ArticleSchizophrenia bulletin2020
Development and Validation of a Dynamic Risk Prediction Model to Forecast Psychosis Onset in Patients at Clinical High Risk.
Article in Schizophrenia bulletin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Clinical outcomes in brief psychotic episodes: a systematic review and meta-analysis.Epidemiology and psychiatric sciences · 2021Pooled it
- Using Large Language Models to Measure Symptom Severity Scores in Patients At-Risk for Schizophrenia.Schizophrenia bulletin · 2026Article
- Decoding Psychosis Risk: Neuroanatomical Correlates of the NAPLS-2 Calculator in the PRONIA Cohort.Schizophrenia bulletin · 2026Article
- Validation and Extension of a Risk Calculator to Predict Mood Recurrence in Young People with Bipolar Disorder.medRxiv : the preprint server for health sciences · 2026Article
- Characterizing the Clinical Trajectory and Predicting Persistence and Deterioration of Attenuated Psychotic Symptoms in Ultra-High-Risk Individuals.Schizophrenia bulletin · 2025Article
- Leveraging multigenerational health data to enhance mental disorder risk prediction: a population-based cohort study.BMC psychiatry · 2025Article
- A Phenomenological Reappraisal of Dynamical Systems in Psychopathology.Psychopathology · 2025Review
- Development and temporal validation of a clinical prediction model of transition to psychosis in individuals at ultra-high risk in the UHR 1000+ cohort.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Are factors that predict conversion to psychosis associated with initial transition to a high risk state? An adolescent brain cognitive development study analysis.Schizophrenia research · 2024Article
- Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach.JAMA psychiatry · 2023Article
- Distress Related to Attenuated Psychotic Symptoms: Static and Dynamic Association With Transition to Psychosis, Nonremission, and Transdiagnostic Symptomatology in Clinical High-Risk Patients in an International Intervention Trial.Schizophrenia bulletin open · 2022Article
- Translating RDoC to Real-World Impact in Developmental Psychopathology: A Neurodevelopmental Framework for Application of Mental Health Risk Calculators.Development and psychopathology · 2021Article
- Developing and Validating an Individualized Clinical Prediction Model to Forecast Psychotic Recurrence in Acute and Transient Psychotic Disorders: Electronic Health Record Cohort Study.Schizophrenia bulletin · 2021Article
- Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.Schizophrenia bulletin · 2021Article
- Article
- 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
- Real-world digital implementation of the Psychosis Polyrisk Score (PPS): A pilot feasibility study.Schizophrenia research · 2020Article
- Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack.Journal of visualized experiments : JoVE · 2020Article
- Early Intervention Services for First Episode of Psychosis in South London and the Maudsley (SLaM): 20 Years of Care and Research for Young People.Frontiers in psychiatry · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of outcomes in patients at Clinical High Risk for Psychosis (CHR-P) almost exclusively relies on static data obtained at a single snapshot in time (ie, baseline data). Although the CHR-P symptoms are intrinsically evolving over time, available prediction models cannot be dynamically updated to reflect these changes. Hence, the aim of this study was to develop and internally validate a dynamic risk prediction model (joint model) and to implement this model in a user-friendly online risk calculator. Furthermore, we aimed to explore the prognostic performance of extended dynamic risk prediction models and to compare static with dynamic prediction. One hundred ninety-six CHR-P patients were recruited as part of the "Basel Früherkennung von Psychosen" (FePsy) study. Psychopathology and transition to psychosis was assessed at regular intervals for up to 5 years using the Brief Psychiatric Rating Scale-Expanded (BPRS-E). Various specifications of joint models were compared with regard to their cross-validated prognostic performance. We developed and internally validated a joint model that predicts psychosis onset from BPRS-E disorganization and years of education at baseline and BPRS-E positive symptoms during the follow-up with good prognostic performance. The model was implemented as online risk calculator (http://www.fepsy.ch/DPRP/). The use of extended joint models slightly increased the prognostic accuracy compared to basic joint models, and dynamic models showed a higher prognostic accuracy than static models. Our results confirm that extended joint modeling could improve the prediction of psychosis in CHR-P patients. We implemented the first online risk calculator that can dynamically update psychosis risk prediction.
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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.