O3.5. EARLY TRAJECTORIES OF POSITIVE SYMPTOMS REMISSION IN FIRST EPISODE-PSYCHOSIS: A 2-YEAR FOLLOW-UP STUDY
Notice bibliographique
Résumé
Abstract Background The Prevention and Early intervention Program for Psychosis (PEPP) provides young people with first episode psychosis (FEP) rapid access to appropriate mental health services designed on the principles of early intervention (EI). We have previously demonstrated high rates of positive symptom (PS) remission. However, the relationship between PS, negative symptoms (NS) and functional outcomes remains unclear. Adherence to medication and early treatment response have been shown to be important independent determinants of the level of, and time to, symptom and functional remission, respectively. While trajectories of symptom severity have been shown to be heterogeneous, no previous study has investigated the prognosis of PS remission among individuals with FEP treated in an EI service. Identification of different trajectories of PS remission is a useful strategy to provide insight into clinically meaningful subgroups of patients while providing valuable information on NS and functioning for improving treatment outcomes. Methods The 2-year treatment at PEPP comprises different psychosocial (i.e., cognitive behavioral therapy, group intervention, family intervention, individual placement and support program) and psychopharmacological interventions (i.e., minimum effective dosage of second-generation antipsychotics). Monthly assessments were conducted from baseline to month 24. A total of 387 FEP patients, aged 14–35 years, with DSM IV affective or non-affective psychosis and little or no prior antipsychotic treatment (i.e., < 30 days) were included. PS remission was defined as absence of overt psychotic symptoms (i.e., all global SAPS items ≤ 2). A Latent Class Growth Analysis (LCGA) was used to investigate the distinct trajectories based on cumulative length of PS remission assessed at 3, 6, 9, 12, 15, 18, 21, and 24 months of treatment. Predictors of trajectories were investigated among sociodemographic, pre-treatment, as well as baseline and course clinical characteristics. Chi-square tests, one-way and mixed ANOVAs identified which baseline and longitudinal variables differed between and within trajectories. Candidate predictors that were statistically significant were then entered into a multinomial regression model to determine which factors independently predict trajectory membership. Results Three distinct trajectories of PS remission were identified. Excellent (68%), unstable (15%) and poor (17%) trajectory. Trajectories differed at baseline in DUP, diagnosis of affective psychosis and PS severity. Over the 24 months of treatment, negative, depressive, anxiety and mania symptoms, as well as functioning, best improved in the excellent trajectory among which patients were prescribed less antipsychotics in term of chlorpromazine equivalent than patients in other trajectories. Multinomial regression of baseline characteristics revealed that absence of early treatment response at 3 months (adjusted OR=2.53; 95%CI=1.24–5.16) independently predicted poorer trajectory. Discussion These results highlight the heterogeneous prognosis of PS remission suggesting that the diversity in FEP response and phenotypes may be determined by different pathophysiological underpinnings. The fact that early response was found to be a strong predictor of PS remission supports early and adequate symptom control for which medication is a critical issue. Further research applying data-driven trajectory analysis in FEP is warranted to facilitate better characterization of longer-term patterns of remission and development of targeted intervention to promote early recovery.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».