Interventions targeting multiple risk factors for cardiovascular disease (CVD), including poor diet and physical inactivity, are more effective than interventions targeting a single risk factor. A motivational interviewing (MI) intervention can provide modest dietary improvements and physical activity increases, while adding cognitive behaviour therapy (CBT) skills may enhance the effects of MI. We designed a randomised controlled trial (RCT) to examine whether specific behaviour change techniques integrating MI and CBT result in favourable changes in weight and physical activity in those at high risk of CVD. A group and individual intervention will be compared to usual care. A group intervention offers potential benefits from social support and may be more cost effective.

Background: The first rate-limiting step for primary indicated prevention of psychosis is the detection of young people who may be at risk. The ability of specialized clinics to detect individuals at risk for psychosis is limited. A clinically based, individualized, transdiagnostic risk calculator has been developed and externally validated to improve the detection of individuals at risk in secondary mental health care. This calculator employs core sociodemographic and clinical predictors, including age, which is defined in linear terms. Recent evidence has suggested a nonlinear impact of age on the probability of psychosis onset. Aim: To define at a meta-analytical level the function linking age and probability of psychosis onset. To incorporate this function in a refined version of the transdiagnostic risk calculator and to test its prognostic performance, compared to the original specification. Design: Secondary analyses on a previously published meta-analysis and clinical register-based cohort study based on 2008-2015 routine secondary mental health care in South London and Maudsley (SLaM) National Health Service (NHS) Foundation Trust. Participants: All patients receiving a first index diagnosis of non-organic/non-psychotic mental disorder within SLaM NHS Trust in the period 2008-2015. Main outcome measure: Prognostic accuracy (Harrell's C). Results: A total of 91,199 patients receiving a first index diagnosis of non-organic and non-psychotic mental disorder within SLaM NHS Trust were included in the derivation (33,820) or external validation (54,716) datasets. The mean follow-up was 1,588 days. The meta-analytical estimates showed that a second-degree fractional polynomial model with power (-2, -1: age1 = age-2 and age2 = age-1) was the best-fitting model (P < 0.001). The refined model that included this function showed an excellent prognostic accuracy in the external validation (Harrell's C = 0.805, 95% CI from 0.790 to 0.819), which was statistically higher than the original model, although of modest magnitude (Harrell's C change = 0.0136, 95% CIs from 0.006 to 0.021, P < 0.001). Conclusions: The use of a refined version of the clinically based, individualized, transdiagnostic risk calculator, which allows for nonlinearity in the association between age and risk of psychosis onset, may offer a modestly improved prognostic performance. This calculator may be particularly useful in young individuals at risk of developing psychosis who access secondary mental health care.

To study the social determinants and cardiovascular risk factors for multimorbidity and the acquisition sequence of multimorbidity.

Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predictors automatically. To date, there is no empirical research comparing the prognostic accuracy of these two methods for the prediction of psychosis onset. In a first experiment, no improved performance was observed when machine-learning methods (LASSO and RIDGE) were applied-using the same predictors-to an individualised, transdiagnostic, clinically based, risk calculator previously developed on the basis of clinical-learning (predictors: age, gender, age by gender, ethnicity, ICD-10 diagnostic spectrum), and externally validated twice. In a second experiment, two refined versions of the published model which expanded the granularity of the ICD-10 diagnosis were introduced: ICD-10 diagnostic categories and ICD-10 diagnostic subdivisions. Although these refined versions showed an increase in apparent performance, their external performance was similar to the original model. In a third experiment, the three refined models were analysed under machine-learning and clinical-learning with a variable event per variable ratio (EPV). The best performing model under low EPVs was obtained through machine-learning approaches. The development of prognostic models on the basis of a priori clinical knowledge, large samples and adequate events per variable is a robust clinical prediction method to forecast psychosis onset in patients at-risk, and is comparable to machine-learning methods, which are more difficult to interpret and implement. Machine-learning methods should be preferred for high dimensional data when no a priori knowledge is available.

