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The current definition of prediabetes is controversial and subject to continuous debate. Nonetheless, prediabetes is a risk factor for type 2 diabetes, is highly prevalent and is associated with diabetic complications and mortality. Thereby, it has the potential to become a huge strain on healthcare systems in the future, necessitating action from legislators and healthcare providers. But how do we best reduce its associated burden on health? As a compromise between differing opinions in the literature and among the authors of this article, we suggest stratifying individuals with prediabetes according to estimated risk and only offering individual-level preventive interventions to those at high risk. At the same time, we argue to identify those with prediabetes and already established diabetes-related complications and treat them as we would treat individuals with established type 2 diabetes. Graphical abstract

Peer reviewed: True, BACKGROUND: The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention. METHODS: We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology. FINDINGS: We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse. CONCLUSIONS: Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

A stratified medicine approach for the treatment of psoriasis promises greater certainty of clinical decision making through prediction of response on the basis of clinical, pharmacological, and -omics data from an individual patient. As yet, there is no predictive model for treatment response in routine clinical use for psoriasis. The Psoriasis Stratification to Optimise Relevant Therapy (PSORT) Consortium is a United Kingdom Medical Research Council‒funded, academic‒industrial stratified medicine consortium established with the objective of discovering the predictors and stratifiers of response of psoriasis to biologic therapies. A showcase meeting was convened and attended by 80 stakeholders at the Royal College of Physicians, London, United Kingdom on 18 November 2019. The purpose was to disseminate the research findings from the PSORT consortium discovered thus far. This report summarizes the presentations made on the day and the significant advances made by PSORT toward a stratified medicine approach to the management of psoriasis.

One of the targets of personalized medicine is to provide treatment recommendations using patient characteristics. We present the command ptr, which both predicts a personalized treatment recommendation algorithm and evaluates its effectiveness versus an alternative regime, using randomized trial data. The command allows for multiple (continuous or categorical) biomarkers and a binary or continuous outcome. Confidence intervals for the evaluation parameter are provided using bootstrap resampling.

Background In the presence of heterogeneous treatment effects, it is desirable to divide patients into subgroups based on their expected response to treatment. This is formalised via a personalised treatment recommendation: an algorithm that uses biomarker measurements to select treatments. It could be that multiple, rather than single, biomarkers better predict these subgroups. However, finding the optimal combination of multiple biomarkers can be a difficult prediction problem. Methods We described three parametric methods for finding the optimal combination of biomarkers in a personalised treatment recommendation, using randomised trial data: a regression approach that models outcome using treatment by biomarker interactions; an approach proposed by Kraemer that forms a combined measure from individual biomarker weights, calculated on all treated and control pairs; and a novel modification of Kraemer’s approach that utilises a prognostic score to sample matched treated and control subjects. Using Monte Carlo simulations under multiple data-generating models, we compare these approaches and draw conclusions based on a measure of improvement under a personalised treatment recommendation compared to a standard treatment. The three methods are applied to data from a randomised trial of home-delivered pragmatic rehabilitation versus treatment as usual for patients with chronic fatigue syndrome (the FINE trial). Prior analysis of this data indicated some treatment effect heterogeneity from multiple, correlated biomarkers. Results The regression approach outperformed Kraemer’s approach across all data-generating scenarios. The modification of Kraemer’s approach leads to improved treatment recommendations, except in the case where there was a strong unobserved prognostic biomarker. In the FINE example, the regression method indicated a weak improvement under its personalised treatment recommendation algorithm. Conclusions The method proposed by Kraemer does not perform better than a regression approach for combining multiple biomarkers. All methods are sensitive to misspecification of the parametric models.

