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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. [ABSTRACT FROM AUTHOR]

Background: It remains unclear which biological mechanisms affect neurocognition in first episode psychosis (FEP) patients. There is minimal evidence from current literature suggesting an association between duration of untreated psychosis (DUP) or duration of untreated illness (DUI) and cognitive decline in FEP patients. It is still controversial whether genetic factors, such as polygenic risk score for schizophrenia, determine observed cognitive deficits. The study of interplay between DUP, DUI and genetic risk factors might be important to understand underlying pathways. Methods: Ninety FEP patients where recruited during Athens FirstEpisode Psychosis Research study between 2015–2018. All participants provided inform consent. DUP for each patient was defined by NOS-DUP (Nottingham onset schedule: modified DUP version) assessment tool and DUI was determined using the symptom onset in schizophrenia (SOS) inventory. DNA was collected in order to create polygenic risk score (PGC) r schizophrenia for each individual using SNPs selected according to the significance of their association with the phenotype at nominal p-value thresholds of 0,05. WAIS-IV total score and subscales, i.e. Verbal Comprehension (VC), Perceptual Reasoning (PR), Working Memory (WM), Processing Speed (PS) and moreover index differences between these 4 subscales were applied as a measure of cognitive deficit. Results: Generalized linear model analysis, after adjustment for years of education and gender, found no significant main effect of DUP, DUI or PGC on any cognitive subscale. Furthermore, conducting an exploratory analysis for possible interactions between DUP/DUI and PGC (n=90), we found statistically significant findings of DUP x PGC (F=8,175, p=,005) and DUI x PGC (F=5,592, p=,021) for the cognitive variable of VC/WM difference. The interplay between DUP and PCG and DUI and PGC was associated with observed differences between VM and WM in our FEP sample. Discussion: Our preliminary results are consistent with recent literature suggesting that neurocognition is not determined by DUP, DUI or PGC for schizophrenia. Novel approach based on WAIS-IV indexes of interest allows to explore subtle differences between cognitive subdomains. Elucidating underlying interplay between genetic and disease-related mechanisms could be important to understand the core feature of cognitive deficit in FEP patients. [ABSTRACT FROM AUTHOR]