2026 ECNP Congress

PS01-1224

Comparative analysis of the diagnostic performance of clinical high risk for psychosis (CHR-P) instruments based on reported data

Igor Nastas — L. Boronin · Nicolae Testemitanu State University of Medicine and Pharmacy, Mental Health- Medical Psychology and Psychotherapy, Chisinau, Moldova

2026 ECNP Congress

Introduction

Clinical High Risk for Psychosis (CHR-P) is considered a transdiagnostic and multifactorial syndrome, associated with an increased risk of developing various psychotic disorders, including schizophrenia and perinatal psychoses. CHR-P represents an intermediate stage between a normal mental state and the first psychotic episode [1]. Multiple combinations of risk factors may influence the onset of psychosis [2]. At the same time, the absence of highly specific symptoms at this stage complicates diagnostic orientation and the establishment of clear diagnostic criteria [3].

Aims

The aim of this study was to evaluate the diagnostic and screening performance of instruments used in Clinical High Risk for Psychosis (CHR-P).

Methods

A systematic review of the literature was conducted focusing on screening and diagnostic instruments for CHR-P, followed by a comparative analysis of diagnostic performance based on reported sensitivity and specificity values. The primary inclusion criterion was the availability of sensitivity and specificity data related to CHR-P. The absence of these parameters constituted an exclusion criterion. The databases PubMed, Scopus, Web of Science, PsycINFO, and Research4Life were searched. Twelve screening and diagnostic instruments for CHR-P were identified. However, sensitivity and specificity parameters were available in only seven sources. These included seven high-risk psychosis screening instruments (ProdScreen [PS], Prodromal Questionnaire [PQ], Prodromal Questionnaire–Brief [PQ-B], PRIME Screen, PRIME Screen–Revised [PS-R], Youth Psychosis at Risk Questionnaire [Y-PARQ], SIPS Screen [SS]) and two diagnostic instruments assessing symptom severity (SIPS/SOPS). The included studies were published between 2003 and 2025. Based on the extracted data, receiver operating characteristic (ROC) curves were constructed to compare the discriminative performance of the principal screening and diagnostic instruments used in the clinical high risk for psychosis. ROC curves were constructed using MathWorks MATLAB (R2025b; Natick, MA: MathWorks, Inc.; 2025).

Results

Comparative analysis of the screening and diagnostic instruments used in high risk for psychosis revealed significant differences between structured clinical interview–based instruments and self-report screening tools. The Structured Interview for Prodromal Syndromes (SIPS) demonstrated the highest overall diagnostic accuracy, with an area under the curve (AUC) of 0.87 and consistently reported sensitivity values of 100%, accompanied by specificity values ranging from 71% to 74%. PRODscreen showed high diagnostic performance, with an AUC of 0.83, supporting its utility as an initial clinical triage tool. The combined use of three self-report screening instruments (PRIME Screen, PQ-Brief, and Y-PARQ) resulted in an intermediate AUC of 0.76, indicating an acceptable balance between sensitivity and specificity.

Conclusions

ROC curve analysis highlights the clear superiority of the SIPS interview over self-report screening instruments, reflected by an optimal combination of sensitivity and specificity.

Limitations

This analysis is based on sensitivity and specificity values reported across heterogeneous studies with differing populations and diagnostic thresholds. The resulting ROC curves represent a comparative visualization of instrument performance rather than true empirical ROC curves derived from a single prospective population.

Keywords:Clinical high risk for psychosis, prodromal symptoms, perinatal period, early diagnosis, sensitivity and specificity

References

  1. . Oliver D, Chesney E, Cullen AE, Davies C, Englund A, Gifford G, et al. Exploring causal mechanisms of psychosis risk. Neurosci Biobehav Rev. 2024 Jul;162:105699.
  2. . Lundin NB, Blouin AM, Cowan HR, Moe AM, Wastler HM, Breitborde NJK. Identification of Psychosis Risk and Diagnosis of First-Episode Psychosis: Advice for Clinicians. Psychol Res Behav Manag. 2024;17:1365–83.
  3. . Häfner H, Maurer K. Early detection of schizophrenia: current evidence and future perspectives. World Psychiatry. 2006 Oct;5(3):130–8.