IntroductionPrimary Sjögren’s Syndrome (PSS) is a chronic disease characterised by symptoms of oral and ocular dryness, pain, fatigue, anxiety and depression. PSS patients can be subclassified by the pattern of severity of these five key symptoms using the Newcastle Sjögren’s Stratification Tool (NSST). Although PSS is often associated with one or more comorbidities, the relationship between comorbidities, polypharmacy, and PSS symptom burden is unclear. Using data from the UK Primary Sjögren’s Syndrome Registry (UKPSSR) we describe the landscape of polypharmacy and comorbidities in PSS.MethodsThe UKPSSR is research biobank of clinically well-defined PSS patients where clinical, demographic, comorbidities and concomitant medications data are recorded. Patients were subclassified into the four NSST subgroups: Low Symptom Burden (LSB), High Symptom Burden (HSB), Dryness Dominated Fatigue (DDF) and Pain Dominated Fatigue (PDF). Group analyses of comorbid conditions and polypharmacy scores were performed. Comorbidity and Polypharmacy Scores (CPS) were modelled as a function of age, sex, symptom duration, body mass index (BMI), current immunosuppressant and hydroxychloroquine prescriptions and NSST subgroup.ResultsThere were marked differences in the number and the nature of comorbidities associated with the NSST subgroups. LSB and DDF patients were characterized by fewer comorbidities and medications. In contrast, HSB and PDF patients were associated with more comorbidities and were more likely to be prescribed multiple medications. Group analysis shows that HSB patients are more closely associated with peripheral vascular disease and infection whereas the PDF patients were associated with cardiovascular disease and gastrointestinal comorbidities. Comorbidity and polypharmacy scores increase with age and BMI regardless of symptom subgroup and symptom duration. In addition, the longer the reported symptom duration the higher the associated comorbidities and polypharmacy scores.ConclusionComorbid conditions are more prevalent in some subgroups of the PSS cohort but increase with age and BMI across the entire cohort. It is unclear from these data whether specific comorbid conditions are a consequence of PSS or represent shared aetiology or pathogenetic susceptibility. Regardless, these findings may have implications for disease management and clinical trial design.

SUMMARYStratified medicine has been successfully used in many areas of medicine, perhaps most notably oncology. There is now both a growing evidence base and mounting enthusiasm, supported at a governmental level and across industry, academia and clinical medicine, to apply this approach to neurodegenerative illnesses, including dementia, as these provide the greatest clinical and social challenge of our times. In this article we consider definitions of stratified medicine, look at its application in other medical specialties, review the national context in the UK and consider the current state, future potential and specific considerations of applying stratified medicine to dementia.

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two-stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous.

Highlights • Peripheral blood leucocyte ratios are useful infection biomarkers. • Neutrophil:lymphocyte ratio (NLR) is predictive of bacteraemia. • NLR should be included in future stratification models to identify sepsis endotypes. • Lymphocyte:monocyte ratio is predictive of influenza virus infection. • Utility was also found in CAP, UTI, pertussis, CCHF and diabetic foot infections., Summary Objectives To assess the utility of the neutrophil:lymphocyte (NLR), lymphocyte:monocyte (LMR) and platelet:lymphocyte ratios (PLR) as infection biomarkers. Methods PubMed/MEDLINE, Embase and Cochrane databases were searched to identify eligible articles. Studies of diagnosis, severity or outcome were included. PROSPERO systematic review registration CRD42017075032. Results Forty studies were included, reporting on bacterial and viral infections, malaria, and critical illness due to sepsis. Ten studies reported an association of higher NLR with bacteraemia, supported by meta-analysis of patient-level data (five studies, n = 3320; AUC 0.72, p12.65. Two studies reported an association with lower LMR and diagnosis of influenza virus infection in patients with respiratory tract infection. Meta-analysis of patient-level data (n = 85; AUC 0.66, p = 0.01) identified a cut-off of ≤2.06. The directionality of associations between NLR and outcomes in heterogeneous cohorts of critically ill adults with sepsis varied. Potential clinical utility was also demonstrated in pneumonia (NLR), pertussis (NLR), urinary tract infection (NLR), diabetic foot infections (NLR) and Crimean Congo Haemorrhagic Fever (PLR). Longitudinal measurement of LMR during respiratory virus infection reflected symptoms and NLR during sepsis and bacteraemia predicted mortality. Conclusions Peripheral blood leucocyte ratios are useful infection biomarkers, with the most evidence related to diagnosis of bacteraemia and influenza virus infection. In critical illness due to sepsis, a signal towards an association with NLR and outcomes exists, and NLR should be evaluated in future stratification models. Longitudinal measurement of ratios during infection could be informative. Overall, these biomarkers warrant further recognition and study in infectious diseases.

