The PReDicT study showed that predictive algorithm-guided antidepressant treatment reduces anxiety and improves functioning in patients with depression.

To estimate the costs, outcomes and cost-effectiveness of the PReDicT test compared with treatment as usual (TAU) for primary depression care in five European countries.

Within-trial economic analysis was conducted over 24 weeks from the health/social care and societal perspectives alongside the PReDicT trial (NCT02790970) in France, Germany, The Netherlands, Spain, and the UK, according to Consolidated Health Economic Evaluation Reporting Standards guidelines. We calculated quality-adjusted life-years (QALYs) based on the EQ-5D-5L, capability-weighted life-years based on the Oxford Capabilities Questionnaire – Mental Health (OxCAP-MH) (Germany and UK only), and costs for 2018 (€). Multiple imputation for missing data, multivariable regression for cost and outcome differences, and bootstrapping and sensitivity analyses for uncertainty were conducted.

There were significant outcome improvements (EQ-5D-5L PRedicT: +0.139; TAU: +0.140) and societal cost reductions (PRedicT: −€2589; TAU: −€2602) in both groups (N = 913) between the before and during trial periods. In the UK and Germany (n = 619), the PReDicT group showed significant additional capability well-being gains (OxCAP-MH: +2.127, p = 0.021). Cost-effectiveness probabilities ranged from 46 to 59% at trial level, but exceeded 80% in the UK. Results remained stable across different sensitivity analyses, with societal cost-effectiveness improved for those (self-)employed.

We observed potentially meaningful health and economic benefits of closely monitored antidepressant treatment, as implemented in both treatment and control arms of the PReDicT trial. The PReDicT test itself had some added benefits in improved capabilities and productivity, however, with great uncertainty and country-level variations in cost-effectiveness.

Information about a treatment’s benefits and harms available to a patient often relies on text. However, for many medical conditions, patients must trade off benefits and harms across multiple competing treatments. It remains unknown how to appropriately communicate information on benefits and harms to patients.

We compared three communication tools using textual information (Cochrane summary of findings table) or increasing combinations of textual and graphical information (Kilim and Vitruvian plots, respectively) to convey the available evidence.

Communication of Benefit–Risk Information, an online randomised controlled trial, is a three-group, parallel, open-label, automated, randomised controlled trial (no. NCT05917639). We recruited participants aged between 18 and 65 years from the general population. Participants were randomly allocated (1:1:1) to one of the three communication tools providing information on competing fictional treatments for social anxiety, and were asked to choose one based on externally provided preferences. The primary outcome was the perceived level of decisional conflict when selecting a treatment (decisional conflict scale (DCS): 0 = best, 100 = worst). Because this was an all-or-nothing, single-visit trial, only those participants providing data contributed to the primary analyses (modified intention to treat).

We recruited 2178 adults between 1 June and 27 November 2023. Vitruvian and Kilim plots outperformed the Cochrane summary of findings table on the primary outcome (adjusted mean difference −10.9, 95% CI −13.5 to −8.2, P < 0.0001 and −9.7, 95% CI −12.4 to −7.1, P < 0.0001), respectively). Results varied by participants’ literacy and numeracy skills, lived experience of the condition of interest, ethnic group, gender assigned at birth and age.

Combining graphical and textual information, as opposed to text only, improved communication and reduced decisional conflict when choosing across multiple competing medical interventions. Organisations involved in disseminating scientific evidence should consider endorsing a combined graphical and textual approach and adopting more intuitive and accessible communication methods. We identified several prognostic factors that should inform the development of future patient decision aids and communication of scientific findings.

NCT05917639.

People with psychosis have a life expectancy that is reduced by 15 years, mainly owing to preventable physical illnesses of which obesity is a precursor. Obesity is three times more common in individuals with psychosis, and antipsychotics are an important cause. Prediction could individualise obesity treatment, but current models are not fully actionable for individuals.

To test whether antipsychotic-induced weight increase at 1 year is causally mediated by weight change in the first 12 weeks of treatment, and then develop and internally validate a causal actionable prediction pathway to prevent antipsychotic-induced obesity.

This was a post hoc analysis of a clinical trial of olanzapine versus haloperidol which recruited 263 participants with first-episode psychosis. We conducted two distinct analyses: causal mediation and prediction modelling, within which there were two sequential models (a baseline model to predict 12-week outcome and a 12-week model to predict 1-year outcome), followed by counterfactual prediction. In the first analysis, we used parallel causal mediation analysis to determine the natural direct and indirect and total effects of antipsychotic choice on weight in 97 participants, considering two mediators: weight change from 0 to 12 weeks, and weight change from 12 to 52 weeks. In the second analysis, we first developed a baseline causal actionable prediction model to predict weight gain at 12 weeks in 172 participants and then a 12-week model to predict obesity at 1 year in 97 of the participants. Finally, we demonstrated counterfactual prediction.

Antipsychotic-induced weight gain at 1 year appeared to be causally mediated by weight change during the first 12 weeks of treatment (indirect effect 5.70; 95% CI 2.83 to 8.66). At internal validation, the discrimination c-statistic for the baseline causal actionable prediction model was 0.728 (95% CI 0.661 to 0.801), and the calibration slope was 0.768 (95% CI 0.436 to 1.21). For the 12-week model, the c-statistic was 0.904 (95% CI 0.820 to 0.961), and the calibration slope was 0.601 (95% CI −0.0633 to 1.21). We used the models to predict the counterfactual outcomes of antipsychotic choice and 12-week weight change.

Our results show that it may be early rather than later weight change that causally mediates antipsychotic-induced weight gain at 1 year. They also demonstrate the potential for causal actionable prediction of counterfactuals for true precision medicine, although this is tempered by the feasibility scope of this study and small sample size. Our results are hypothesis-generating and not yet clinically deployable.

