Long‐acting antipsychotic medication as first‐line treatment of first‐episode psychosis with comorbid substance use disorder
Bibliographic record
Abstract
AIM: Substance use disorder (SUD) is highly prevalent among patients with first-episode psychosis (FEP) and associated with poor adherence and worst treatment outcomes. Although relapses are frequent in FEP, current literature on long-acting injectable antipsychotics (LAI-AP) use in FEP is scarce and studies often exclude patients with SUD. OBJECTIVES: To determine the impact of LAI-AP as first-line treatment on psychotic relapses or rehospitalizations in FEP patients with comorbid SUD (FEP-SUD). METHODS: This is a naturalistic, longitudinal, 3-year prospective and retrospective study on 237 FEP-SUD admitted in two EIS in Montreal, between 2005 and 2012. The patients were divided on the basis of first-line medication introduced, either oral antipsychotics (OAP, n = 206) or LAI-AP (n = 31). Baseline characteristics were compared using χ² test and analysis of variance, and Kaplan-Meier survival analysis was performed on relapse and rehospitalization. RESULTS: Compared to the OAP group, patients in the LAI-AP group presented worse prognostic factors (eg, history of homelessness). Despite this, the LAI-AP group presented a lower relapse rate (67.7% vs 76.7%), higher relapse-free survival time (694 vs 447 days, P = 0.008 in Kaplan-Meier analysis), and trends for reduced rehospitalization rates (48.4% vs 57.3%) and hospitalization-free survival time (813 vs 619 days, P = 0.065 Kaplan-Meier analysis). Of those receiving OAP as first-line, 41.3% were eventually switched to LAI-AP and displayed worst outcome in relapse and rehospitalization. CONCLUSION: LAI-AP should be strongly considered as first-line treatment of FEP-SUD patients since this pharmacological option reduces the risk of relapse and rehospitalization even in the individuals with poor prognostic factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".