Search

Malaria infected erythrocytes utilize the parasite protein VAR2CSA to bind to a unique presentation of chondroitin sulfate (CS) for their placenta specific tropism. Interestingly, many cancers express a similar form of CS, thereby termed oncofetal CS (ofCS). The distinctive tropism of malaria infected erythrocytes and the identification of oncofetal CS, therefore, represent potentially potent tools for cancer targeting. Here we describe an intriguing drug delivery platform that effectively mimics infected erythrocytes and their specificity for ofCS. We used a lipid catcher-tag conjugation system for the functionalization of erythrocyte membrane-coated drug carriers with recombinant VAR2CSA (rVAR2). We show that these malaria mimicking erythrocyte nanoparticles (MMENPs) loaded with docetaxel (DTX) specifically target and kill melanoma cells in vitro. We further demonstrate effective targeting and therapeutic efficacy in a xenografted melanoma model. These data thus provide a proof of concept for the use of a malaria biomimetic for tumor targeted drug delivery. Given the broad presentation of ofCS found across various types of malignancies, this biomimetic may therefore show potential as a broadly targeted cancer therapy against multiple tumor indications.

Most acute phase antipsychotic drug trials in schizophrenia last only a few weeks, but patients must usually take these drugs much longer. We examined the long‐term efficacy of antipsychotic drugs in acutely ill patients using network meta‐analysis. We searched the Cochrane Schizophrenia Group register up to March 6, 2022 for randomized, blinded trials of at least 6‐month duration on all second‐generation and 18 first‐generation antipsychotics. The primary outcome was change in overall symptoms of schizophrenia; secondary outcomes were all‐cause discontinuation; change in positive, negative and depressive symptoms; quality of life, social functioning, weight gain, antiparkinson medication use, akathisia, serum prolactin level, QTc prolongation, and sedation. Confidence in the results was assessed by the CINeMA (Confidence in Network Meta‐Analysis) framework. We included 45 studies with 11,238 participants. In terms of overall symptoms, olanzapine was on average more efficacious than ziprasidone (standardized mean difference, SMD=0.37, 95% CI: 0.26‐0.49), asenapine (SMD=0.33, 95% CI: 0.21‐0.45), iloperidone (SMD=0.32, 95% CI: 0.15‐0.49), paliperidone (SMD=0.28, 95% CI: 0.11‐0.44), haloperidol (SMD=0.27, 95% CI: 0.14‐0.39), quetiapine (SMD=0.25, 95% CI: 0.12‐0.38), aripiprazole (SMD=0.16, 95% CI: 0.04‐0.28) and risperidone (SMD=0.12, 95% CI: 0.03‐0.21). The 95% CIs for olanzapine versus aripiprazole and risperidone included the possibility of trivial effects. The differences between olanzapine and lurasidone, amisulpride, perphenazine, clozapine and zotepine were either small or uncertain. These results were robust in sensitivity analyses and in line with other efficacy outcomes and all‐cause discontinuation. Concerning weight gain, the impact of olanzapine was higher than all other antipsychotics, with a mean difference ranging from –4.58 kg (95% CI: –5.33 to –3.83) compared to ziprasidone to –2.30 kg (95% CI: –3.35 to –1.25) compared to amisulpride. Our data suggest that olanzapine is more efficacious than a number of other antipsychotic drugs in the longer term, but its efficacy must be weighed against its side effect profile.

Clozapine is recommended by national and international guidelines for people with treatment-resistant schizophrenia. However, available meta-analyses of randomised controlled trials have not shown superior efficacy of clozapine when compared with other second-generation antipsychotics, with heterogeneity identified between the original studies. We aimed to use individual patient data (IPD) to account for potential reasons of variability and to synthesise an adjusted estimate for the difference in efficacy between clozapine and other second-generation antipsychotics for treatment-resistant schizophrenia.

