ArticleSchizophrenia research2017
Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis.
Article in Schizophrenia research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- Predictors of transition in patients with clinical high risk for psychosis: an umbrella review.Translational psychiatry · 2023Pooled it
- Negative Symptoms and Functioning in Youth at Risk of Psychosis: A Systematic Review and Meta-analysis.Harvard review of psychiatryPooled it
- Dissecting multimodal predictors of psychosis risk and remission in youth at ultra‑high risk of developing psychosis: An exploratory study.PLOS mental health · 2026Article
- Leveraging computational linguistics and machine learning for detection of ultra-high risk of mental health disorders in youths.Schizophrenia (Heidelberg, Germany) · 2025Article
- The electroencephalography protocol for the Accelerating Medicines Partnership® Schizophrenia Program: Reliability and stability of measures.Schizophrenia (Heidelberg, Germany) · 2025Article
- Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach.JAMA psychiatry · 2023Article
- Psychotic disorders as a framework for precision psychiatry.Nature reviews. Neurology · 2023Review
- Can machine learning identify childhood characteristics that predict future development of bipolar disorder a decade later?Journal of psychiatric research · 2022Article
- Individualized Prediction of Prodromal Symptom Remission for Youth at Clinical High Risk for Psychosis.Schizophrenia bulletin · 2022Article
- A randomized Phase II trial evaluating efficacy, safety, and tolerability of oral BI 409306 in attenuated psychosis syndrome: Design and rationale.Early intervention in psychiatry · 2021Article
- Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations.Journal of medical Internet research · 2021Article
- Article
- Review
- Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence.JAMA psychiatry · 2021Article
- Prediction and Prevention in the Clinical High-Risk for Psychosis Paradigm: A Review of the Current Status and Recommendations for Future Directions of Inquiry.Frontiers in psychiatry · 2021Review
- Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.European child & adolescent psychiatry · 2020Article
- Structural and functional imaging markers for susceptibility to psychosis.Molecular psychiatry · 2020Review
- Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies-PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice.Schizophrenia bulletin · 2020Article
- Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion.Frontiers in psychiatry · 2020Review
- Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk.Translational psychiatry · 2019Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR. Participants included 416 people at UHR followed prospectively at the Personal Assessment and Crisis Evaluation (PACE) Clinic in Melbourne, Australia. The data were analysed using Support Vector Machine (SVM), a supervised machine learning technique that allows inferences at the individual level. SVM predicted transition to psychosis with a specificity of 60.6%, a sensitivity of 68.6% and an accuracy of 64.6% (p<0.001). In addition, SVM predicted functioning with a specificity of 62.5%, a sensitivity of 62.5% and an accuracy of 62.5% (p=0.008). Prediction of transition was driven by disorder of thought content, attenuated positive symptoms and functioning, whereas functioning was best predicted by attention disturbances, anhedonia-asociality and disorder of thought content. These results indicate that psychopathological information allows individualized prognostic predictions with statistically significant accuracy. However, this level of accuracy may not be sufficient for clinical translation in real-world clinical practice. Accuracy might be improved by combining psychopathological information with other types of data using a multivariate machine learning framework.
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Registered trials
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