Background

As efforts intensify to intervene early among those at risk for psychosis, examination of the relationship between presenting psychopathology and long-term functional outcome may guide treatment decision-making and offer a means to prevent or reduce chronic disability.

Methods

Data were collected through the Early Detection and Intervention for the Prevention of Psychosis Program (EDIPPP), a multisite national trial testing the efficacy of an early intervention for youth at risk of developing psychosis. Participants were followed prospectively and completed comprehensive evaluations at 6, 12, and 24 months, including the Structured Interview for Prodromal Syndromes (SIPS) and the Global Social and Role Functioning Scales. The present analyses included 327 participants and examined the relationships between baseline symptoms and longitudinal global social and role functioning using a linear mixed modeling approach.

Results

Higher baseline negative symptoms and deteriorated thought process predicted worse social and role functioning in the follow-up period. The effect of negative symptoms on social functioning, however, was moderated by positive symptoms, and the relationship between positive symptoms and social functioning changed over time. Baseline positive symptoms, distress, and level of symptom severity were not predictors of either social or role functioning.

Conclusions

Baseline negative symptoms and thought disorder appear to predict functional outcome for up to two years among adolescents and young adults at risk for psychosis. Developing effective interventions to target these symptoms may be critical to promote functional recovery among those experiencing attenuated symptoms or a first episode of psychosis.

Objective

As part of the second phase of the North American Prodrome Longitudinal Study (NAPLS-2), Cannon and colleagues report, concurrently with the present article, on a risk calculator for the individualized prediction of a psychotic disorder in a 2-year period. The present study represents an external validation of the NAPLS-2 psychosis risk calculator using an independent sample of patients at clinical high risk for psychosis collected as part of the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP).

Method

Of the total EDIPPP sample of 210 subjects rated as being at clinical high risk based on the Structured Interview for Prodromal Syndromes, 176 had at least one follow-up assessment and were included in the construction of a new prediction model with six predictor variables in the NAPLS-2 psychosis risk calculator (unusual thoughts and suspiciousness, symbol coding test performance, verbal learning test performance, decline in social functioning, baseline age, and family history). Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC). The NAPLS-2 risk calculator was then used to generate a psychosis risk estimate for each case in the external validation sample.

Results

The external validation model showed good discrimination, with an AUC of 0.790 (95% CI=0.644-0.937). In addition, the personalized risk generated by the risk calculator provided a solid estimation of the actual conversion outcome in the validation sample.

Conclusions

Two independent samples of clinical high-risk patients converge to validate the NAPLS-2 psychosis risk calculator. This prediction calculator represents a meaningful step toward early intervention and the personalized treatment of psychotic disorders.

Aim

The Scale of Prodromal Symptoms (SOPS) was developed to identify individuals experiencing early signs of psychosis, a critical first step towards early intervention. Preliminary dimension reduction analyses suggested that psychosis-risk symptoms may deviate from the traditional symptom structure of schizophrenia, but findings have been inconsistent. This study investigated the phenomenology of psychosis risk symptoms in a large sample from a multi-site, national study using rigorous factor analysis procedure.

Methods

Participants were 334 help-seeking youth (age: 17.0 ± 3.3) from the Early Detection and Intervention for the Prevention of Psychosis Program, consisting of 203 participants at clinically higher risk (sum of P scores ≥ 7), 87 with clinically lower risk (sum of P scores < 7) and 44 in very early first-episode psychosis (<30 days of positive symptoms). Baseline SOPS data were subjected to principal axis factoring (PAF), estimating factors based on shared variance, with Oblimin rotation.

Results

PAF yielded four latent factors explaining 36.1% of total variance: positive symptoms; distress; negative symptoms; and deteriorated thought process. They showed reasonable internal consistency and good convergence validity, and were not orthogonal.

Conclusions

The empirical factors of the SOPS showed similarities and notable differences compared with the existing SOPS structure. Regrouping the symptoms based on the empirical symptom dimensions may improve the diagnostic validity of the SOPS. Relative prominence of the factors and symptom frequency support early identification strategies focusing on positive symptoms and distress. Future investigation of long-term functional implications of these symptom factors may further inform intervention strategies.

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.