Cognitive development after schizophrenia onset can be shaped by interventions such as cognitive remediation, yet no study to date has investigated whether patterns of early behavioral development may predict later cognitive changes following intervention. We therefore investigated the extent to which premorbid adjustment trajectories predict cognitive remediation gains in schizophrenia.

In a total sample of 215 participants (170 first-episode schizophrenia participants and 45 controls), we classified premorbid functioning trajectories from childhood through late adolescence using the Cannon-Spoor Premorbid Adjustment Scale. For the 62 schizophrenia participants who underwent 6 months of computer-assisted, bottom-up cognitive remediation interventions, we identified MATRICS Consensus Cognitive Battery scores for which participants demonstrated mean changes after intervention, then evaluated whether developmental trajectories predicted these changes.

Growth mixture models supported three premorbid functioning trajectories: stable-good, deteriorating, and stable-poor adjustment. Schizophrenia participants demonstrated significant cognitive remediation gains in processing speed, verbal learning, and overall cognition. Notably, participants with stable-poor trajectories demonstrated significantly greater improvements in processing speed compared to participants with deteriorating trajectories.

This is the first study to our knowledge to characterize the associations between premorbid functioning trajectories and cognitive remediation gains after schizophrenia onset, indicating that 6 months of bottom-up cognitive remediation appears to be sufficient to yield a full standard deviation gain in processing speed for individuals with early, enduring functioning difficulties. Our findings highlight the connection between trajectories of premorbid and postmorbid functioning in schizophrenia and emphasize the utility of considering the lifespan developmental course in personalizing therapeutic interventions.

The current study aims to overcome past methodological limitations and capture adolescents in need of psychiatric care with psychopathological symptoms in a cohort with unrestricted access to mental health professionals.

The study source population consisted of a random sample of adolescents aged 16-17 years (N=1,369) assessed by the Israeli Draft Board. An adapted version of the Brief Symptom Inventory was used to identify clinically relevant psychopathological symptoms with scores categorized as severe if they were in the top 10th percentile of symptoms, otherwise not severe. An independent interview with a subsequent referral to a mental health professional was used to categorize adolescents in need of psychiatric care. To examine the association between severe psychopathological symptoms and the need for psychiatric care, logistic regression models were fitted unadjusted and adjusted for age, sex, and intellectual assessment scores. Adjusted classification measures were estimated to examine the utility of severe psychopathological symptoms for clinical prediction of need for psychiatric care.

Information on 1,283 adolescents was available in the final analytic sample. Logistic regression modeling showed a statistically significant (p<0.001) association between self-reported severe psychopathological symptoms and the need for psychiatric care (OR adjusted: 4.38; 95% CI: 3.55–5.40). Severe psychopathological symptoms had a classification accuracy of 83% (CI: 81%–85%).

Severe psychopathological symptoms, although accounting for a fair proportion of treatment seeking, would perhaps be better useful for classification purposes alongside other variables rather than in isolation.

Current approaches to assess violence risk in secure hospitals are resource intensive, limited by accuracy and authorship bias and may have reached a performance ceiling. This study seeks to develop scalable predictive models for violent offending following discharge from secure psychiatric hospitals.

We identified all patients discharged from secure hospitals in Sweden between January 1, 1992 and December 31, 2013. Using multiple Cox regression, pre-specified criminal, sociodemographic, and clinical risk factors were included in a model that was tested for discrimination and calibration in the prediction of violent crime at 12 and 24 months post-discharge. Risk cut-offs were pre-specified at 5% (low vs. medium) and 20% (medium vs. high).

We identified 2248 patients with 2933 discharges into community settings. We developed a 12-item model with good measures of calibration and discrimination (area under the curve = 0.77 at 12 and 24 months). At 24 months post-discharge, using the 5% cut-off, sensitivity was 96% and specificity was 21%. Positive and negative predictive values were 19% and 97%, respectively. Using the 20% cut-off, sensitivity was 55%, specificity 83% and the positive and negative predictive values were 37% and 91%, respectively. The model was used to develop a free online tool (FoVOx).

We have developed a prediction score in a Swedish cohort of patients discharged from secure hospitals that can assist in clinical decision-making. Scalable predictive models for violence risk are possible in specific patient groups and can free up clinical time for treatment and management. Further evaluation in other countries is needed.

Wellcome Trust (202836/Z/16/Z) and the Swedish Research Council. The funding sources had no involvement in writing of the manuscript or decision to submit or in data collection, analysis or interpretation or any aspect pertinent to the study.