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OBJECTIVE: To examine the accuracy and likely clinical usefulness of the Psychosis Metabolic Risk Calculator (PsyMetRiC) in predicting up-to six-year risk of incident metabolic syndrome in an Australian sample of young people with first-episode psychosis. METHOD: We conducted a retrospective study at a secondary care early psychosis treatment service among people aged 16-35 years, extracting relevant data at the time of antipsychotic commencement and between one-to-six-years later. We assessed algorithm accuracy primarily via discrimination (C-statistic), calibration (calibration plots) and clinical usefulness (decision curve analysis). Model updating and recalibration generated a site-specific (Australian) PsyMetRiC version. RESULTS: We included 116 people with baseline and follow-up data: 73% male, mean age 20.1 years, mean follow-up 2.6 years, metabolic syndrome prevalence 13%. C-statistics for both partial- (C = 0.71, 95% CI 0.64-0.75) and full-models (C = 0.72, 95% CI 0.65-0.77) were acceptable; however, calibration plots demonstrated consistent under-prediction of risk. Recalibration and updating led to slightly improved C-statistics, greatly improved agreement between observed and predicted risk, and a narrow window of likely clinical usefulness improved significantly. CONCLUSION: An updated and recalibrated PsyMetRiC model, PsyMetRiC-Australia, shows promise. Validation in a large sample is required to confirm its accuracy and clinical usefulness for the Australian population.
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AIM: Educational attainment is consistently highly valued by young people with mental ill health, yet maintenance and completion of education is a challenge. This paper reports on the implementation of a supported education programme for youth mental health. METHODS: Between 10 October 2019 and 10 October 2020, a supported education programme was delivered within primary and tertiary youth mental health services. A description of the programme, context, and adjustments required due to COVID-19 is presented, and the educational outcomes of young people referred to the programme were explored. Two case studies are also presented. RESULTS: The programme received 71 referrals over this period, of which 70.4% had not yet completed secondary school and 68% were experiencing multiple mental health conditions. Overall outcomes were positive, with 47.5% of the 40 young people who chose to engage with the programme maintaining or re-engaging with education. However, the remainder of those who engaged withdrew from the programme, often reporting challenges due to COVID-19 such as social isolation or increased uncertainty. Additionally, a number of young people declined or disengaged from the programme to focus on employment. CONCLUSION: This report of the experience of integrating a supported employment programme in Australian youth mental health services reinforces the need for such support, and provides preliminary evidence for its successful implementation as part of routine care. The disengagement in response to COVID-19 highlights the real-world challenges of the pandemic, while young people's voicing of employment goals indicates the need for combined educational and vocational support-to assist transition and progression between these goals.
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People with severe mental illness (SMI), including schizophrenia and related psychoses and bipolar disorder, are at greater risk for obesity compared with people without mental illness. An altered resting metabolic rate (RMR) may be a key driving factor; however, published studies have not been systematically reviewed. This systematic review and meta-analysis aimed to determine whether the RMR of people with SMI assessed by indirect calorimetry differs from (i) controls, (ii) predictive equations and (iii) after administration of antipsychotic medications. Five databases were searched from database inception to March 2022. Thirteen studies providing nineteen relevant datasets were included. Study quality was mixed (62 % considered low quality). In the primary analysis, RMR in people with SMI did not differ from matched controls (n 2, standardised mean difference (SMD) = 0·58, 95 % CI -1·01, 2·16, P = 0·48, I2 = 92 %). Most predictive equations overestimated RMR. The Mifflin-St. Jeor equation appeared to be most accurate (n 5, SMD = -0·29, 95 % CI -0·73, 0·14, P = 0·19, I2 = 85 %). There were no significant changes in RMR after antipsychotic administration (n 4, SMD = 0·17, 95 % CI -0·21, 0·55, P = 0·38, I2 = 0 %). There is little evidence to suggest there is a difference in RMR between people with SMI and people without when matched for age, sex, BMI and body mass, or that commencement of antipsychotic medication alters RMR.