Objectives The aim of this study was to test the hypothesis that invasive coronary function testing at time of angiography could help stratify management of angina patients without obstructive coronary artery disease. Background Medical therapy for angina guided by invasive coronary vascular function testing holds promise, but the longer-term effects on quality of life and clinical events are unknown among patients without obstructive disease. Methods A total of 151 patients with angina with symptoms and/or signs of ischemia and no obstructive coronary artery disease were randomized to stratified medical therapy guided by an interventional diagnostic procedure versus standard care (control group with blinded interventional diagnostic procedure results). The interventional diagnostic procedure–facilitated diagnosis (microvascular angina, vasospastic angina, both, or neither) was linked to guideline-based management. Pre-specified endpoints included 1-year patient-reported outcome measures (Seattle Angina Questionnaire, quality of life [EQ-5D]) and major adverse cardiac events (all-cause mortality, myocardial infarction, unstable angina hospitalization or revascularization, heart failure hospitalization, and cerebrovascular event) at subsequent follow-up. Results Between November 2016 and December 2017, 151 patients with ischemia and no obstructive coronary artery disease were randomized (n = 75 to the intervention group, n = 76 to the control group). At 1 year, overall angina (Seattle Angina Questionnaire summary score) improved in the intervention group by 27% (difference 13.6 units; 95% confidence interval: 7.3 to 19.9; p, Central Illustration

Personalizing treatment according to patient's characteristics is at the core of stratified or precision medicine. There has been a recent surge of statistical methods aiming at identifying so-called optimal treatment strategies, i.e., strategies that assign a given treatment to a patient according to his/her characteristics. However, when data from a randomized controlled trial are used to estimate the optimal treatment strategy, it is not straightforward to estimate and test the benefit of the estimated strategy as compared to not personalizing treatment. In this context, we propose a principled approach for the estimation of the benefit of an estimated treatment strategy, accounting for its uncertainty. This leads to formalizing a strategy that we term the max lower bound strategy. Numerical simulations are used to show it allows proper type I error rate control and coverage probabilities. The approach is extended to multiple covariates using machine learning techniques. It is then applied to the data of a randomized trial in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis.

Rationale: No evidence-based tools exist to enhance precision in the selection of patient-specific optimal treatment durations to study in tuberculosis clinical trials. Objectives: To develop risk stratification tools that assign patients with tuberculosis into risk groups of unfavorable outcome and inform selection of optimal treatment duration for each patient strata to study in clinical trials. Methods: Publicly available data from four phase 3 trials, each evaluating treatment duration shortening from 6 to 4 months, were used to develop parametric time-to-event models that describe unfavorable outcomes. Regimen, baseline, and on-treatment characteristics were evaluated as predictors of outcomes. Exact regression coefficients of predictors were used to assign risk groups and predict optimal treatment durations. Measurements and Main Results: The parametric model had an area under the receiver operating characteristic curve of 0.72. A six-item risk score (HIV status, smear grade, sex, cavitary disease status, body mass index, and Month 2 culture status) successfully grouped participants into low (1,060/3,791; 28%), moderate (1,740/3,791; 46%), and high (991/3,791; 26%) risk, requiring treatment durations of 4, 6, and greater than 6 months, respectively, to reach a target cure rate of 93% when receiving standard-dose rifamycin-containing regimens. With current one-duration-fits-all approaches, high-risk groups have a 3.7-fold (95% confidence interval, 2.7-5.1) and 2.4-fold (1.9-2.9) higher hazard risk of unfavorable outcomes compared with low- and moderate-risk groups, respectively. Four-month regimens were noninferior to the standard 6-month regimen in the low-risk group. Conclusions: Our model discrimination was modest but consistent with current models of unfavorable outcomes. Our results showed that stratified medicine approaches are feasible and may achieve high cure rates in all patients with tuberculosis. An interactive risk stratification tool is provided to facilitate decision-making in the regimen development pathway.

