We 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. Importantly,
Raballo et al. also reported that only 35.7% of the studies included in our meta-analysis accounted
for APs during follow-up, representing a substantial limitation for thoroughly investigating
its role.To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Biological PsychiatryAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Individualized diagnostic and prognostic models for patients with psychosis risk syndromes: A meta-analytic view on the state of the art.Biol Psychiatry. 2020; 88: 349-360
- Individualized diagnostic and prognostic models for psychosis risk syndromes: Do not underestimate antipsychotic exposure.Biol Psychiatry. 2021; 90: e33-e35
- Meta-analyzing the prevalence and prognostic effect of antipsychotic exposure in clinical high-risk (CHR): When things are not what they seem.Psychol Med. 2020; 50: 2673-2681
- When to initiate antipsychotic treatment for psychotic symptoms: At the premorbid phase or first episode of psychosis?.Aust N Z J Psychiatry. 2021; 55: 314-323
- Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression.JAMA Psychiatry. 2021; 78: 195-209
- Off-label prescription of quetiapine in psychiatric disorders.Expert Rev Neurother. 2007; 7: 841-852
- International trends in antipsychotic use: A study in 16 countries, 2005–2014.Eur Neuropsychopharmacol. 2017; 27: 1064-1076
- Predictors of transition to psychosis in individuals at clinical high risk.Curr Psychiatry Rep. 2019; 21: 39
- Using neuroimaging to help predict the onset of psychosis.Neuroimage. 2017; 145: 209-217
- Neuroanatomical markers of genetic liability to psychosis and first episode psychosis: A voxelwise meta-analytical comparison.World J Biol Psychiatry. 2014; 15: 219-228
- Emotional and behavioral symptomatology reported by help-seeking youth at clinical high-risk for psychosis.Schizophr Res. 2015; 162: 79-85
- Schizophrenia Proneness Instrument, Adult Version.Giovanni Fioriti Editore, Rome, Italy2007
- Course of clinical high-risk states for psychosis beyond conversion.Eur Arch Psychiatry Clin Neurosci. 2018; 268: 39-48
- Antipsychotic treatment in clinical high risk for psychosis: Protective, iatrogenic or further risk flag?.Aust N Z J Psychiatry. 2021; 55: 442-444
Article info
Publication history
Published online: May 15, 2021
Accepted:
March 10,
2021
Received:
March 9,
2021
Footnotes
Identification
Copyright
© 2021 Society of Biological Psychiatry.
ScienceDirect
Access this article on ScienceDirectLinked Article
- Individualized Diagnostic and Prognostic Models for Psychosis Risk Syndromes: Do Not Underestimate Antipsychotic ExposureBiological PsychiatryVol. 90Issue 6
- PreviewResearch 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.
- Full-Text
- Preview
