Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia

The contribution of first-episode illness characteristics and cumulative antipsychotic usage to progressive structural brain changes over a long-term follow-up in schizophrenia

Exposure to antipsychotics as well as certain first-episode illness characteristics have been associated with greater gray matter (GM) deficits in the early phase of schizophrenia. Whether the first-episode illness characteristics affect the long-term progression of the structural brain changes remain unexplored. We therefore assessed the role of first-episode illness characteristics and life-time antipsychotic use in relation to long-term structural brain GM changes in schizophrenia. Individuals with schizophrenia (SZ, n = 29) and non-psychotic controls (n = 61) from the Northern Finland Birth Cohort 1966 underwent structural MRI at the ages of 34 (baseline) and 43 (follow-up) years. At follow-up, the average duration of illness was 19.8 years. Voxel-based morphometry was used to assess the effects of predictors on longitudinal GM changes in schizophrenia-relevant brain areas. Younger age of onset (AoO), higher cumulative antipsychotic dose and severity of symptoms were associated with greater GM deficits in the SZ group at follow-up. None of the first-episode illness characteristics were associated with longitudinal GM changes during 9-year follow-up period. We conclude that a younger AoO and high life-time antipsychotic use may contribute to progression of structural brain changes in schizophrenia. Apart from AoO, other first-episode illness characteristics may not contribute to longitudinal GM changes in midlife.

Development and Validation of a Machine Learning-Based Model of Mortality Risk in First-Episode Psychosis

Importance

There is an absence of mortality risk assessment tools in first-episode psychosis (FEP) that could enable personalized interventions.

Objective

To examine the feasibility of machine learning (ML) in discerning mortality risk in FEP and to assess whether such risk predictions can inform pharmacotherapy choices.

Design, Setting, and Participants

In this prognostic study, Swedish nationwide cohort data (from July 1, 2006, to December 31, 2021) were harnessed for model development and validation. Finnish cohort data (from January 1, 1998, to December 31, 2017) were used for external validation. Data analyses were completed between December 2022 and December 2023.

Main Outcomes and Measures

Fifty-one nationwide register variables, encompassing demographics and clinical and work-related histories, were subjected to ML to predict future mortality risk. The ML model's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The comparative effectiveness of pharmacotherapies in patients was assessed and was stratified by the ML model to those with predicted high mortality risk (vs low risk), using the between-individual hazard ratio (HR). The 5 most important variables were then identified and a model was retrained using these variables in the discovery sample.

Results

This study included 24 052 Swedish participants (20 000 in the discovery sample and 4052 in the validation sample) and 1490 Finnish participants (in the validation sample). Swedish participants had a mean (SD) age of 29.1 (8.1) years, 62.1% were men, and 418 died with 2 years. Finnish participants had a mean (SD) age of 29.7 (8.0) years, 61.7% were men, and 31 died within 2 years. The discovery sample achieved an AUROC of 0.71 (95% CI, 0.68-0.74) for 2-year mortality prediction. Using the 5 most important variables (ie, the top 10% [substance use comorbidities, first hospitalization duration due to FEP, male sex, prior somatic hospitalizations, and age]), the final model resulted in an AUROC of 0.70 (95% CI, 0.63-0.76) in the Swedish sample and 0.67 (95% CI, 0.56-0.78) in the Finnish sample. Individuals with predicted high mortality risk had an elevated 15-year risk in the Swedish sample (HR, 3.77 [95% CI, 2.92-4.88]) and an elevated 20-year risk in the Finnish sample (HR, 3.72 [95% CI, 2.67-5.18]). For those with predicted high mortality risk, long-acting injectable antipsychotics (HR, 0.45 [95% CI, 0.23-0.88]) and mood stabilizers (HR, 0.64 [95% CI, 0.46-0.90]) were associated with decreased mortality risk. Conversely, for those predicted to survive, only oral aripiprazole (HR, 0.38 [95% CI, 0.20-0.69]) and risperidone (HR, 0.38 [95% CI, 0.18-0.82]) were associated with decreased mortality risk.

Conclusions and Relevance

In this prognostic study, an ML-based model was developed and validated to predict mortality risk in FEP. These findings may help to develop personalized interventions to mitigate mortality risk in FEP.