The epidemic of obesity is contributing to the increasing prevalence of people at high risk of cardiovascular disease (CVD), negating the medical advances in reducing CVD mortality. We compared the clinical and cost-effectiveness of an intensive lifestyle intervention consisting of enhanced motivational interviewing in reducing weight and increasing physical activity for patients at high risk of CVD.

Opioid prescribing has more than doubled in the UK between 1998 and 2016. Potential adverse health implications include dependency, falls and increased health expenditure.

Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study's risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whose potential utility in clinical practice was formally assessed. Our findings illustrated significant growth in precision psychiatry with promising progress towards real-world application. Nevertheless, these efforts have been inhibited by a preponderance of bias and overfitting, while the generalizability and clinical utility of many published models has yet to be formally established. Through improved methodological rigor during initial development, robust evaluations of reproducibility via independent validation, and evidence-based implementation frameworks, future research has the potential to generate risk prediction tools capable of enhancing clinical decision-making in psychiatric care.

Evidence suggests that digital mental health interventions (DMHIs) for common mental health conditions are effective. However, digital interventions, such as face-to-face therapies, pose risks to patients. A safe intervention is considered one in which the measured benefits outweigh the identified and mitigated risks.

Randomised controlled trials (RCTs) have shown the efficacy of CBTp, however, few studies have considered its long-term effectiveness in routine services. This study reports the outcomes of clients seen in a psychological therapies clinic, set up following positive results obtained from an RCT (Peters et al., 2010). The aims were to evaluate the effectiveness of CBTp, using data from the service's routine assessments for consecutive referrals over a 12 years period, and assess whether gains were maintained at a 6+ months' follow-up. Of the 476 consenting referrals, all clients (N = 358) who received ≥5 therapy sessions were offered an assessment at four time points (baseline, pre-, mid-, and end of therapy) on measures assessing current psychosis symptoms, emotional problems, general well-being and life satisfaction. A sub-set (N = 113) was assessed at a median of 12 months after finishing therapy. Following the waiting list (median of 3 months) clients received individualized, formulation-based CBTp for a median number of 19 sessions from 121 therapists with a range of experience receiving regular supervision. Clients showed no meaningful change on any measure while on the waiting list (Cohen's d <= 0.23). In contrast, highly significant improvements following therapy, all of which were significantly greater than changes during the waiting list, were found on all domains assessed (Cohen's d: 0.44-0.75). All gains were maintained at follow-up (Cohen's d: 0.29-0.82), with little change between end of therapy and follow-up (Cohen's d <= 0.18). Drop-out rate from therapy was low (13%). These results demonstrate the positive and potentially enduring impact of psychological therapy on a range of meaningful outcomes for clients with psychosis. The follow-up assessments were conducted on only a sub-set, which may not generalize to the full sample. Nevertheless this study is the largest of its kind in psychosis, and has important implications for the practice of CBTp in clinical services.

Depressive symptoms are common but rarely considered a risk factor for unhealthy lifestyles associated with cardiovascular disease (CVD). This study investigates whether depressive symptoms are associated with reduced physical activity (PA) in individuals at high risk of developing CVD.

The purpose of this research is to develop and evaluate methods for conducting cluster randomised trials in a primary care database that contains electronic patient records for large numbers of family practices. Cluster randomised trials are trials in which the units allocated represent groups of individuals, in this case family practices and their registered patients. Cluster randomised trials often suffer from the limitation that they include too few clusters, leading to problems of insufficient power and only imprecise estimation of the intraclass correlation coefficient, a key design parameter. This difficulty might be overcome by utilising databases that already hold electronic patient records for large numbers of practices. The protocol describes one application: a study of antibiotic prescribing for acute respiratory infection; a second protocol outlines an intervention in a less frequent chronic condition of public health importance, stroke.

Assess the impact of text-based electronic notifications on improving clinic attendance, in relation to study quality (according to risk of bias), and to assess simple ways in which notifications can be optimised (ie, impact of multiple notifications).