Metabolic side effects of antipsychotic drugs can have serious health consequences and may increase mortality. Although persons with schizophrenia often take these drugs for a long time, their mid‐ to long‐term metabolic effects have been studied little so far. This study aimed to evaluate the mid‐ to long‐term metabolic side effects of 31 antipsychotics in persons with schizophrenia by applying a random‐effects Bayesian network meta‐analysis. We searched the Cochrane Schizophrenia Group's Study‐Based Register of Trials (up to April 27, 2020) and PubMed (up to June 14, 2021). We included published and unpublished, open and blinded randomized controlled trials with a study duration >13 weeks which compared any antipsychotic in any form of administration with another antipsychotic or with placebo in participants diagnosed with schizophrenia. The primary outcome was weight gain measured in kilograms. Secondary outcomes included “number of participants with weight gain”, fasting glucose, total cholesterol, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, and triglycerides. We identified 137 eligible trials (with 35,007 participants) on 31 antipsychotics, with a median follow‐up of 45 weeks. Chlorpromazine produced the most weight gain (mean difference to placebo: 5.13 kg, 95% credible interval, CrI: 1.98 to 8.30), followed by clozapine (4.21 kg, 95% CrI: 3.03 to 5.42), olanzapine (3.82 kg, 95% CrI: 3.15 to 4.50), and zotepine (3.87 kg, 95% CrI: 2.14 to 5.58). The findings did not substantially change in sensitivity and network meta‐regression analyses, although enriched design, drug company sponsorship, and the use of observed case instead of intention‐to‐treat data modified the mean difference in weight gain to some extent. Antipsychotics with more weight gain were often also among the drugs with worse outcome in fasting glucose and lipid parameters. The confidence in the evidence ranged from low to moderate. In conclusion, antipsychotic drugs differ in their propensity to induce metabolic side effects in mid‐ to long‐term treatment. Given that schizophrenia is often a chronic disorder, these findings should be given more consideration than short‐term data in drug choice.

Schizophrenia is a common, severe, and usually chronic disorder. Maintenance treatment with antipsychotic drugs can prevent relapse but also causes side-effects. We aimed to compare the efficacy and tolerability of antipsychotics as maintenance treatment for non-treatment resistant patients with schizophrenia.

ObjectiveA network meta-analysis (NMA) usually assesses multiple outcomes across several treatment comparisons. The Vitruvian plotaims to facilitate communication of multiple outcomes from NMAs to patients and clinicians.MethodsWe developed this tool following the recommendations on the communication of benefit–risk information from the available literature. We collected and implemented feedback from researchers, statisticians, methodologists, clinicians and people with lived experience of physical and mental health issues.ResultsWe present the Vitruvian plot, which graphically presents absolute estimates and relative performance of competing interventions against a common comparator for several outcomes of interest. We use two alternative colour schemes to highlight either the strength of statistical evidence or the confidence in the evidence. Confidence in the evidence is evaluated across six domains (within-study bias, reporting bias, indirectness, imprecision, heterogeneity and incoherence) using the Confidence in Network Meta-Analysis (CINeMA) system.ConclusionsThe Vitruvian plotallows reporting of multiple outcomes from NMAs, with colourings appropriate to inform credibility of the presented evidence.

ObjectiveThe current practice in meta-analysis of the effects of psychopharmacological interventions ignors the administered dose or restricts the analysis in a dose range. This may introduce unnecessary uncertainty and heterogeneity. Methods have been developed to integrate the dose–effect models in meta-analysis.MethodsWe describe the two-stage and the one-stage models to conduct a dose–effect meta-analysis using common or random effects methods. We illustrate the methods on a dataset of selective serotonin reuptake inhibitor antidepressants. The dataset comprises 60 randomised controlled trials. The dose–effect is measured on an odds ratio scale and is modelled using restricted cubic splines to detect departure from linearity.ResultsThe estimated summary curve indicates that the probability of response increases up to 30 mg/day of fluoxetine-equivalent which results in reaching 50% probability to respond. Beyond 40 mg/day, no further increase in the response is observed. The one-stage model includes all studies, resulting in slightly less uncertainty than the two-stage model where only part of the data is analysed.ConclusionsThe dose–effect meta-analysis enables clinicians to understand how the effect of a drug changes as a function of its dose. Such analysis should be conducted in practice using the one-stage model that incorporates evidence from all available studies.

Many psychosocial and psychological interventions are used in patients with schizophrenia, but their comparative efficacy in the prevention of relapse is not known. We aimed to evaluate the efficacy, acceptability, and tolerability of psychosocial and psychological interventions for relapse prevention in schizophrenia.

