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Background and Hypothesis Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. Study Design Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. Study Results PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68–0.77], Boston Children's Hospital 0.80 [0.75–0.84], and Boston Medical Center 0.83 [0.79–0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80–0.92) and major depressive disorder with psychosis were the most variable (0.57–0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08–0.62). Conclusions We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis. [ABSTRACT FROM AUTHOR]

Aim: To identify the demographic predictors of lack of current mental health (MH) treatment among university students with a schizophrenia spectrum disorder (SSD). Methods: Adult university students with a self‐identified diagnosis of an SSD (schizophreniform, schizophrenia, schizoaffective disorder) were identified from the 2019–2020 Healthy Minds Study survey. In this study, pertinent demographic factors included age, race/ethnicity, sex assigned at birth, gender identity, sexual orientation, parental education, financial stress, and employment. Multivariable modelling was used to investigate the demographic predictors of lack of current psychotherapy treatment, no current antipsychotic use, and lack of any MH treatment (defined as concurrent lack of psychotherapy and antipsychotic treatment). Results: Of the 135 included students with a SSD, the median age was 23 years old and 79 (58.5%) were assigned female at birth. Fifty‐five participants (40.7%) lacked any current MH treatment. In fully adjusted models, lack of current MH treatment was associated with working more than 20 h per week (OR 2.9 [1.2–7.1], p = 0.02). No current antipsychotic use was associated with Hispanic/Latino race/ethnicity (OR 4.2 (1.2–14.5), p = 0.04). Lack of current psychotherapy treatment was associated with cisgender male identity (OR 5.5 [2.0–15.2], p < 0.01), working greater than 20 hours per week (OR 6.5 [2.2–19.2], p < 0.01), and having one or more structural or attitudinal barriers to care (OR = 4.6 [1.5–13.9], p < 0.01). Conclusions: The demographic predictors of lack of current MH treatment varied between psychotherapy and antipsychotic use, suggesting university health centres should consider interventions targeting several at‐risk populations to increase treatment use among students with a SSD. [ABSTRACT FROM AUTHOR]

Background and Hypothesis: Early detection of psychosis is critical for improving outcomes. Algorithms to predict or detect psychosis using electronic health record (EHR) data depend on the validity of the case definitions used, typically based on diagnostic codes. Data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis., Study Design: Using EHRs at three health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into five higher-order groups. 1,133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings., Study Results: PPVs across all diagnostic groups and hospital systems exceeded 70%: Massachusetts General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62)., Conclusions: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the development of risk prediction models designed to predict or detect undiagnosed psychosis.