Development and temporal validation of a clinical prediction model of transition to psychosis in individuals at ultra‐high risk in the <scp>UHR</scp> 1000+ cohort

The concept of ultra-high risk for psychosis (UHR) has been at the forefront of psychiatric research for several decades, with the ultimate goal of preventing the onset of psychotic disorder in high-risk individuals. Orygen (Melbourne, Australia) has led a range of observational and intervention studies in this clinical population. These datasets have now been integrated into the UHR 1000+ cohort, consisting of a sample of 1,245 UHR individuals with a follow-up period ranging from 1 to 16.7 years. This paper describes the cohort, presents a clinical prediction model of transition to psychosis in this cohort, and examines how predictive performance is affected by changes in UHR samples over time. We analyzed transition to psychosis using a Cox proportional hazards model. Clinical predictors for transition to psychosis were investigated in the entire cohort using multiple imputation and Rubin's rule. To assess performance drift over time, data from 1995-2016 were used for initial model fitting, and models were subsequently validated on data from 2017-2020. Over the follow-up period, 220 cases (17.7%) developed a psychotic disorder. Pooled hazard ratio (HR) estimates showed that the Comprehensive Assessment of At-Risk Mental States (CAARMS) Disorganized Speech subscale severity score (HR=1.12, 95% CI: 1.02-1.24, p=0.024), the CAARMS Unusual Thought Content subscale severity score (HR=1.13, 95% CI: 1.03-1.24, p=0.009), the Scale for the Assessment of Negative Symptoms (SANS) total score (HR=1.02, 95% CI: 1.00-1.03, p=0.022), the Social and Occupational Functioning Assessment Scale (SOFAS) score (HR=0.98, 95% CI: 0.97-1.00, p=0.036), and time between onset of symptoms and entry to UHR service (log transformed) (HR=1.10, 95% CI: 1.02-1.19, p=0.013) were predictive of transition to psychosis. UHR individuals who met the brief limited intermittent psychotic symptoms (BLIPS) criteria had a higher probability of transitioning to psychosis than those who met the attenuated psychotic symptoms (APS) criteria (HR=0.48, 95% CI: 0.32-0.73, p=0.001) and those who met the Trait risk criteria (a first-degree relative with a psychotic disorder or a schizotypal personality disorder plus a significant decrease in functioning during the previous year) (HR=0.43, 95% CI: 0.22-0.83, p=0.013). Models based on data from 1995-2016 displayed good calibration at initial model fitting, but showed a drift of 20.2-35.4% in calibration when validated on data from 2017-2020. Large-scale longitudinal data such as those from the UHR 1000+ cohort are required to develop accurate psychosis prediction models. It is critical to assess existing and future risk calculators for temporal drift, that may reduce their utility in clinical practice over time.

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Journal
World Psychiatry
Published
2024-09-16
DOI
https://doi.org/10.1002/wps.21240
Citations
16
Primary Topic
Schizophrenia research and treatment
Type
article
Field-Weighted Citation Impact
6.17

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article

Development and temporal validation of a clinical prediction model of transition to psychosis in individuals at ultra‐high risk in the UHR 1000+ cohort

Ashleigh Lin, Gregor Berger, Lisa Phillips, Swapna Verma et al.
16 citations
World Psychiatry
Schizophrenia research and treatment
6.17
article

Development and temporal validation of a clinical prediction model of transition to psychosis in individuals at ultra‐high risk in the UHR 1000+ cohort

Ashleigh Lin, Gregor Berger, Lisa Phillips, Swapna Verma, Jessica Hartmann, Stephen James Wood, Dominic Dwyer, Andrew David Thompson, Johanna T. W. Wigman, Scott Richard Clark, Merete Nordentoft, Isabelle Scott, Blake Cavve, Barnaby Nelson, Lieuwe de Haan, Anita Riecher‐Rössler, Enda M. Byrne, Caroline X. Gao, Dorien H. Nieman, Patrick D. McGorry, Ian Bernard Hickie, Hok Pan Yuen, G. Paul Amminger, Simon Hartmann, Stefan Smesny, Monika Schlögelhofer, Alison Ruth Yung, Nilufar Mossaheb, Cassandra M. J. Wannan, Christel Maria Middeldorp, Eric Yu Hai Chen, Miriam R. Schäfer
article en
16 citations

