According to existing evidence, during menopause transition, women with psychosis may present with exacerbated psychiatric symptoms, due to age-related hormonal changes.

We aimed to (a) replicate this evidence, using age as a proxy for peri/menopausal status; (b) investigate how clinical presentation is affected by concomitant factors, including hyperprolactinaemia, dose and metabolism of prescribed antipsychotics using cross-sectional and longitudinal analyses.

Secondary analysis on 174 women aged 18–65, from the IMPaCT (Improving physical health and reducing substance use in psychosis) randomised controlled trial. We compared women aged below (N = 65) and above 40 (N = 109) for (a) mental health status with the Positive and Negative Syndrome Scale (PANSS) and Montgomery Asberg Depression Rating Scale; (b) current medications and (c) prolactin levels, at baseline and at follow-up (12/15 months later).

Women aged above 40 showed higher baseline PANSS total score (mean ± s.d. = 53.4 ± 14.1 v. 48.0 ± 13.0, p = 0.01) and general symptoms scores (28.0 ± 7.4 v. 25.7 ± 7.8, p = 0.03) than their younger counterparts. Progressive sub-analysis revealed that this age-related difference was observed only in women with non-affective psychosis (n = 93) (PANSS total score: 57.1 ± 13.6 v. 47.0 ± 14.4, p < 0.005) and in those prescribed antipsychotic monotherapy with olanzapine or clozapine (n = 25) (PANSS total score: 63 ± 16.4 v. 42.8 ± 10.9, p < 0.05).

Among all women with hyperprolactinaemia, those aged above 40 also had higher PANSS positive scores than their younger counterparts. No longitudinal differences were found between age groups.

Women aged above 40 showed worse psychotic symptoms than younger women. This difference seems diagnosis-specific and may be influenced by antipsychotics metabolism. Further longitudinal data are needed considering the menopause transition.

A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.

To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.

We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model.

We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.

We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.