Literature DB >> 29413807

Meta-analytical prognostic accuracy of the Comprehensive Assessment of at Risk Mental States (CAARMS): The need for refined prediction.

D Oliver1, M Kotlicka-Antczak2, A Minichino3, G Spada4, P McGuire5, P Fusar-Poli6.   

Abstract

Primary indicated prevention is reliant on accurate tools to predict the onset of psychosis. The gold standard assessment for detecting individuals at clinical high risk (CHR-P) for psychosis in the UK and many other countries is the Comprehensive Assessment for At Risk Mental States (CAARMS). While the prognostic accuracy of CHR-P instruments has been assessed in general, this is the first study to specifically analyse that of the CAARMS. As such, the CAARMS was used as the index test, with the reference index being psychosis onset within 2 years. Six independent studies were analysed using MIDAS (STATA 14), with a total of 1876 help-seeking subjects referred to high risk services (CHR-P+: n=892; CHR-P-: n=984). Area under the curve (AUC), summary receiver operating characteristic curves (SROC), quality assessment, likelihood ratios, and probability modified plots were computed, along with sensitivity analyses and meta-regressions. The current meta-analysis confirmed that the 2-year prognostic accuracy of the CAARMS is only acceptable (AUC=0.79 95% CI: 0.75-0.83) and not outstanding as previously reported. In particular, specificity was poor. Sensitivity of the CAARMS is inferior compared to the SIPS, while specificity is comparably low. However, due to the difficulties in performing these types of studies, power in this meta-analysis was low. These results indicate that refining and improving the prognostic accuracy of the CAARMS should be the mainstream area of research for the next era. Avenues of prediction improvement are critically discussed and presented to better benefit patients and improve outcomes of first episode psychosis.
Copyright © 2017 The Authors. Published by Elsevier Masson SAS.. All rights reserved.

Entities:  

Keywords:  CAARMS; Clinical utility; Prevention; Prognostic accuracy; Psychosis

Mesh:

Year:  2018        PMID: 29413807     DOI: 10.1016/j.eurpsy.2017.10.001

Source DB:  PubMed          Journal:  Eur Psychiatry        ISSN: 0924-9338            Impact factor:   5.361


  11 in total

1.  Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.

Authors:  Jessica Irving; Rashmi Patel; Dominic Oliver; Craig Colling; Megan Pritchard; Matthew Broadbent; Helen Baldwin; Daniel Stahl; Robert Stewart; Paolo Fusar-Poli
Journal:  Schizophr Bull       Date:  2021-03-16       Impact factor: 9.306

2.  Preventive psychiatry: a blueprint for improving the mental health of young people.

Authors:  Paolo Fusar-Poli; Christoph U Correll; Celso Arango; Michael Berk; Vikram Patel; John P A Ioannidis
Journal:  World Psychiatry       Date:  2021-06       Impact factor: 79.683

3.  Antipsychotics are related to psychometric conversion to psychosis in ultra-high-risk youth.

Authors:  Antonio Preti; Andrea Raballo; Anna Meneghelli; Angelo Cocchi; Maria Meliante; Simona Barbera; Lara Malvini; Emiliano Monzani; Mauro Percudani
Journal:  Early Interv Psychiatry       Date:  2021-05-05       Impact factor: 2.721

4.  Efficacy and Acceptability of Interventions for Attenuated Positive Psychotic Symptoms in Individuals at Clinical High Risk of Psychosis: A Network Meta-Analysis.

Authors:  Cathy Davies; Joaquim Radua; Andrea Cipriani; Daniel Stahl; Umberto Provenzani; Philip McGuire; Paolo Fusar-Poli
Journal:  Front Psychiatry       Date:  2018-06-12       Impact factor: 4.157

Review 5.  A Comprehensive Review of Computational Methods for Automatic Prediction of Schizophrenia With Insight Into Indigenous Populations.

Authors:  Randall Ratana; Hamid Sharifzadeh; Jamuna Krishnan; Shaoning Pang
Journal:  Front Psychiatry       Date:  2019-09-12       Impact factor: 4.157

6.  Pan-London Network for Psychosis-Prevention (PNP).

Authors:  Paolo Fusar-Poli; Andrés Estradé; Tom J Spencer; Susham Gupta; Silvia Murguia-Asensio; Savithasri Eranti; Kerry Wilding; Olivier Andlauer; Jonathan Buhagiar; Martin Smith; Sharon Fitzell; Victoria Sear; Adelaide Ademan; Andrea De Micheli; Philip McGuire
Journal:  Front Psychiatry       Date:  2019-10-11       Impact factor: 4.157

7.  What Causes the Onset of Psychosis in Individuals at Clinical High Risk? A Meta-analysis of Risk and Protective Factors.

Authors:  Dominic Oliver; Thomas J Reilly; Ottone Baccaredda Boy; Natalia Petros; Cathy Davies; Stefan Borgwardt; Philip McGuire; Paolo Fusar-Poli
Journal:  Schizophr Bull       Date:  2020-01-04       Impact factor: 9.306

8.  Predictors of Outcomes in Adolescents With Clinical High Risk for Psychosis, Other Psychiatric Symptoms, and Psychosis: A Longitudinal Protocol Study.

Authors:  Silvia Molteni; Eleonora Filosi; Maria Martina Mensi; Giulia Spada; Chiara Zandrini; Federica Ferro; Matteo Paoletti; Anna Pichiecchio; Ilaria Bonoldi; Umberto Balottin
Journal:  Front Psychiatry       Date:  2019-12-03       Impact factor: 4.157

9.  Real-world digital implementation of the Psychosis Polyrisk Score (PPS): A pilot feasibility study.

Authors:  Dominic Oliver; Giulia Spada; Amir Englund; Edward Chesney; Joaquim Radua; Abraham Reichenberg; Rudolf Uher; Philip McGuire; Paolo Fusar-Poli
Journal:  Schizophr Res       Date:  2020-04-24       Impact factor: 4.939

10.  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

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