Personalized medicine begins with the phenotype: identifying antipsychotic response phenotypes in a first‐episode psychosis cohort

Abstract

PEPs group. Personalized medicine begins with the phenotype: identifying antipsychotic response phenotypes in a first-episode psychosis cohort.Aims: Here, we present a clustering strategy to identify phenotypes of antipsychotic (AP) response by using longitudinal data from patients presenting first-episode psychosis (FEP). Method: One hundred and ninety FEP with complete data were selected from the PEPs project. The efficacy was assessed using total PANSS, and adverse effects using total UKU, during one-year follow-up. We used the Klm3D method to cluster longitudinal data. Results: We identified four clusters: cluster A, drug not toxic and beneficial; cluster B, drug beneficial but toxic; cluster C, drug neither toxic nor beneficial; and cluster D, drug toxic and not beneficial. These groups significantly differ in baseline demographics, clinical, and neuropsychological characteristics (PAS, total PANSS, DUP, insight, pIQ, age of onset, cocaine use and family history of mental illness). Conclusions:The results presented here allow the identification of phenotypes of AP response that differ in well-known simple and classic clinical variables opening the door to clinical prediction and application of personalized medicine.

Characterizing Use of a Multicomponent Digital Intervention to Predict Treatment Outcomes in First-Episode Psychosis: Cluster Analysis

“…These techniques have previously been used for detecting similar symptom trajectories in mental health interventions [31]. K-means clustering techniques have also been used to identify and characterize participants based on unidimensional and multidimensional trajectories [32][33][34].…”

Real-World Data on the Adverse Metabolic Effects of Second-Generation Antipsychotics and Their Potential Determinants in Adult Patients: A Systematic Review of Population-Based Studies

“…Antipsychotic treatment selection should be individualized and based on effectiveness and tolerability in addition to patients’ preferences. Increasing knowledge of the tolerability profile of well-established antipsychotics as well as recently introduced antipsychotics will help achieve individualized antipsychotic treatment selection [ 10 ].…”

Commentary on Kohne and van OS view on precision psychiatry

“…They can also be used to design optimal dynamic treatment regimens by combining the individual clinical evolution and the treatment history of each patient. The application of new analytical approaches is already helping to identify clusters of patients more similar in terms of a particular characteristic, such as response to particular treatments or cognitive performance (Mas et al, 2020; Varo et al, 2020). This information can be used for clinical prediction or to design tailored pharmacological and psychological interventions for subgroups of patients that are expected to benefit the most from these treatments.…”