Rationale: No evidence-based tools exist to enhance precision in the selection of patient-specific optimal treatment durations to study in tuberculosis clinical trials. Objectives: To develop risk stratification tools that assign patients with tuberculosis into risk groups of unfavorable outcome and inform selection of optimal treatment duration for each patient strata to study in clinical trials. Methods: Publicly available data from four phase 3 trials, each evaluating treatment duration shortening from 6 to 4 months, were used to develop parametric time-to-event models that describe unfavorable outcomes. Regimen, baseline, and on-treatment characteristics were evaluated as predictors of outcomes. Exact regression coefficients of predictors were used to assign risk groups and predict optimal treatment durations. Measurements and Main Results: The parametric model had an area under the receiver operating characteristic curve of 0.72. A six-item risk score (HIV status, smear grade, sex, cavitary disease status, body mass index, and Month 2 culture status) successfully grouped participants into low (1,060/3,791; 28%), moderate (1,740/3,791; 46%), and high (991/3,791; 26%) risk, requiring treatment durations of 4, 6, and greater than 6 months, respectively, to reach a target cure rate of 93% when receiving standard-dose rifamycin-containing regimens. With current one-duration-fits-all approaches, high-risk groups have a 3.7-fold (95% confidence interval, 2.7–5.1) and 2.4-fold (1.9–2.9) higher hazard risk of unfavorable outcomes compared with low- and moderate-risk groups, respectively. Four-month regimens were noninferior to the standard 6-month regimen in the low-risk group. Conclusions: Our model discrimination was modest but consistent with current models of unfavorable outcomes. Our results showed that stratified medicine approaches are feasible and may achieve high cure rates in all patients with tuberculosis. An interactive risk stratification tool is provided to facilitate decision-making in the regimen development pathway.

Improved outcomes for acutely unwell patients are predicated on early identification of deterioration, accelerating the time to accurate diagnosis of the underlying condition, selection and titration of treatments that target biological phenotypes, and personalised endpoints to achieve optimal benefit yet minimise iatrogenic harm. Technological developments entering routine clinical practice over the next decade will deliver a sea change in patient management. Enhanced point of care diagnostics, more sophisticated physiological and biochemical monitoring with superior analytics and computer-aided support tools will all add considerable artificial intelligence to complement clinical skills. Experts in different fields of emergency and critical care medicine offer their perspectives as to which research developments could make a big difference within the next decade.

The 2017 American College of Cardiology/American Heart Association hypertension guidelines lowered the thresholds for defining and treating hypertension. However, the SPRINT trial showed substantial heterogeneity in benefits and harms of intensive antihypertensive treatment depending on patients’ characteristics. We aimed at illustrating the potential gains of personalizing intensive antihypertensive treatment. Using the US National Health and Nutrition Examination Survey 2011 to 2014 (n=2067), and prediction models derived from the SPRINT trial, we computed expected benefits and harms of intensive antihypertensive treatment for individuals aged 50 or more. We compared 2 interventions: (1) intensive antihypertensive treatment for all individuals meeting the 2017 American College of Cardiology/American Heart Association thresholds and (2) a stratified medicine strategy excluding from intensive treatment individuals with predicted unfavorable benefit-risk. Outcome measures were model-predicted 5-year risk of cardiovascular events or death (myocardial infarction, acute coronary, stroke, acute decompensated heart failure, and cardiovascular-related death), and severe adverse events (hypotension, syncope, electrolyte abnormalities, bradycardia, and acute kidney injury). Per 2017 American College of Cardiology/American Heart Association guidelines, 40.1 million (39.2%) US individuals aged 50 or more should initiate or intensify antihypertensive treatment, thereby preventing cardiovascular events for 795 000 individuals and inducing severe adverse events for 848 000 over 5 years. A stratified treatment strategy could decrease the number of individuals treated by 21.2 million (52.9%) and reduce the number of individuals with severe adverse events by 38.3%, with 11.7% fewer individuals with cardiovascular events prevented. Personalizing antihypertensive treatment according to predicted benefits and harms could spare treatment for more than half individuals while reducing harms 3× more than benefits.

