2026 ECNP Congress

PS01-1232

Three-level, resistance-informed clinical staging model for schizophrenia: a Delphi-based expert consensus

Alejo Corrales — G. Vazquez, F. Rebok, G. Bartoli, C. Benavente Pinto, A. Cabrera, S. Camino, R. Corral, P.D. Gargoloff, P.R. Gargoloff, A. Goldchuk, F. Lamaison, E.A. Leiderman, T. Maresca, C. Morra, C. Prokopez, J. Ramirez, E. Rubio Dominguez, D. Sotelo, J.J. Vilaprino, M. Vilapriño Duprat, M. Cetkovich-Bakmas · Department of Psychiatry, National University of Argentina UNT, Buenos Aires, Argentina

2026 ECNP Congress

Introduction Treatment-resistant schizophrenia (TRS) remains a major clinical challenge, yet current definitions—typically based on the failure of two adequate antipsychotic trials—are limited by their binary nature and do not adequately capture key sources of diagnostic uncertainty (1,3). These include non-adherence, subtherapeutic drug exposure, pharmacokinetic variability, and contextual factors, which may lead to misclassification of pseudo-resistance as true refractoriness. Moreover, existing staging models often assume a direct relationship between illness progression and treatment resistance without systematically verifying this link, potentially delaying access to effective interventions such as clozapine.Objective

The primary objective was to refine and overcome limitations in current TRS definitions. we propose a resistance-informed refinement of staging models based on three hierarchical levels of certainty: possible, probable, and definite TRS. This three-level structure builds upon prior national consensus initiatives and ongoing multidisciplinary work by the same expert group, integrating principles of treatment optimization, adherence verification, and pseudoresistance into a structured staging framework.The possible level corresponds to the conventional TRS definition, reflecting insufficient response to two adequate antipsychotic trials (2). At this stage, apparent non-response may still be explained by incomplete adherence, insufficient drug exposure, rapid treatment switching, substance use, or contextual and psychosocial factors. The probable level incorporates objective verification of adherence through sustained treatment with long-acting injectable antipsychotics under structured clinical monitoring. The definite level requires confirmation of adequate biological exposure through therapeutic drug monitoring and, where available, pharmacogenetic assessment (1). To enhance reproducibility and clinical applicability, operational criteria were defined for each resistance level, including standardized thresholds for symptom severity (e.g., CGI-S ≥4, PANSS ≥60, GAF ≤50), minimum duration of LAI treatment (3–6 months), and TDM requirements across antipsychotics. Additionally, temporal and clinical specifiers were incorporated. Temporal specifiers (early ≤5 years vs late >5 years) were derived from longitudinal cohort data, while clinical specifiers follow a dimensional framework encompassing positive, negative, cognitive, and affective domains.

Methods

A national panel of 20 psychiatrists developed a consensus-based framework through iterative literature review, structured expert discussion, and a formal Delphi process. Panel members rated 10 items across four domains using a 5-point Likert scale, with consensus defined a priori as ≥75% agreement.

Results

All items achieved consensus. We propose a resistance-informed clinical staging framework integrating longitudinal illness trajectory, symptom dimensions, functional impairment, and graded certainty of treatment non-response. The model is organized around progressive reduction of uncertainty regarding adherence and biological drug exposure. Treatment resistance is classified into three hierarchical categories—possible, probable, and definite—reflecting verification of treatment delivery and biological exposure. Clinical and temporal specifiers capture symptom persistence and timing of resistance onset. The framework emphasizes systematic exclusion of reversible causes prior to resistance classification and links stage allocation to evidence-informed treatment pathways, including treatment optimization, adherence verification, and timely clozapine initiation.

Conclusions

Integrating verified treatment resistance into clinical staging addresses a major limitation of existing models and provides a pragmatic heuristic structure for individualized care. Supported by high levels of expert agreement, the framework may reduce misclassification, facilitate earlier definitive interventions, and support stratified clinical research. Prospective validation and international replication are required.

References

1-Corrales, A., Abadi, A., Bartoli, G., Benavente Pinto … Cetkovich-Bakmas, M. (2026).First Argentine Consensus on Schizophrenia: Part III. Guidelines for a Comprehensive Approach to Treatment. Vertex (Buenos Aires, Argentina), 37(171, Jan–Mar), 72–126. https://doi.org/10.53680/vertex.v37i171.979 2-Correll, C. U., & Howes, O. D. (2021). Treatment-resistant schizophrenia: Definition, predictors, and therapy options. The Journal of Clinical Psychiatry, 82(5), MY20096AH1C. https://doi.org/10.4088/JCP.MY20096AH1C 3-Howes, Oliver D et al. “Treatment-Resistant Schizophrenia: Treatment Response and Resistance in Psychosis (TRRIP) Working Group Consensus Guidelines on Diagnosis and Terminology.” The American journal of psychiatry vol. 174,3 (2017): 216-229. doi:10.1176/appi.ajp.2016.16050503.