Literature DB >> 28212173

Prediction of conversion to psychosis in individuals with an at-risk mental state: a brief update on recent developments.

Anita Riecher-Rössler1, Erich Studerus.   

Abstract

PURPOSE OF REVIEW: So far, only little more than one-third of individuals classified as being at-risk for psychosis have been shown to actually convert to frank psychosis during follow-up. There have therefore been enormous efforts to improve the accuracy of predicting this transition. We reviewed the most recent studies in the field with the aim to clarify whether accuracy of prediction has been improved by the different research endeavors and what could be done to further improve it, and/or what alternative goals research should pursue. RECENT
FINDINGS: A total of 56 studies published between May 2015 and December 2016 were included, of which eight were meta-analyses. New meta-analytical evidence confirms that established instruments for checking clinical risk criteria have an excellent clinical utility in individuals referred to high-risk services. Within a such identified group of ultra-high-risk (UHR) individuals, especially Brief Limited Intermittent Psychotic Symptoms and Attenuated Psychotic Symptoms seem to predict transition. Further assessments should be performed within the UHR individuals, as risk of transition seems particularly high in those with an even higher severity of certain symptoms such as suspiciousness or anhedonia, in those with lower global or social functioning, poor neurocognitive performance or cannabis abuse. Also, electroencephalography, neuroimaging and blood biomarkers might contribute to improving individual prediction. The most promising approach certainly is a staged multidomain assessment. Risk calculators to integrate all data for an individualized prediction are being developed.
SUMMARY: Prediction of psychosis is already possible with an excellent prognostic performance based on clinical assessments. Recent studies show that this accuracy can be further improved by using multidomain approaches and modern statistics for individualized prediction. The challenge now is the translation into the clinic with a broad clinical implementation.

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Mesh:

Year:  2017        PMID: 28212173     DOI: 10.1097/YCO.0000000000000320

Source DB:  PubMed          Journal:  Curr Opin Psychiatry        ISSN: 0951-7367            Impact factor:   4.741


  9 in total

1.  The prodromal phase: Time to broaden the scope beyond transition to psychosis?

Authors:  Fabio Ferrarelli; Daniel Mathalon
Journal:  Schizophr Res       Date:  2020-01-07       Impact factor: 4.939

2.  Predictors of study drop-out and service disengagement in patients at clinical high risk for psychosis.

Authors:  Letizia Leanza; Erich Studerus; Amatya J Mackintosh; Katharina Beck; Leonie Seiler; Christina Andreou; Anita Riecher-Rössler
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2019-10-23       Impact factor: 4.328

3.  Development and Validation of a Dynamic Risk Prediction Model to Forecast Psychosis Onset in Patients at Clinical High Risk.

Authors:  Erich Studerus; Katharina Beck; Paolo Fusar-Poli; Anita Riecher-Rössler
Journal:  Schizophr Bull       Date:  2020-02-26       Impact factor: 9.306

4.  Multimodal prognosis of negative symptom severity in individuals at increased risk of developing psychosis.

Authors:  Daniel J Hauke; André Schmidt; Erich Studerus; Christina Andreou; Anita Riecher-Rössler; Joaquim Radua; Joseph Kambeitz; Anne Ruef; Dominic B Dwyer; Lana Kambeitz-Ilankovic; Theresa Lichtenstein; Rachele Sanfelici; Nora Penzel; Shalaila S Haas; Linda A Antonucci; Paris Alexandros Lalousis; Katharine Chisholm; Frauke Schultze-Lutter; Stephan Ruhrmann; Jarmo Hietala; Paolo Brambilla; Nikolaos Koutsouleris; Eva Meisenzahl; Christos Pantelis; Marlene Rosen; Raimo K R Salokangas; Rachel Upthegrove; Stephen J Wood; Stefan Borgwardt
Journal:  Transl Psychiatry       Date:  2021-05-24       Impact factor: 6.222

5.  Development of a probability calculator for psychosis risk in children, adolescents, and young adults.

Authors:  Tyler M Moore; Monica E Calkins; Adon F G Rosen; Ellyn R Butler; Kosha Ruparel; Paolo Fusar-Poli; Nikolaos Koutsouleris; Philip McGuire; Tyrone D Cannon; Ruben C Gur; Raquel E Gur
Journal:  Psychol Med       Date:  2021-01-12       Impact factor: 10.592

6.  Sex differences in cognitive functioning of patients at-risk for psychosis and healthy controls: Results from the European Gene-Environment Interactions study.

Authors:  Stephanie Menghini-Müller; Erich Studerus; Sarah Ittig; Lucia R Valmaggia; Matthew J Kempton; Mark van der Gaag; Lieuwe de Haan; Barnaby Nelson; Rodrigo A Bressan; Neus Barrantes-Vidal; Célia Jantac; Merete Nordentoft; Stephan Ruhrmann; Garbiele Sachs; Bart P Rutten; Jim van Os; Anita Riecher-Rössler
Journal:  Eur Psychiatry       Date:  2020-03-13       Impact factor: 5.361

7.  Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust.

Authors:  Paolo Fusar-Poli; Nomi Werbeloff; Grazia Rutigliano; Dominic Oliver; Cathy Davies; Daniel Stahl; Philip McGuire; David Osborn
Journal:  Schizophr Bull       Date:  2019-04-25       Impact factor: 9.306

8.  Brain functional connectivity data enhance prediction of clinical outcome in youth at risk for psychosis.

Authors:  Guusje Collin; Alfonso Nieto-Castanon; Martha E Shenton; Ofer Pasternak; Sinead Kelly; Matcheri S Keshavan; Larry J Seidman; Robert W McCarley; Margaret A Niznikiewicz; Huijun Li; Tianhong Zhang; Yingying Tang; William S Stone; Jijun Wang; Susan Whitfield-Gabrieli
Journal:  Neuroimage Clin       Date:  2019-11-20       Impact factor: 4.881

9.  EEG microstates as biomarker for psychosis in ultra-high-risk patients.

Authors:  Renate de Bock; Amatya J Mackintosh; Franziska Maier; Stefan Borgwardt; Anita Riecher-Rössler; Christina Andreou
Journal:  Transl Psychiatry       Date:  2020-08-24       Impact factor: 6.222

  9 in total

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