Pancreatic ductal adenocarcinoma (PDAC) poses significant clinical challenges, often presenting as unresectable with limited biopsy options. Here, we show that circulating tumor cells (CTCs) offer a promising alternative, serving as a “liquid biopsy” that enables the generation of in vitro3D models and highly aggressive in vivomodels for functional and molecular studies in advanced PDAC. Within the retrieved CTC pool (median 65 CTCs/5 mL), we identify a subset (median content 8.9%) of CXCR4+CTCs displaying heightened stemness and metabolic traits, reminiscent of circulating cancer stem cells. Through comprehensive analysis, we elucidate the importance of CTC-derived models for identifying potential targets and guiding treatment strategies. Screening of stemness-targeting compounds identified stearoyl-coenzyme A desaturase (SCD1) as a promising target for advanced PDAC. These results underscore the pivotal role of CTC-derived models in uncovering therapeutic avenues and ultimately advancing personalized care in PDAC.

Fewer than half of new drugs have data on their comparative benefits and harms against existing treatment options at the time of regulatory approval in Europe and the USA. Even when active-comparator trials exist, they might not produce meaningful data to inform decisions in clinical practice and health policy. The uncertainty associated with the paucity of well designed active-comparator trials has been compounded by legal and regulatory changes in Europe and the USA that have created a complex mix of expedited programmes aimed at facilitating faster access to new drugs. Comparative evidence generation is even sparser for medical devices. Some have argued that the current process for regulatory approval needs to generate more evidence that is useful for patients, clinicians, and payers in health-care systems. We propose a set of five key principles relevant to the European Medicines Agency, European medical device regulatory agencies, US Food and Drug Administration, as well as payers, that we believe will provide the necessary incentives for pharmaceutical and device companies to generate comparative data on drugs and devices and assure timely availability of evidence that is useful for decision making. First, labelling should routinely inform patients and clinicians whether comparative data exist on new products. Second, regulators should be more selective in their use of programmes that facilitate drug and device approvals on the basis of incomplete benefit and harm data. Third, regulators should encourage the conduct of randomised trials with active comparators. Fourth, regulators should use prospectively designed network meta-analyses based on existing and future randomised trials. Last, payers should use their policy levers and negotiating power to incentivise the generation of comparative evidence on new and existing drugs and devices, for example, by explicitly considering proven added benefit in pricing and payment decisions.

Schizophrenia is one of the most common, burdensome, and costly psychiatric disorders in adults worldwide. Antipsychotic drugs are its treatment of choice, but there is controversy about which agent should be used. We aimed to compare and rank antipsychotics by quantifying information from randomised controlled trials.

Depression is the single largest contributor to non-fatal health loss worldwide. Second-generation antidepressants are the first-line option for pharmacological management of depression. Optimising their use is crucial in reducing the burden of depression; however, debate about their dose dependency and their optimal target dose is ongoing. We have aimed to summarise the currently available best evidence to inform this clinical question.

AimTo describe the pattern of the prevalence of mental health problems during the first year of the COVID-19 pandemic and examine the impact of containment measures on these trends.MethodsWe identified articles published until 30 August 2021 that reported the prevalence of mental health problems in the general population at two or more time points. A crowd of 114 reviewers extracted data on prevalence, study and participant characteristics. We collected information on the number of days since the first SARS-CoV-2 infection in the study country, the stringency of containment measures and the number of cases and deaths. We synthesised changes in prevalence during the pandemic using a random-effects model. We used dose-response meta-analysis to evaluate the trajectory of the changes in mental health problems.ResultsWe included 41 studies for 7 mental health conditions. The average odds of symptoms increased during the pandemic (mean OR ranging from 1.23 to 2.08). Heterogeneity was very large and could not be explained by differences in participants or study characteristics. Average odds of psychological distress, depression and anxiety increased during the first 2 months of the pandemic, with increased stringency of the measures, reported infections and deaths. The confidence in the evidence was low to very low.ConclusionsWe observed an initial increase in the average risk of psychological distress, depression-related and anxiety-related problems during the first 2 months of the pandemic. However, large heterogeneity suggests that different populations had different responses to the challenges imposed by the pandemic.