Up to 10% of adolescents report self-harm in the previous year. Non-fatal repetition is common (18% in 1 year), death from any cause shows a fourfold and suicide a 10-fold excess. Despite the scale of the problem, there is insufficient evidence for effective interventions for self-harm. Those who self-harm do so for a variety of different reasons. Different treatments may be more effective for subgroups of adolescents; however, little is known about which subgroups are appropriate for further study. This protocol outlines a systematic review and individual participant data meta-analysis (IPD-MA) to identify subgroups of adolescents for which therapeutic interventions for self-harm show some evidence of benefit.

Hypertension, especially if poorly controlled, is a key determinant of chronic kidney disease (CKD) development and progression to end stage renal disease (ESRD).

Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

Kidney transplant recipients (KTRs) with "operational tolerance" (OT) maintain a functioning graft without immunosuppressive (IS) drugs, thus avoiding treatment complications. Nevertheless, IS drugs can influence gene-expression signatures aiming to identify OT among treated KTRs.

The increasing prevalence of type 2 diabetes and suboptimal glycaemic control in Kuwait requires novel, wide-reaching, low-cost interventions to motivate and mobilise individuals towards more effective self-management. More than 2 million people in Kuwait own mobile phones. We will test whether automated personalised health text messages based on principles of motivational interviewing and are responsive to biodata delivered remotely is potentially effective in improving glycaemic control compared to usual care.

 To determine whether the incidence of pneumonia, peritonsillar abscess, mastoiditis, empyema, meningitis, intracranial abscess, and Lemierre's syndrome is higher in general practices that prescribe fewer antibiotics for self limiting respiratory tract infections (RTIs).

Background: Attenuated positive psychotic symptoms represent the defining features of the clinical high-risk for psychosis (CHR-P) criteria. The effectiveness of each available treatment for reducing attenuated positive psychotic symptoms remains undetermined. This network meta-analysis (NMA) investigates the consistency and magnitude of the effects of treatments on attenuated positive psychotic symptoms in CHR-P individuals, weighting the findings for acceptability. Methods: Web of Science (MEDLINE), PsycInfo, CENTRAL and unpublished/gray literature were searched up to July 18, 2017. Randomized controlled trials in CHR-P individuals, comparing at least two interventions and reporting on attenuated positive psychotic symptoms at follow-up were included, following PRISMA guidelines. The primary outcome (efficacy) was level of attenuated positive psychotic symptoms at 6 and 12 months; effect sizes reported as standardized mean difference (SMD) and 95% CIs in mean follow-up scores between two compared interventions. The secondary outcome was treatment acceptability [reported as odds ratio (OR)]. NMAs were conducted for both primary and secondary outcomes. Treatments were cluster-ranked by surface under the cumulative ranking curve values for efficacy and acceptability. Assessments of biases, assumptions, sensitivity analyses and complementary pairwise meta-analyses for the primary outcome were also conducted. Results: Overall, 1,707 patients from 14 studies (57% male, mean age = 20) were included, representing the largest evidence synthesis of the effect of preventive treatments on attenuated positive psychotic symptoms to date. In the NMA for efficacy, ziprasidone + Needs-Based Intervention (NBI) was found to be superior to NBI (SMD = -1.10, 95% CI -2.04 to -0.15), Cognitive Behavioral Therapy-French and Morrison protocol (CBT-F) + NBI (SMD = -1.03, 95% CI -2.05 to -0.01), and risperidone + CBT-F + NBI (SMD = -1.18, 95% CI -2.29 to -0.07) at 6 months. However, these findings did not survive sensitivity analyses. For acceptability, aripiprazole + NBI was significantly more acceptable than olanzapine + NBI (OR = 3.73; 95% CI 1.01 to 13.81) at 12 months only. No further significant NMA effects were observed at 6 or 12 months. The results were not affected by inconsistency or evident small-study effects, but only two studies had an overall low risk of bias. Conclusion: On the basis of the current literature, there is no robust evidence to favor any specific intervention for improving attenuated positive psychotic symptoms in CHR-P individuals.

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.