T116. PREDICTION OF REMISSION IN NON-CONVERTING INDIVIDUALS AT CLINICAL HIGH RISK FOR PSYCHOSIS
Notice bibliographique
Résumé
Abstract Background The clinical high-risk period before a first episode of psychosis (CHR-P) has been widely studied in the past 30 years with the goal of understanding the development of psychosis. Despite the progress in understanding what factors are associated with conversion to psychosis from the CHR-P state, less attention has been paid to the individuals who do not transition to psychosis. It is estimated that approximately 75–80% of individuals do not go on to convert to psychosis from the CHR-P state and this group should not simply be characterized as the inverse of conversion. To date, only a handful of studies have examined the characteristics and predictors of those who do not convert to psychosis and ultimately either remit or continue to meet symptom-based CHR-P criteria. The present study took an exploratory empirical approach to determining potential factors that predict remission in non-converters. Methods Participants were drawn from the North American Prodrome Longitudinal Study (NAPLS2). Univariate Kaplan Meier survival analyses were performed on a pool of available demographic and clinical variables. Variables that were significant (p < 0.05) in the univariate analyses were then included in a multivariate Cox proportional hazard regression to predict remission. Remission was defined as all SOPS positive symptom subscale items rated as a 2 or lower at any given follow-up visit. Results A total of 359 participants from the NAPLS2 study who did not convert to psychosis and had data for at least the baseline and first follow-up visit and were included in this study. Of these participants, 174 met criteria for symptomatic remission. A total of 57 clinical variables were tested in univariate analyses and 14 of these variables met criteria for inclusion in the multivariate model. The variables included in the multivariate model were demographic variables (ethnicity, stressful life events), items from the Scale of Prodromal Symptoms (SOPS) (avolition, dysphoric mood), subtest scores from the MATRICS Cognitive Battery (speed of processing, verbal learning, verbal and non-verbal working memory, reasoning and problem solving, visual learning), one item from the Calgary Depression Scale for Schizophrenia (CDSS) (pathological guilt) and measures of functioning (GAF decline in past year, lowest GAF score in the past year). Overall, the multivariate model achieved a C-index of 0.64 (SE = 0.02) and p-value of 0.001 in predicting remission. In the multivariate model, significant covariates included stressful life events (HR = .95, p = .006), Hispanic ethnicity (HR = 1.45, p = .045), and avolition (HR = .89, p = .04). Covariates approaching significance included visual learning (HR = 1.02, p = .07), and GAF decline in the past year (HR = 1.01, p = .09). Discussion This study is the first to use a data-driven approach to systematically assess clinical and demographic predictors of symptomatic remission in individuals who do not convert to psychosis. The identified set of significant clinical variables is novel, suggesting that remission represents a unique clinical phenomenon and suggesting that further study is warranted to best understand factors contributing to resilience and recovery from the CHR-P period.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».