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Prevention strategies in clinical high-risks states for psychotic disorders: weighing up costs and benefits

Published online by Cambridge University Press:  19 July 2023

F. Schultze-Lutter*
Affiliation:
Department of Psychiatry and Psychotherapy, Heinrich-Heine University, Düsseldorf, Germany

Abstract

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Abstract

Today, the indicated prevention of psychosis prior to its first episode is mainly based on clinical high-risk of psychosis (CHR) criteria, namely ultra-high risk criteria and basic symptom criteria. These are associated with conversion-to-psychosis rates of about 30% within three years. Thus, many patients meeting CHR criteria will not progress to psychosis over a medium-term period, and the cost-benefit evaluation of CHR states is always complicated by the largely unknown individual psychosis risk of CHR patients. In consequence, for the lesser risk of adverse events, treatment recommendations commonly favour non-pharmacological strategies, in particular cognitive-behavioural psychotherapy. Yet, individual risk estimation in identified CHR patients is increasingly done with help of machine learning algorithms, which might help to identify CHR patients who would greatly benefit from an additional pharmacological intervention with low-dose antipsychotics. The presentation will discuss the evidence-base of such a multistep, machine learning informed prevention strategy.

Disclosure of Interest

None Declared

Type
Abstract
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the European Psychiatric Association
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