BackgroundApproximately 30% of patients experience substantial improvement in depression after 2 months without treatment, and 45% with antidepressants. The smallest worthwhile difference (SWD) refers to an intervention’s smallest beneficial effect over a comparison patients deem worthwhile given treatment burdens (harms, expenses and inconveniences), but is undetermined for antidepressants.ObjectiveEstimating the SWD of commonly prescribed antidepressants for depression compared to no treatment.MethodsThe SWD was estimated as a patient-required difference in response rates between antidepressants and no treatment after 2 months. An online cross-sectional survey using Prolific, MQ Mental Health and Amazon Mechanical Turk crowdsourcing services in the UK and USA between October 2022 and January 2023 garnered participants (N=935) that were a mean age of 44.1 (SD=13.9) and 66% women (n=617).FindingsOf 935 participants, 124 reported moderate-to-severe depressive symptoms but were not in treatment, 390 were in treatment and 495 reported absent-to-mild symptoms with or without treatment experiences. The median SWD was a 20% (IQR=10–30%) difference in response rates for people with moderate-to-severe depressive symptoms, not in treatment, and willing to consider antidepressants, and 25% (IQR=10–35%) for the full sample.ConclusionsOur observed SWDs mean that the current 15% antidepressant benefit over no treatment was sufficient for one in three people to accept antidepressants given the burdens, but two in three expected greater treatment benefits.ImplicationsWhile a minority may be satisfied with the best currently available antidepressants, more effective and/or less burdensome medications are needed, with more attention given to patient perspectives.

IntroductionWe have recently compared all second-generation as well as selected first-generation antidepressants in terms of efficacy and acceptability in the acute treatment of major depression. Here we present a protocol for a network meta-analysis aimed at extending these results, updating the evidence base and comparing all second-generation as well as selected first-generation antidepressants in terms of specific adverse events and tolerability in the acute treatment of major depression in adults.Methods and analysisWe will include all double-blind randomised controlled trials comparing one active drug with another or with placebo in the acute treatment major depression in adults. We will compare the following active agents: agomelatine, amitriptyline, bupropion, citalopram, clomipramine, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, milnacipran, mirtazapine, nefazodone, paroxetine, reboxetine, sertraline, trazodone, venlafaxine, vilazodone and vortioxetine. The main outcomes will include the total number of patients experiencing specific adverse events; experiencing serious adverse events; and experiencing at least one adverse event. Published and unpublished studies will be retrieved through relevant database searches, trial registries and websites; reference selection and data extraction will be completed by at least two independent reviewers. For each outcome we will undertake a network meta-analysis to synthesise all evidence. We will use local and global methods to evaluate consistency. We will perform all analyses in R. We will assess the quality of evidence contributing to network estimates with the Confidence in Network Meta-Analysis web application.DiscussionThis work will provide an in- depth analysis and an insight into the specific adverse events of individual antidepressants.Ethics and disseminationThis review does not require ethical approval.PROSPERO registration numberCRD42019128141.

Glycosaminoglycans (GAGs) are essential polysaccharides in normal physiology and disease. However, understanding of the contribution of specific GAG structures to specific biological functions is limited, largely because of the great structural heterogeneity among GAGs themselves, as well as technical limitations in the structural characterization and chemical synthesis of GAGs. Here we describe a cell-based method to produce and display distinct GAGs with a broad repertoire of modifications, a library we refer to as the GAGOme. By using precise gene editing, we engineered a large panel of Chinese hamster ovary cells with knockout or knock-in of the genes encoding most of the enzymes involved in GAG biosynthesis, to generate a library of isogenic cell lines that differentially display distinct GAG features. We show that this library can be used for cell-based binding assays, recombinant expression of proteoglycans with distinct GAG structures, and production of distinct GAG chains on metabolic primers that may be used for the assembly of GAG glycan microarrays.

