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Background <p>The At Risk Mental State (ARMS) (also known as the Ultra or Clinical High Risk) criteria identify individuals at high risk for psychotic disorder. However, there is a need to improve prediction as only about 18% of individuals meeting these criteria develop a psychosis with 12-months. We have developed and internally validated a prediction model using characteristics that could be used in routine practice.</p> Methods <p>We conducted a systematic review and individual participant data meta-analysis, followed by focus groups with clinicians and service users to ensure that identified factors were suitable for routine practice. The model was developed using logistic regression with backwards selection and an individual participant dataset. Model performance was evaluated via discrimination and calibration. Bootstrap resampling was used for internal validation.</p> Results <p>We received data from 26 studies contributing 3739 individuals; 2909 from 20 of these studies, of whom 359 developed psychosis, were available for model building. Age, functioning, disorders of thought content, perceptual abnormalities, disorganised speech, antipsychotic medication, cognitive behavioural therapy, depression and negative symptoms were associated with transition to psychosis. The final prediction model included disorders of thought content, disorganised speech and functioning. Discrimination of 0.68 (0.5-1 scale; 1=perfect discrimination) and calibration of 0.91 (0-1 scale; 1=perfect calibration) showed the model had fairly good predictive ability.</p> Discussion <p>The statistically robust prediction model, built using the largest dataset in the field to date, could be used to guide frequency of monitoring and enable rational use of health resources following assessment of external validity and clinical utility.</p>

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Clinical prediction model for transition to psychosis in individuals meeting At Risk Mental State criteria

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月27日

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  • Laura J. Bonnett,
  • Alexandra Hunt,
  • Allan Flores,
  • Catrin Tudur Smith,
  • Filippo Varese,
  • Rory Byrne,
  • Heather Law,
  • Marko Milicevic,
  • Rebekah Carney,
  • Sophie Parker,
  • Alison R. Yung,
  • Jai Shah,
  • Marita Pruessner,
  • Ashok Malla,
  • Tim Ziermans,
  • Sarah Durston,
  • W. C. Chang,
  • Anthony Morrison,
  • David Shiers,
  • Mark van der Gaag,
  • William McFarlane,
  • Patrick Welsh,
  • Paul Tiffin,
  • Anita Riecher-Rössler,
  • Erich Studerus,
  • Frauke Schultze-Lutter,
  • Stephan Ruhrmann,
  • Joachim Klosterkötter,
  • Suk Kyoon An,
  • Inti Qurashi,
  • Nusrat Huasain,
  • Simon Chu,
  • Paul Amminger,
  • Magdalena Kotlicka-Antczak,
  • Jean Addington,
  • Silvia Rigucci,
  • Swapna Verma,
  • Chun Ting Chan,
  • Masahiro Katsura,
  • Kazunori Matsumoto,
  • Tsutomu Takahashi,
  • Pablo Gaspar,
  • Rolando Castillo,
  • Sebastian Corral,
  • Rocio Mayol-Troncoso,
  • Alejandro Maturana,
  • Peter Uhlhaas,
  • Nicolas Rüsch

摘要

Background

The At Risk Mental State (ARMS) (also known as the Ultra or Clinical High Risk) criteria identify individuals at high risk for psychotic disorder. However, there is a need to improve prediction as only about 18% of individuals meeting these criteria develop a psychosis with 12-months. We have developed and internally validated a prediction model using characteristics that could be used in routine practice.

Methods

We conducted a systematic review and individual participant data meta-analysis, followed by focus groups with clinicians and service users to ensure that identified factors were suitable for routine practice. The model was developed using logistic regression with backwards selection and an individual participant dataset. Model performance was evaluated via discrimination and calibration. Bootstrap resampling was used for internal validation.

Results

We received data from 26 studies contributing 3739 individuals; 2909 from 20 of these studies, of whom 359 developed psychosis, were available for model building. Age, functioning, disorders of thought content, perceptual abnormalities, disorganised speech, antipsychotic medication, cognitive behavioural therapy, depression and negative symptoms were associated with transition to psychosis. The final prediction model included disorders of thought content, disorganised speech and functioning. Discrimination of 0.68 (0.5-1 scale; 1=perfect discrimination) and calibration of 0.91 (0-1 scale; 1=perfect calibration) showed the model had fairly good predictive ability.

Discussion

The statistically robust prediction model, built using the largest dataset in the field to date, could be used to guide frequency of monitoring and enable rational use of health resources following assessment of external validity and clinical utility.