. Most robust event rates for psychotic relapse were seen for discontinuing antipsychotics, and if not discontinuing, dose-reduction till under 5mg haloperidol equivalents daily (HE). Abrupt reduction yielded higher rates than gradual reduction. During short follow-up time more relapses occurred than in studies with long follow-up time. Older studies and studies in which relapse was defined as a clinical decision (without applying a psychometric scale) also yielded high event rates, explained by the fact that older studies mostly reduced antipsychotics abruptly till zero or at least doses under 5mgHE, while more recent studies did not, and used rating scales for relapse. Discussion In patients with chronic schizophrenia discontinuing, and to a lesser extent DR till end-dose <5mgHE, patients who reduce doses abrupt, inpatients, and patients with a short duration of illness carry highest relapse risk. Most relapses occur during the first half year after DR.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.047 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".