Psychological treatments are increasingly regarded as useful interventions for schizophrenia. However, a comprehensive evaluation of the available evidence is lacking and the benefit of psychological interventions for patients with current positive symptoms is still debated. The present study aimed to evaluate the efficacy, acceptability and tolerability of psychological treatments for positive symptoms of schizophrenia by applying a network meta‐analysis approach, that can integrate direct and indirect comparisons. We searched EMBASE, MEDLINE, PsycINFO, PubMed, BIOSIS, Cochrane Library, World Health Organization's International Clinical Trials Registry Platform and ClinicalTrials.govfor randomized controlled trials of psychological treatments for positive symptoms of schizophrenia, published up to January 10, 2018. We included studies on adults with a diagnosis of schizophrenia or a related disorder presenting positive symptoms. The primary outcome was change in positive symptoms measured with validated rating scales. We included 53 randomized controlled trials of seven psychological interventions, for a total of 4,068 participants receiving the psychological treatment as add‐on to antipsychotics. On average, patients were moderately ill at baseline. The network meta‐analysis showed that cognitive behavioural therapy (40 studies) reduced positive symptoms more than inactive control (standardized mean difference, SMD=−0.29; 95% CI: –0.55 to −0.03), treatment as usual (SMD=−0.30; 95% CI: –0.45 to −0.14) and supportive therapy (SMD=−0.47; 95% CI: –0.91 to −0.03). Cognitive behavioural therapy was associated with a higher dropout rate compared with treatment as usual (risk ratio, RR=0.74; 95% CI: 0.58 to 0.95). Confidence in the estimates ranged from moderate to very low. The other treatments contributed to the network with a lower number of studies. Results were overall consistent in sensitivity analyses controlling for several factors, including the role of researchers’ allegiance and blinding of outcome assessor. Cognitive behavior therapy seems to be effective on positive symptoms in moderately ill patients with schizophrenia, with effect sizes in the lower to medium range, depending on the control condition.

Missing outcome data can invalidate the results of randomized trials and their meta-analysis. However, addressing missing data is often a challenging issue because it requires untestable assumptions. The impact of missing outcome data on the meta-analysis summary effect can be explored by assuming a relationship between the outcome in the observed and the missing participants via an informative missingness parameter. The informative missingness parameters cannot be estimated from the observed data, but they can be specified, with associated uncertainty, using evidence external to the meta-analysis, such as expert opinion. The use of informative missingness parameters in pairwise meta-analysis of aggregate data with binary outcomes has been previously implemented in Stata by the metamisscommand. In this article, we present the new command metamiss2, which is an extension of metamissfor binary or continuous data in pairwise or network meta-analysis. The command can be used to explore the robustness of results to different assumptions about the missing data via sensitivity analysis.

Acutely occurring, life-threatening side-effects of antipsychotic drugs might contribute to the reduced life expectancy observed in patients with severe mental disorders. We aimed to assess this hypothesis by doing a systematic review and meta-analysis of deaths occurring in placebo-controlled trials of antipsychotic drugs.

Major depressive disorder is one of the most common, burdensome, and costly psychiatric disorders worldwide in adults. Pharmacological and non-pharmacological treatments are available; however, because of inadequate resources, antidepressants are used more frequently than psychological interventions. Prescription of these agents should be informed by the best available evidence. Therefore, we aimed to update and expand our previous work to compare and rank antidepressants for the acute treatment of adults with unipolar major depressive disorder.

Previous studies have reported that effect sizes of antidepressants were larger in two-armed than in three-armed or more-armed (multiarmed) randomized trials, where the probability of being allocated to placebo is lower. However, these studies have not taken into account the publication bias, differences among antidepressants, or covariance in multiarmed studies, or examined sponsorship bias. We searched published and unpublished randomized-controlled trials that compared placebo with 21 antidepressants for the acute treatment of major depression in adults. We calculated the ratio of odds ratios (ROR) of drug response over placebo in two-armed versus multiarmed trials for each antidepressant, and then synthesized RORs across all the included antidepressants using the multivariate meta-analysis. A random-effects model was used throughout. Two hundred and fifty-eight trials (66 two-armed and 192 multiarmed trials; 80 454 patients; 43.0% with unpublished data) were included in the present analyses. The pooled ROR for response of two-armed trials over multiarmed trials was 1.09 (95% confidence interval: 0.96–1.24). The ROR did not materially change between types of antidepressants, publication year, or sponsorship. The differences between two-armed versus multiarmed studies were much smaller than were suggested in previous studies and were not significant.

Results from well-conducted randomised controlled studies should ideally inform on the comparative merits of treatment choices for a health condition. In the absence of this, one attempts to use evidence from the impact of treatment when administered according to decisions of the physicians and the patients (observational evidence). Naive comparisons between treatment options using observational evidence will lead to biased results. Under certain conditions, however, it is possible to obtain valid estimates of the comparative merits of different treatments from observational data. Causal inference can be conceptualised as a framework aiming to provide valid information about causal effects of treatments using observational evidence. It can be viewed as a missing data problem in which each patient has two outcomes: the observed outcome under the treatment actually received and a counterfactual (unobserved) outcome hadthe patient received a different treatment. Methodological developments over the last decades clarified the appropriate conditions and methods to obtain valid comparisons. This article provides an introduction to some of these methods.

