Investigating environmental parameters involved in psychosis etiopathology. Translation and test-retest reliability of four scales.

First Episode Psychosis (FEP) emergence and clinical outcome might be attributed to various parameters, wherein gene - environment interaction plays a pivotal role in. Four specified psychometric tools, that have been used for the evaluation of possible environmental, social and psychological parameters involved in the etiopathology and clinical course of psychosis are the following: Social Environment Assessment Tool (SEAT), Discrimination (DISC), Brief Core-Schema Scales (BCSS) and Life-Threatening Events-Brief Life-Events Questionnaire (LTE-Q). These tools were used in the Athens-FEP Study assessment schedule, which investigates the gene-environment interaction among patients presenting with FEP. The goal of the present study is to present them in regard to their content, their use in international literature, their translation in Greek, and their test-retest reliability. SEAT, DISC and BCSS were provided to the Athens FEP Study by the European Network of National Schizophrenia Network studying Gene-Environment interactions (EUGEI) research project. LTE-Q was already translated into Greek and was selected as befitting the purposes of the FEP-Study. The EUGEI instruments were translated into Greek language by two independent translators for each instrument. All translators were qualified in the administration of the English version of the scales after being trained online through a comprehensive work-package training set provided by the EUGEI. The principal investigator of the Athens-FEP project checked and approved the final versions of the questionnaires. The four tools were administered to 32 subjects, all diagnosed with FEP, participating in the Athens-FEP project. Intraclass correlation coefficients (ICCs) were used to assess the agreement between scores of the four questionnaires after first and second administration. The scales were administered to our subjects twice, with an intermediate period of three weeks between the first and second administrations, by three qualified researchers. There was a statistically significant agreement for almost all measurements of the four questionnaires, except for the frequency dimension of DISC. Agreement between those measurements was very high (ICCs>0.8). Our study is an indication that the Greek versions of the psychometric tools are reliable, although a more thorough test of their psychometric properties is needed. All four questionnaires have unique properties that differentiate them from other similar tools. Moreover, the DISC is the only discrimination scale translated into Greek. More importantly, the translated questionnaires are part of a broad, well-established research package of psychometric tools, suitable for the evaluation of environmental risk factors potentially involved in early psychosis, which might represent a valuable scientific resource in the Greek research field.

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.

BACKGROUND: Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis. METHOD: Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts. RESULTS: Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naïve. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P < .0001). It also predicted 6-week remission status in the Danish cohort (ROC-AUC = 67.74, P < .0001), demonstrating reliability. CONCLUSIONS: Using items from common and validated clinical scales, our model significantly predicted early remission in patients with first-episode psychosis. Although replicated in an independent cohort, forward testing between machine learning models and clinicians' assessment should be undertaken to evaluate the possible utility as a routine clinical tool.