Abstract

The concept of ultra-high risk for psychosis (UHR) has been at the forefront of psychiatric research for several decades, with the ultimate goal of preventing the onset of psychotic disorder in high-risk individuals. Orygen (Melbourne, Australia) has led a range of observational and intervention studies in this clinical population. These datasets have now been integrated into the UHR 1000+ cohort, consisting of a sample of 1,245 UHR individuals with a follow-up period ranging from 1 to 16.7 years. This paper describes the cohort, presents a clinical prediction model of transition to psychosis in this cohort, and examines how predictive performance is affected by changes in UHR samples over time. We analyzed transition to psychosis using a Cox proportional hazards model. Clinical predictors for transition to psychosis were investigated in the entire cohort using multiple imputation and Rubin's rule. To assess performance drift over time, data from 1995-2016 were used for initial model fitting, and models were subsequently validated on data from 2017-2020. Over the follow-up period, 220 cases (17.7%) developed a psychotic disorder. Pooled hazard ratio (HR) estimates showed that the Comprehensive Assessment of At-Risk Mental States (CAARMS) Disorganized Speech subscale severity score (HR=1.12, 95% CI: 1.02-1.24, p=0.024), the CAARMS Unusual Thought Content subscale severity score (HR=1.13, 95% CI: 1.03-1.24, p=0.009), the Scale for the Assessment of Negative Symptoms (SANS) total score (HR=1.02, 95% CI: 1.00-1.03, p=0.022), the Social and Occupational Functioning Assessment Scale (SOFAS) score (HR=0.98, 95% CI: 0.97-1.00, p=0.036), and time between onset of symptoms and entry to UHR service (log transformed) (HR=1.10, 95% CI: 1.02-1.19, p=0.013) were predictive of transition to psychosis. UHR individuals who met the brief limited intermittent psychotic symptoms (BLIPS) criteria had a higher probability of transitioning to psychosis than those who met the attenuated psychotic symptoms (APS) criteria (HR=0.48, 95% CI: 0.32-0.73, p=0.001) and those who met the Trait risk criteria (a first-degree relative with a psychotic disorder or a schizotypal personality disorder plus a significant decrease in functioning during the previous year) (HR=0.43, 95% CI: 0.22-0.83, p=0.013). Models based on data from 1995-2016 displayed good calibration at initial model fitting, but showed a drift of 20.2-35.4% in calibration when validated on data from 2017-2020. Large-scale longitudinal data such as those from the UHR 1000+ cohort are required to develop accurate psychosis prediction models. It is critical to assess existing and future risk calculators for temporal drift, that may reduce their utility in clinical practice over time.

World PsychiatryVol. 23(3)
University of Copenhagen (DK), The University of Sydney (AU), University Medical Center Groningen (NL), Deakin University (AU), The University of Queensland (AU), The University of Melbourne (AU), University of Groningen (NL), Nanyang Technological University (SG), The Kids Research Institute Australia (AU), The University of Western Australia (AU), University of Basel (CH), University of Zurich (CH), Heidelberg University (DE), University Hospital Heidelberg (DE), Amsterdam UMC Location University of Amsterdam (NL), Children’s Health Queensland Hospital and Health Service (AU), Central Institute of Mental Health (DE), Orygen (AU), Duke-NUS Medical School (SG), Jena University Hospital (DE), Institute of Mental Health (SG), Vienna Biocenter (AT), Arkin (NL), Reinier van Arkel (NL), Amsterdam University Medical Centers (NL), The University of Adelaide (AU), Australian Psychological Society (AU), Medical University of Vienna (AT), University of Birmingham (GB), University of Hong Kong (HK)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Medical Research Council, National Health and Medical Research Council
Openalex Percentile: Top 3%
Schizophrenia research and treatment
6.17
DOIOpen Access PDFOpenAlex
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