Background: Stratified medicine methodologies based on subgroup analyses are often insufficiently powered. More powerful personalized medicine approaches are based on continuous scores. Objective: We deployed a patient-specific continuous score predicting treatment response in patients with relapsing-remitting multiple sclerosis (RRMS). Methods: Data from two independent randomized controlled trials (RCTs) were used to build and validate an individual treatment response (ITR) score, regressing annualized relapse rates (ARRs) on a set of baseline predictors. Results: The ITR score for the combined treatment groups versus placebo detected differential clinical response in both RCTs. High responders in one RCT had a cross-validated ARR ratio of 0.29 (95% confidence interval (CI) = 0.13–0.55) versus 0.62 (95% CI = 0.47–0.83) for all other responders (heterogeneity p = 0.038) and were validated in the other RCT, with the corresponding ARR ratios of 0.31 (95% CI = 0.18–0.56) and 0.61 (95% CI = 0.47–0.79; heterogeneity p = 0.036). The strongest treatment effect modifiers were the Short Form-36 Physical Component Summary, age, Visual Function Test 2.5%, prior MS treatment and Expanded Disability Status Scale. Conclusion: Our modelling strategy detects and validates an ITR score and opens up avenues for building treatment response calculators that are also applicable in routine clinical practice.

Background Trials have become bigger and more complicated due to the complexity introduced by biomarker stratification, and the advent of multi-arm multi-stage trials, and umbrella and basket platform designs. The trials unit at University College London has been at the forefront of this work, with ground-breaking trials such as STAMPEDE and FOCUS4. The trial management and data management teams on these trials have summarised the operational challenges, to enable the broader clinical trials community to learn from their experiences. In a small-scale qualitative study, we examined the personal experience of individual researchers working on these trials. Commentary We found reports of high workloads, with potentially significant stress for individuals and with an impact on their career choices. We conclude that there was an initial underestimation of the work required and of the inherent, largely unanticipated, challenges. We discuss the importance of fully understanding these trials’ resource requirements, both for those writing grant applications and critically, for those with responsibility for deciding on funding. The working environment was characterised by three features: complexity, scale and heightened expectations. These features are highly attractive for professional development and engender high levels of loyalty and commitment. We observed a trade-off between these intrinsic rewards and the continuous demands of overlapping tasks, balancing a mix of routine and high-profile work, and the changing nature of pivotal roles. Such demands present challenges for colleague relationships, by enhancing the potential for competition and by disrupting the natural opportunities to pause, review and celebrate team achievements. In addition, molecular stratification in effect brings the patient into the trial office, as a specific individual, despite anonymisation, who is owed test results and a treatment decision. We discuss these observations with a view to interconnecting the need for compassion for patients with caring for the researchers engaged in the research ecosystem who are aiming to produce much hoped-for advances in medical science. Conclusions There is a need for increased awareness of the challenge these studies place on those throughout the team delivering the study. Such considerations must influence leaders and funders, both in their initial budget considerations and throughout delivery.

In the modern era of cancer treatment, with targeted agents superseding more traditional cytotoxic chemotherapeutics, it is becoming increasingly important to use stratified medicine approaches to ensure that patients receive the most appropriate drugs and treatment schedules. In this context, there is significant potential for the use of pharmacodynamic biomarkers to provide pharmacological information, which could be used in a therapeutic drug monitoring setting. This review focuses on discussing some of the challenges faced to date in translating preclinical pharmacodynamic biomarker approaches to a clinical setting. Recent advances in important areas including circulating biomarkers and pharmacokinetic/pharmacodynamic modeling approaches are discussed, and selected examples of anticancer drugs where there is existing evidence to potentially advance pharmacodynamic therapeutic drug monitoring approaches to deliver more effective treatment are discussed. Although we may not yet be in a position to systematically implement therapeutic drug monitoring approaches based on pharmacodynamic information in a cancer patient setting, such approaches are likely to become more commonplace in the coming years. Based on ever-increasing levels of pharmacodynamic information being generated on newer anticancer drugs, facilitated by increasingly advanced and accessible experimental approaches available to researchers to collect these data, we can now look forward optimistically to significant advances being made in this area.

