Return to College After a First Episode of Psychosis.

A first episode of psychosis (FEP) can derail a patient's educational goals, including attainment of a college education, and this can have lasting ramifications for socioeconomic and health outcomes. Despite this, few studies have examined return to college, which is an important index of real-world educational success after a FEP. In this study, we conducted a longitudinal medical record review of patients in a transdiagnostic outpatient FEP program and performed survival analysis, setting return to college as the endpoint, among the subset of patients whose college education was interrupted. We found that 82% (93/114) of college-enrolled FEP individuals experienced disruptions to their education after FEP, but that return to college also occurred in a substantial proportion (49/88, 56%) among those on leave who had follow-up data. In this sample, the median time to college return was 18 months. When separated by baseline diagnostic category, FEP patients with affective psychotic disorders (FEAP, n = 45) showed faster time to college return than those with primary psychotic disorders (FEPP, n = 43) (median 12 vs 24 mo; P = .024, unadjusted). When adjusted for having no more than 1 psychiatric hospitalization at intake and absence of cannabis use in the 6 months prior to intake (which were also significant predictors), differences by diagnostic category were more significant (hazard ratio 2.66, 95% CI 1.43-4.94, P = .002). Participation in education is an important outcome for stakeholders, and students with FEP can be successful in accomplishing this goal.

Two-year diagnostic stability in a real-world sample of individuals with early psychosis.

OBJECTIVES: Diagnostic shifts in first episode psychosis (FEP) are not uncommon. Many studies examining diagnostic stability use structured diagnostic interviews. Less is known about the stability of FEP diagnoses made clinically. METHODS: We conducted a retrospective chart review of patients enrolled in a transdiagnostic FEP clinic. For the 96 patients followed clinically at least 2 years, we compared diagnoses at intake and 24 months. RESULTS: Diagnostic stability was high for bipolar disorder (89%), schizoaffective disorder (89%), and schizophrenia (82%). Psychosis not otherwise specified (13%) was more unstable, with limited baseline differences that would enable clinicians to predict who would convert to a primary psychotic vs affective psychotic disorder. CONCLUSIONS: Our real-world clinical sample shows that FEP diagnoses, with the exception of unspecified psychosis, are diagnostically stable, even without structured diagnostic interviews.

Analysis of risk factor domains in psychosis patient health records.

BACKGROUND: Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. RESULTS: We designed and evaluated multiple multilayer perceptron and radial basis function neural networks to predict the sentences in a patient's EHR that are associated with one or more of seven readmission risk factor domains that we identified. In contrast to our baseline cosine similarity model that is based on the methodologies of prior works, our deep learning approaches achieved considerably better F1 scores (0.83 vs 0.66) while also being more scalable and computationally efficient with large volumes of data. Additionally, we found that integrating clinically relevant multiword expressions during preprocessing improves the accuracy of our models and allows for identifying a wider scope of training data in a semi-supervised setting. CONCLUSION: We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show results for our topic extraction model and identify additional features we will be incorporating in the future.

McLean OnTrack: a transdiagnostic program for early intervention in first-episode psychosis.

AIMS: Most programs specializing in the treatment of first-episode psychosis in the United States focus on schizophrenia. However, many early psychosis patients do not fit into this diagnostic category. Here we describe McLean OnTrack, an intensive outpatient treatment program that accepts all comers with first-episode psychosis. METHODS: We assessed baseline characteristics of patients in the 2.5 years since program initiation. We examined how initial referral diagnoses compare with current diagnoses, calculating the proportion of diagnostic changes. RESULTS: At 2.5 years, patients in McLean OnTrack consist of 30 (33.0%) individuals with primary psychotic disorder, 40 (44.0%) with affective psychosis, 19 (20.9%) with psychotic disorder not otherwise specified (NOS) who do not meet full criteria for either category and two (2.2%) individuals with no psychosis. Although patients with affective psychosis had higher pre-morbid functioning, all three categories of psychosis had similar rates of prior hospitalizations and substance use. The retention rate in the psychotic disorder NOS group was lower than that in affective and primary psychotic disorders. Finally, diagnoses changed over the course of treatment in 50.5% of patients. CONCLUSIONS: Diagnostic heterogeneity appears to be the norm among patients with first-episode psychosis, and diagnoses commonly evolve over the illness course. Baseline indices of illness severity were similar across categories and suggest the need for early intervention, irrespective of specific diagnosis. We discuss the benefits and challenges of a transdiagnostic approach to early intervention in first-episode psychosis, treating patients who share many but not all characteristics.