TREATMENT DISENGAGEMENT AND APD TREATMENT IN EARLY PSYCHOSIS: RESULTS FROM EPINET – THE LARGEST EP NETWORK IN THE USA
Bibliographic record
Abstract
Abstract Background Treatment disengagement (TD) is a far too frequent event among patients with early psychosis (EP). TD contributes to poor outcomes as it has been associated with relapses and hospitalizations (1). Rates of TD are higher in EP than other forms a of psychosis and are as high as 80% during the first year of care (2). One of the most important factors associated with TD is antipsychotic drug (APD)-related side effects and dissatisfaction (3). Aims & Objectives The purpose of this paper is to examine TD and APD usage in the largest EP intervention network termed Early Psychosis Intervention Network (EPINET) which is comprised of 8 hub and spoke (clinics) networks, over 100 EP clinics and over 4,000 EP patients all in the USA. Discharge rates, reasons for discharge, and APD patterns of use, satisfaction and adverse event rates will be examined. Method Admission criteria to EPINET includes an age range of 14 to 35 and affective or nonaffective psychotic disorders with onsets within 5 years of first psychotic symptoms. All clinics employ the coordinated specialty care (CSC) treatment model and utilize a common assessment battery (CAB). The CAB is administered at enrollment (baseline) and at each sequential 6-month time point. It contains a battery of validated assessment measures and include data collection of discharges, reasons for discharge, types of APDs, and APD side effects. Results An EPINET-wide analysis revealed a 6-month (N=2391) discharge rate of 20.0%, and a 12-month (N=1852) discharge rate of 41.5%. The most commonly reported reasons for discharge (N=1,818) were: terminated services 24.7%; completed program/no longer needed services 22.7% and whereabouts unknown 15.8%. Remarkably similar discharge rates were observed in Academic-Community (AC) EPINET (one of the 8 EPINET hub networks) of 6-month (N=515) discharge rate of 19.8% and 12-month (N=457) discharge rate of 38.9%. The most commonly reported reasons for discharges (N=183) were: whereabouts unknown 26.8%; terminated services 23.5% and moved out of service area 13.7%. In AC- EPINET, 74.3% were prescribed antipsychotic medications. The most commonly prescribed oral APDs (N=283) were aripiprazole 29.3%, olanzapine 19.1% and risperidone 18.2%. There were no prescriptions for the three most recently FDA approved APDs – lumateperone, caripirazine and brexpiperozole. Clozapine use was 9.5%. Long-acting depot APDs use was 15.3%. On a APD satisfaction scale rated 0 (not at all) to10 (entirely), only 50.6% rated high satisfaction (scores 8 to 10). The most common medication side effects were changes in weight/appetite 28.1%, daytime sedation 17.4%, concentration problems 11.6%, restlessness 10.7%, and muscle tenseness 8.7%. Discussion & Conclusion The results indicate that discharges were high among EP patients with 40% rates within 12 months of CSC treatment. Only 50% of patient were highly satisfied with their APD medication treatment with weight change, and sedation being the most common side effects. These results suggest that improved treatment engagement strategies and better APD medications are need for EP care. References 1. Dixon LB et al Treatment engagement of individuals experiencing mental illness: review and update. World Psychiatry 2016; 15:13-20. 2. Kane JM et al Factors contributing to engagement during the initial stages of treatment for psychosis Qual Health Res 2012; 23;336-47. 3. Lal S et al Service engagement in first-episode psychosis: Current issues and future directions. Canadian Journal of Psychiatry 2015; 60:341
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".