Despite huge advances in understanding the molecular basis of IBD, clinical management has continued to rely on a "trial and error" approach. In addition, a therapeutic ceiling has emerged whereby even the most effective interventions are only beneficial for approximately 30% of patients. Consequently, several tools have been developed to aid stratification and guide treatment-decisions. We review the potential application for many of these precision medicine approaches, which are now almost within reach. We highlight the importance of early action (and avoiding inaction) to ensure the best outcomes for patients and how combining early action with precision tools will likely ensure the right treatment is delivered at the right time and place for each individual person living with IBD. The lack of clinical impact to date from precision medicine, despite much hype and investment, should be tempered with the knowledge that clinical translation can take a long time, and many promising breakthroughs might be ready for clinical implementation in the near future. We discuss some of the remaining challenges and barriers to overcome for clinical adoption. We also highlight that early recognition, early diagnosis, early stratification, and early intervention go hand in hand with precision medicine tools. It is the combination of these approaches that offer the greatest opportunity to finally deliver on the promise of precision medicine in IBD.Several precision medicine tools to guide treatment decisions are almost ready for clinical implementation. We highlight that combining these tools with a focus on early action offers the greatest opportunity to finally deliver on the promise of precision medicine in IBD.

The aim of this study was to compare the relationship between two health outcomes (pain and self-reported health) and personality while accounting for heterogeneity in arthritic disease. Traditionally health psychology and other health research has treated patients’ disease experiences as homogeneous but stratified medicine suggests that treating a disease as homogenous might over-generalise findings and miss important effects. We present a longitudinal analysis over 14 years, on a subsample of 443 arthritic respondents from the English Longitudinal Study of Ageing (ELSA). Using linear regressions, we modelled how the Big Five domains of personality (wave 5) moderated the relationship between past health (at wave 1) and present health (at wave 7). Then, to model heterogeneity in arthritis experience we included assignment to 4 different sub-groups based on their experience of pain progression. The results showed that modelling heterogeneity led to the identification of specific stratified effects for personality (neuroticism, agreeableness, and extraversion) not observed when these data are model treating the sample as homogenous. For example, higher agreeableness was associated with worse pain for those in a sub-group reporting the greatest pain, and higher extraversion was protective against pain among those whose pain improved. The results highlight the importance of modelling heterogeneity of disease.

Background Health services have advocated a stratified medicine approach in mental health, but little is known about whether service users would accept this approach. Aims To explore service users’ views of the acceptability of stratified medicine for treatment-resistant schizophrenia compared to the traditional “trial-and-error” approach. Methods A mixed methods observational study that explored questionnaire responses on acceptability and whether these responses were affected by demographic or clinical variables. We also investigated whether treatment responsiveness or experience of invasive tests (brain scans and blood tests) affected participants’ responses. Questionnaire generated qualitative data were analyzed thematically. Participants (N108) were aged 18–65, had a diagnosis of schizophrenia, and were adherent to antipsychotic medication. Results Acceptability of a stratified approach was high, even after participants had experienced invasive tests. Most rated it as safer (62% vs 43%; P < .01 [CI: −1.69 to 2.08]), less risky (77% vs 44%; P < .01 [CI: −1.75 to 1.10]), and less painful (90% vs 73%; P < 0.01 [CI: −0.84 to 0.5]) and this was not affected by treatment responsiveness or test experience. Although not statistically significant, treatment nonresponders were more willing to undergo invasive tests. Qualitatively, all participants raised concerns about the risks, discomfort, and potential side effects associated with the invasive tests. Conclusions Service users were positive about a stratified approach for choosing treatments but were wary of devolving clinical decisions to purely data-driven algorithms. These results reinforce the value of service user perspectives in the development and evaluation of novel treatment approaches.