Despite omics technologies gaining traction in clinical settings, particularly in oncology, challenges persist in their widespread adoption due to the pre-requisite robust evidence supporting efficacy and cost-effectiveness. This study aims to explore the experiences of organizations working in the health technology assessment (HTA) field in evaluating omics technologies, with a particular focus on the adoption and application of specific assessment frameworks.

We conducted a global survey to gather insights into current practices and frameworks used in HTA evaluations of omics technologies.

We gathered responses from thirty-nine participants representing organizations across twenty-nine countries and five continents. Among them, 51 percent (n = 20) reported experience in evaluating omics technologies, including multi-omics tests for early disease detection, biomarker-based cancer diagnostics, and advanced genomic sequencing techniques. Only three organizations employed specific assessment frameworks: the Adelaide Health Technology Assessment Agency in Australia, the Netherlands Cancer Institute, and the Andalusian HTA Agency in Spain. These frameworks address key evaluation aspects such as analytical and clinical validity, clinical and personal utility, organizational impact, and ethical, legal, and social implications of omics technologies.

Despite their relevance, the limited adoption of tailored frameworks highlights the need for more structured and context-specific approaches to facilitate the integration of omics technologies into healthcare systems. Collaborative efforts among stakeholders, including patients, healthcare providers, policymakers, and industry representatives, are crucial for devising robust evaluation strategies addressing the complexities of omics technologies comprehensively.

Lived experience – how individuals perceive and interact with their environment – plays a central role in understanding mental health. Yet, insights into this first-person perspective, including subjective thoughts, emotions, and socio-contextual influences, remain limited in current research approaches.

To address this gap, we developed StreetMind, a scalable, secure, and user-friendly digital citizen science platform grounded in a psycho-sociogeographic framework. The platform collects self-reported data on individuals’ activity spaces through a mobile app and web interface, capturing location visits, travel routes, and daily experiences. These subjective reports are combined with objective real-time health, environmental, and sociocultural data to generate integrated community “footprints.”

Initial usage data (N = 1,010 for location and route entries; N = 509 for daily experiential data) demonstrate the platform’s structural robustness and functional feasibility. StreetMind enables classification of daily experiences by linking personal perceptions with contextual environmental data. This integration facilitates the identification and quantification of key environmental and psychosocial factors associated with mental well-being.

StreetMind offers a novel, data-rich mapping of health–environment interactions by merging individual lived experience with environmental metrics. This approach supports the creation of dynamic “health–environment spaces” and holds promise for informing public health strategies and advancing precision mental health care.

Although global knowledge on paediatric cardiomyopathies has advanced, prospective cohort studies from Brazil, particularly those integrating clinical and genetic data, remain limited.

To describe the clinical and genetic characteristics of paediatric cardiomyopathy patients and identify mortality predictors in a metropolitan region of Brazil.

Prospective observational study of paediatric patients with cardiomyopathies. Clinical data, genetic findings, and survival were analysed using Kaplan–Meier curves.

A total of 45 cases, male predominance (55.6%), and mean age at diagnosis of 6.5 years. Dilated and hypertrophic cardiomyopathy were the most common (33.3%). The main reason for diagnosis was the investigation of cardiovascular symptoms (60.9%). Genetic investigation occurred in 66.6%, a positivity rate of 60%. Multi-organ/system involvement was significantly associated with a positive genetic result (77.7%, p = 0.017). Mortality was 11.1%; survival was significantly lower in the following conditions: ejection fraction < 30% (p < 0.0001), functional class III/IV (p < 0.0001), heart failure (p = 0.0091), use of three or more cardiovascular medications (p < 0.001), N-Terminal Pro-B-Type natriuretic peptide >1000pg/mL (p = 0.004), and heart transplant indication (p < 0.001).

These findings provide novel data in Brazil, highlight a high rate of positive genetic test, particularly among patients with systemic involvement and identify key clinical predictors of mortality to guide risk stratification and care.

It remains unclear which individuals with subthreshold depression benefit most from psychological intervention, and what long-term effects this has on symptom deterioration, response and remission.

To synthesise psychological intervention benefits in adults with subthreshold depression up to 2 years, and explore participant-level effect-modifiers.

Randomised trials comparing psychological intervention with inactive control were identified via systematic search. Authors were contacted to obtain individual participant data (IPD), analysed using Bayesian one-stage meta-analysis. Treatment–covariate interactions were added to examine moderators. Hierarchical-additive models were used to explore treatment benefits conditional on baseline Patient Health Questionnaire 9 (PHQ-9) values.

IPD of 10 671 individuals (50 studies) could be included. We found significant effects on depressive symptom severity up to 12 months (standardised mean-difference [s.m.d.] = −0.48 to −0.27). Effects could not be ascertained up to 24 months (s.m.d. = −0.18). Similar findings emerged for 50% symptom reduction (relative risk = 1.27–2.79), reliable improvement (relative risk = 1.38–3.17), deterioration (relative risk = 0.67–0.54) and close-to-symptom-free status (relative risk = 1.41–2.80). Among participant-level moderators, only initial depression and anxiety severity were highly credible (P > 0.99). Predicted treatment benefits decreased with lower symptom severity but remained minimally important even for very mild symptoms (s.m.d. = −0.33 for PHQ-9 = 5).

Psychological intervention reduces the symptom burden in individuals with subthreshold depression up to 1 year, and protects against symptom deterioration. Benefits up to 2 years are less certain. We find strong support for intervention in subthreshold depression, particularly with PHQ-9 scores ≥ 10. For very mild symptoms, scalable treatments could be an attractive option.