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.

The relationship between early symptom severity, improvement and remission in first episode psychosis with jumping to conclusions.

It is suggested that Jumping To Conclusions (JTC) reasoning bias might contribute to the distortion of external reality. However, the association between psychotic manifestations and JTC is obscure, especially if general intelligence is considered as a mediator. The aim of this study is to investigate the relation between severity, early clinical improvement and remission of symptoms in First Episode Psychosis (FEP) with JTC as an explanatory factor. One hundred seventy-one FEP individuals were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and one month after treatment initiation. Clinical improvement was ascribed as symptom change one-month post-baseline measurements. Symptomatic remission was assessed with the Andreasen severity criteria and JTC with the Beads Task, operationalized through Draws To Decision (DTD) (the lower the number of DTD, the higher the JTC bias). Regarding symptoms severity, total psychotic, total positive psychotic, and hallucinations-item PANSS scores showed a negative association with JTC after controlling for IQ. Regarding early clinical improvement, the association with JTC was non-significant. No significant association was detected between one month remission status of FEP and JTC. Our findings indicate that severity of positive symptoms is not associated with hastiness in decision-making, but rather with a heightened conservatism in terms of increased data gathering. Further research is required to replicate the results and clarify the cognitive processes involved.