Research on clinical high risk for psychosis (CHR-P) is central for the early detection
field and the deployment of suitable clinical care pathways aiming at preventing the
consequences of psychosis. In the last decades, the field has been engaged in a robust
effort to develop prognostic models for transdiagnostic staging and individualized
risk stratification, as shown in the recent meta-analysis by Sanfelici et al. (
1
). However, in such vibrant yet tumultuous growth, the accelerated search for scalable
predictors was not immune to disharmonies and involuntary distortions, such as the
neglect of important clinical confounders. This is the case of baseline exposure to
antipsychotics (APs) in individuals assessed for CHR-P state (
2
,
3
,
4
,
5
). Such ongoing AP exposure at the moment of CHR evaluation may alter the natural
course of transition to psychosis, blurring the clinical presentation and/or modulating
the frequency or severity of positive psychotic symptoms and, thereby, confounding
the formal psychometric transition threshold for psychosis (
2
). This aspect was recently investigated through a meta-analytic approach (
6
), which confirmed that CHR-P subjects undergoing AP treatment at the time of enrollment
have different longitudinal trajectories and risk of transition to psychosis as compared
to AP-naïve CHR-P subjects (29% vs. 16%; risk ratio of transition, 1.47). This strongly
indicates that baseline AP treatment is a plausible clinical proxy for higher clinical
severity, which is associated with increased risk of imminent transition to psychosis
at follow-up. Furthermore, it cannot be excluded that a fraction of AP-exposed individuals
who met formal CHR-P criteria are in fact subjects with a pharmacologically attenuated
first-episode psychosis (
4
). Surreptitiously conflating these two populations (i.e., true CHR-P and first-episode
psychosis who present attenuated positive psychotic symptoms due to concomitant AP
treatment) might have intuitive, cascading consequences on the precision of prognostic
risk estimates.To read this article in full you will need to make a payment
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Article info
Publication history
Published online: May 15, 2021
Accepted:
March 12,
2021
Received:
January 21,
2021
Footnotes
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© 2021 Society of Biological Psychiatry.
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- Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the ArtBiological PsychiatryVol. 88Issue 4
- PreviewThe clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data.
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- Reply to: Individualized Diagnostic and Prognostic Models for Psychosis Risk Syndromes: Do Not Underestimate Antipsychotic ExposureBiological PsychiatryVol. 90Issue 6
- PreviewWe thank the authors for their interest in our meta-analysis on diagnostic and prognostic models for clinical high risk (CHR) individuals (1). Raballo et al. (2) discuss the critical issue of antipsychotics (APs) within the CHR paradigm and its potential role in predictive models for detection of transition to psychosis. Specifically, Raballo et al. highlighted three points: 1) the majority of predictive models lack information about APs in high-risk cohorts, 2) baseline APs could influence the clinical presentation of individuals and/or modify the longitudinal development of their symptoms, and 3) the presence of APs at baseline in CHR individuals might signal higher psychopathological severity and thus be a proxy for higher risk of developing psychosis.
- Full-Text
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