ObjectiveSeveral tools have been developed to evaluate the extent to which the findings from a network meta-analysis would be valid; however, applying these tools is a time-consuming task and often requires specific expertise. Clinicians have little time for critical appraisal, and they need to understand the key elements that help them select network meta-analyses that deserve further attention, optimising time and resources. This paper is aimed at providing a practical framework to assess the methodological robustness and reliability of results from network meta-analysis.MethodsAs a working example, we selected a network meta-analysis about drug treatments for generalised anxiety disorder, which was published in 2011 in the British Medical Journal. The same network meta-analysis was previously used to illustrate the potential of this methodology in a methodological paper published in JAMA.ResultsWe reanalysed the 27 studies included in this network following the methods reported in the original article and compared our findings with the published results. We showed how different methodological approaches and the presentation of results can affect conclusions from network meta-analysis. We divided our results into three sections, according to the specific issues that should always be addressed in network meta-analysis: (1) understanding the evidence base, (2) checking the statistical analysis and (3) checking the reporting of findings.ConclusionsThe validity of the results from network meta-analysis depends on the plausibility of the transitivity assumption. The risk of bias introduced by limitations of individual studies must be considered first and judgement should be used to infer about the plausibility of transitivity. Inconsistency exists when treatment effects from direct and indirect evidence are in disagreement. Unlike transitivity, inconsistency can be always evaluated statistically, and it should be specifically investigated and reported in the published paper. Network meta-analysis allows researchers to list treatments in preferential order; however, in this paper we demonstrated that rankings could be misleading if based on the probability of being the best. Clinicians should always be interested in the effect sizes rather than the naive rankings.

Previous studies have shown that placebo response rates in antidepressant trials have been increasing since the 1970s. However, these studies have been based on outdated or limited datasets and have used inappropriate statistical methods. We did a systematic review of placebo-controlled randomised controlled trials of antidepressants to examine associations between placebo-response rates and study and patient characteristics.

In clinical oncology, diagnosis and evaluation of optimal treatment strategies are mostly based on histopathological examination combined with immunohistochemical (IHC) expression analysis of cancer-associated antigens in formalin fixed paraffin-embedded (FFPE) tissue biopsies. However, informative IHC analysis depends on both the specificity and affinity of the binding reagent, which are inherently difficult to quantify in situ. Here we describe a label-free method that allows for the direct and real-time assessment of molecular binding kinetics in situon FFPE tissue specimens using quartz crystal microbalance (QCM) enabled biosensor technology. We analysed the interaction between the rVAR2 protein and its placental-like chondroitin sulfate (pl-CS) receptor in primary human placenta tissue and in breast and prostate tumour specimens in situ. rVAR2 interacted with FFPE human placenta and cancer tissue with an affinity in the nanomolar range, and showed no detectable interaction with pl-CS negative normal tissue. We further validated the method by including analysis with the androgen receptor N-20 antibody (anti-AR). As the KDvalue produced by this method is independent of the number of epitopes available, this readout offers a quantitative and unbiased readout for in situbinding-avidity and amount of binding epitopes. In summary, this method adds a new and important dimension to classical IHC-based molecular pathology by adding information about the binding characteristics in biologically relevant conditions. This can potentially be used to select optimal biologics for diagnostic and for therapeutic applications as well as guide the development of novel high affinity binding drugs.

In anxiety, depression and psychosis, there has been frustratingly slow progress in developing novel therapies that make a substantial difference in practice, as well as in predicting which treatments will work for whom and in what contexts. To intervene early in the process and deliver optimal care to patients, we need to understand the underlying mechanisms of mental health conditions, develop safe and effective interventions that target these mechanisms, and improve our capabilities in timely diagnosis and reliable prediction of symptom trajectories. Better synthesis of existing evidence is one way to reduce waste and improve efficiency in research towards these ends. Living systematic reviews produce rigorous, up-to-date and informative evidence summaries that are particularly important where research is emerging rapidly, current evidence is uncertain and new findings might change policy or practice. Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) aims to tackle the challenges of mental health science research by cataloguing and evaluating the full spectrum of relevant scientific research including both human and preclinical studies. GALENOS will also allow the mental health community—including patients, carers, clinicians, researchers and funders—to better identify the research questions that most urgently need to be answered. By creating open-access datasets and outputs in a state-of-the-art online resource, GALENOS will help identify promising signals early in the research process. This will accelerate translation from discovery science into effective new interventions for anxiety, depression and psychosis, ready to be translated in clinical practice across the world.

