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Dynamic prediction of transition to psychosis using joint modelling.
Journal article

Dynamic prediction of transition to psychosis using joint modelling.

  • Yuen HP Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Centre for Youth Mental Health, The University of Melbourne, Australia. Electronic address: hokpan.yuen@orygen.org.au.
  • Mackinnon A Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne, Australia; Black Dog Institute, New South Wales, Australia; University of New South Wales, New South Wales, Australia.
  • Hartmann J Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Centre for Youth Mental Health, The University of Melbourne, Australia.
  • Amminger GP Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Centre for Youth Mental Health, The University of Melbourne, Australia.
  • Markulev C Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Centre for Youth Mental Health, The University of Melbourne, Australia.
  • Lavoie S Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Centre for Youth Mental Health, The University of Melbourne, Australia.
  • Schäfer MR Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.
  • Polari A Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Centre for Youth Mental Health, The University of Melbourne, Australia; Orygen Youth Health, Melbourne, Australia.
  • Mossaheb N Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University of Vienna, Austria.
  • Schlögelhofer M Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria.
  • Smesny S University Hospital Jena, Germany.
  • Hickie IB Brain and Mind Centre, University of Sydney, Australia.
  • Berger G Child and Adolescent Psychiatric Service of the Canton of Zurich, Zurich, Switzerland.
  • Chen EYH Department of Psychiatry, University of Hong Kong, Hong Kong.
  • de Haan L Academic Medical Center, Amsterdam, the Netherlands.
  • Nieman DH Academic Medical Center, Amsterdam, the Netherlands.
  • Nordentoft M Mental Health Centre Copenhagen, Mental Health Services in the Capital Region, Copenhagen University Hospital, Denmark.
  • Riecher-Rössler A Psychiatric University Clinics Basel, Basel, Switzerland.
  • Verma S Department of Psychosis, Institute of Mental Health, Singapore, Singapore.
  • Thompson A Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Division of Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, England, UK; North Warwickshire Early Intervention in Psychosis Service, Coventry and Warwickshire NHS Partnership Trust, England, UK.
  • Yung AR Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Institute of Brain, Behaviour and Mental Health, University of Manchester, Manchester, UK; Greater Manchester West NHS Mental Health Foundation Trust, Manchester, England, UK.
  • McGorry PD Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Centre for Youth Mental Health, The University of Melbourne, Australia.
  • Nelson B Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; Centre for Youth Mental Health, The University of Melbourne, Australia.
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  • 2018-12-13
Published in:
  • Schizophrenia research. - 2018
Dynamic prediction
Joint modelling
Transition to psychosis
UHR
Adolescent
Adult
Disease Progression
Fatty Acids, Omega-3
Female
Follow-Up Studies
Humans
Male
Models, Statistical
Prognosis
Psychotic Disorders
Young Adult
English Considerable research has been conducted seeking risk factors and constructing prediction models for transition to psychosis in individuals at ultra-high risk (UHR). Nearly all such research has only employed baseline predictors, i.e. data collected at the baseline time point, even though longitudinal data on relevant measures such as psychopathology have often been collected at various time points. Dynamic prediction, which is the updating of prediction at a post-baseline assessment using baseline and longitudinal data accumulated up to that assessment, has not been utilized in the UHR context. This study explored the use of dynamic prediction and determined if it could enhance the prediction of frank psychosis onset in UHR individuals. An emerging statistical methodology called joint modelling was used to implement the dynamic prediction. Data from the NEURAPRO study (n = 304 UHR individuals), an intervention study with transition to psychosis study as the primary outcome, were used to investigate dynamic predictors. Compared with the conventional approach of using only baseline predictors, dynamic prediction using joint modelling showed significantly better sensitivity, specificity and likelihood ratios. As dynamic prediction can provide an up-to-date prediction for each individual at each new assessment post entry, it can be a useful tool to help clinicians adjust their prognostic judgements based on the unfolding clinical symptomatology of the patients. This study has shown that a dynamic approach to psychosis prediction using joint modelling has the potential to aid clinicians in making decisions about the provision of timely and personalized treatment to patients concerned.
Language
  • English
Open access status
green
Identifiers
Persistent URL
https://sonar.ch/global/documents/84214
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