ArticlePsychological medicine2019
A predictive model for conversion to psychosis in clinical high-risk patients.
Article in Psychological medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 4 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Clinical features and comorbidity in very early-onset schizophrenia: a systematic review.Frontiers in psychiatry · 2023Pooled it
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- A meta-analysis of ultra-high field glutamate, glutamine, GABA and glutathione 1HMRS in psychosis: Implications for studies of psychosis risk.Schizophrenia research · 2020Pooled it
- What Causes the Onset of Psychosis in Individuals at Clinical High Risk? A Meta-analysis of Risk and Protective Factors.Schizophrenia bulletin · 2020Pooled it
- Functional brain connectivity in clinical high-risk youth from the Kenya psychosis-risk outcomes study (KePROS): functional network comparative area and topography analysis (FUNCATA).NeuroImage. Clinical · 2026Article
- Using Large Language Models to Measure Symptom Severity Scores in Patients At-Risk for Schizophrenia.Schizophrenia bulletin · 2026Article
- Predicting ordinal clinical outcomes in at-risk mental states: a multimodal approach.Frontiers in psychiatry · 2026Article
- Victimization and engagement with the legal system among individuals at clinical high risk (CHR) for psychosis.Schizophrenia research · 2025Article
- Do psychotic symptoms predict future psychotic disorders in adolescent psychiatry inpatients? A 17-year cohort study.Psychological medicine · 2025Article
- Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program.Schizophrenia (Heidelberg, Germany) · 2025Article
- PEPAMARKER: a multicenter cohort study protocol on predictive biomarkers of affectiveFrontiers in psychiatry · 2025Article
- Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.The Permanente journal · 2024Article
- Different learning aberrations relate to delusion-like beliefs with different contents.Brain : a journal of neurology · 2024Article
- Development of the PSYCHS: Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS.Early intervention in psychiatry · 2024Article
- Heterogeneity of clinical symptomatology in pediatric patients at clinical high risk for psychosis.BMC research notes · 2024Article
- Unique Challenges in Biomarkers for Psychotic Disorders.Brain sciences · 2024Article
- Kenya Psychosis-Risk Outcomes Study (KePROS): Development of an Accelerated Medicine Partnership Schizophrenia-Aligned Project in Africa.Schizophrenia bulletin open · 2024Article
- Development of the PSYCHS: Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS.medRxiv : the preprint server for health sciences · 2023Article
- A Review of Potential Neuroimaging Biomarkers of Schizophrenia-Risk.Journal of psychiatry and brain science · 2023Article
- Serum Levels of Tumor Necrosis Factor-α and Vascular Endothelial Growth Factor in the Subtypes of Clinical High Risk Individuals: A Prospective Cohort Study.Neuropsychiatric disease and treatment · 2023Article
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundThe authors developed a practical and clinically useful model to predict the risk of psychosis that utilizes clinical characteristics empirically demonstrated to be strong predictors of conversion to psychosis in clinical high-risk (CHR) individuals. The model is based upon the Structured Interview for Psychosis Risk Syndromes (SIPS) and accompanying clinical interview, and yields scores indicating one's risk of conversion.
methodsBaseline data, including demographic and clinical characteristics measured by the SIPS, were obtained on 199 CHR individuals seeking evaluation in the early detection and intervention for mental disorders program at the New York State Psychiatric Institute at Columbia University Medical Center. Each patient was followed for up to 2 years or until they developed a syndromal DSM-4 disorder. A LASSO logistic fitting procedure was used to construct a model for conversion specifically to a psychotic disorder.
resultsAt 2 years, 64 patients (32.2%) converted to a psychotic disorder. The top five variables with relatively large standardized effect sizes included SIPS subscales of visual perceptual abnormalities, dysphoric mood, unusual thought content, disorganized communication, and violent ideation. The concordance index (c-index) was 0.73, indicating a moderately strong ability to discriminate between converters and non-converters.
conclusionsThe prediction model performed well in classifying converters and non-converters and revealed SIPS measures that are relatively strong predictors of conversion, comparable with the risk calculator published by NAPLS (c-index = 0.71), but requiring only a structured clinical interview. Future work will seek to externally validate the model and enhance its performance with the incorporation of relevant biomarkers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.