ObjectiveWhen developing prediction models, researchers commonly employ a single model which uses all the available data (end-to-endapproach). Alternatively, a similarity-basedapproach has been previously proposed, in which patients with similar clinical characteristics are first grouped into clusters, then prediction models are developed within each cluster. The potential advantage of the similarity-based approach is that it may better address heterogeneity in patient characteristics. However, it remains unclear whether it improves the overall predictive performance. We illustrate the similarity-based approach using data from people with depression and empirically compare its performance with the end-to-end approach.MethodsWe used primary care data collected in general practices in the UK. Using 31 predefined baseline variables, we aimed to predict the severity of depressive symptoms, measured by Patient Health Questionnaire-9, 60 days after initiation of antidepressant treatment. Following the similarity-based approach, we used k-means to cluster patients based on their baseline characteristics. We derived the optimal number of clusters using the Silhouette coefficient. We used ridge regression to build prediction models in both approaches. To compare the models’ performance, we calculated the mean absolute error (MAE) and the coefficient of determination (R2) using bootstrapping.ResultsWe analysed data from 16 384 patients. The end-to-end approach resulted in an MAE of 4.64 and R2of 0.20. The best-performing similarity-based model was for four clusters, with MAE of 4.65 and R2of 0.19.ConclusionsThe end-to-end and the similarity-based model yielded comparable performance. Due to its simplicity, the end-to-end approach can be favoured when using demographic and clinical data to build prediction models on pharmacological treatments for depression.

ObjectiveThere is no standard tool for assessing risk of bias (RoB) in prevalence studies. For the purposes of a living systematic review during the COVID-19 pandemic, we developed a tool to evaluate RoB in studies measuring the prevalence of mental health disorders (RoB-PrevMH) and tested inter-rater reliability.MethodsWe decided on items and signalling questions to include in RoB-PrevMH through iterative discussions. We tested the reliability of assessments by different users with two sets of prevalence studies. The first set included a random sample of 50 studies from our living systematic review. The second set included 33 studies from a systematic review of the prevalence of post-traumatic stress disorders, major depression and generalised anxiety disorder. We assessed the inter-rater agreement by calculating the proportion of agreement and Kappa statistic for each item.ResultsRoB-PrevMH consists of three items that address selection bias and information bias. Introductory and signalling questions guide the application of the tool to the review question. The inter-rater agreement for the three items was 83%, 90% and 93%. The weighted kappa scores were 0.63 (95% CI 0.54 to 0.73), 0.71 (95% CI 0.67 to 0.85) and 0.32 (95% CI −0.04 to 0.63), respectively.ConclusionsRoB-PrevMH is a brief, user-friendly and adaptable tool for assessing RoB in studies on prevalence of mental health disorders. Initial results for inter-rater agreement were fair to substantial. The tool’s validity, reliability and applicability should be assessed in future projects.

Network meta-analysis has been established in recent years as a particularly useful evidence synthesis tool. However, it is still challenging to develop understandable and concise ways to present data, assumptions, and results from network meta-analysis to inform decision making and evaluate the credibility of the results. In this article, we provide a suite of commands with graphical tools to facilitate the understanding of data, the evaluation of assumptions, and the interpretation of findings from network meta-analysis.

Percutaneous coronary intervention (PCI) with drug-eluting stents is the standard of care for treatment of native coronary artery stenoses, but optimum treatment strategies for bare metal stent and drug-eluting stent in-stent restenosis (ISR) have not been established. We aimed to compare and rank percutaneous treatment strategies for ISR.

The comparative efficacy and safety of pharmacological agents to lower blood pressure in adults with diabetes and kidney disease remains controversial. We aimed to investigate the benefits and harms of blood pressure-lowering drugs in this population of patients.