Aim Coronary angiography is indicated in many patients with known or suspected angina for the investigation of coronary artery disease (CAD). However, up to half of patients with symptoms of ischaemia have no obstructive coronary arteries (INOCA). This large subgroup includes patients with suspected microvascular angina (MVA) and/or vasospastic angina (VSA). Clinical guidelines relating to the management of patients with INOCA are limited. Uncertainty regarding the diagnosis of patients with INOCA presents a health economic challenge, both in terms of healthcare resource utilisation and of quality-of-life impact on patients. Methods A cost-effectiveness analysis of the introduction of stratified medicine into the invasive management of INOCA, based on clinical and resource-use data obtained in the CorMicA trial, from a UK NHS perspective. The intervention included an invasive diagnostic procedure (IDP) of coronary vascular function during coronary angiography to define clinical endotypes to target with linked medical therapy. Outcomes of interest were mean total cost and QALY gain between treatment groups, and the incremental cost-effectiveness ratio. We undertook probabilistic sensitivity and scenario analyses. Results The incremental cost per QALY gained at 12 months was £4500 (£2937, £33264). Compared with a willingness-to-pay (WTP) threshold of £20,000 per QALY, the use of the IDP test is cost-effective. At this WTP threshold there is a 96% probability of the IDP being cost-effective, based on the uncertainty described by bootstrap analysis. Conclusions The burden of INOCA, particularly in women, is known to be significant. These findings provided new evidence to inform this unmet clinical need.

Purpose of ReviewIn acute ST-segment elevation myocardial infarction (STEMI), successful restoration of blood flow in the infarct-related coronary artery may not secure effective myocardial reperfusion. The mortality and morbidity associated with acute MI remain significant. Microvascular obstruction (MVO) represents failed microvascular reperfusion. MVO is under-recognized, independently associated with adverse cardiac prognosis and represents an unmet therapeutic need.Recent FindingsMultiple factors including clinical presentation, patient characteristics, biochemical markers, and imaging parameters are associated with MVO after MI.SummaryImpaired microvascular reperfusion is common following percutaneous coronary intervention (PCI). New knowledge about disease mechanisms underpins precision medicine with individualized risk assessment, investigation, and stratified therapy. To date, there are no evidence-based therapies to prevent or treat MVO post-MI. Identifying novel therapy for MVO is the next frontier.

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies—such as genomics, transcriptomics, proteomics and metabolomics—are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.

Introduction Cilostazol may be a novel therapeutic agent for Alzheimer's disease. Its metabolite, OPC‐13015, has a stronger inhibitory effect on type 3 phosphodiesterase than cilostazol. Methods We prospectively enrolled patients with mild cognitive impairment to whom cilostazol was newly prescribed. Patients underwent the Montreal Cognitive Assessment (MoCA) twice, at a 6‐month interval. Plasma cilostazol, OPC‐13015, OPC‐13213, and OPC‐13217 concentrations were determined using liquid chromatography‐tandem mass spectrometry. Results MoCA score changes from baseline to the 6‐month visit were positively correlated with ratios of OPC‐13015 to cilostazol and total metabolites (n = 19, P = .005). Patients with higher ratios of OPC‐13015 (≥0.18, median value; n = 10) had significantly higher MoCA scores (P = .036) than patients with lower ratios (the ratio

Background: Stratified medicine may enable the development of effective treatments for particular groups of patients with heart failure with preserved ejection fraction (HFpEF); however, the heterogeneity of this syndrome makes it difficult to group patients together by common disease features. The aim of the present study was to find new subgroups of HFpEF using machine learning.Methods: K-means clustering was used to stratify patients with HFpEF. We retrospectively enrolled 350 outpatients with HFpEF. Their clinical characteristics, blood sample test results and hemodynamic parameters assessed by echocardiography, electrocardiography and jugular venous pulse, and clinical outcomes were applied to k-means clustering. The optimal k was detected using Hartigan's rule.Results: HFpEF was stratified into four groups. The characteristic feature in group 1 was left ventricular relaxation abnormality. Compared with group 1, patients in groups 2, 3, and 4 had a high mean mitral E/e′ ratio. The estimated glomerular filtration rate was lower in group 2 than in group 3 (median 51 ml/min/1.73 m2 vs. 63 ml/min/1.73 m2p < 0.05). The prevalence of less-distensible right ventricle and atrial fibrillation was higher, and the deceleration time of mitral inflow was shorter in group 3 than in group 2 (93 vs. 22% p < 0.05, 95 vs. 1% p < 0.05, and median 167 vs. 223 ms p < 0.05, respectively). Group 4 was characterized by older age (median 85 years) and had a high systolic pulmonary arterial pressure (median 37 mmHg), less-distensible right ventricle (89%) and renal dysfunction (median 54 ml/min/1.73 m2). Compared with group 1, group 4 exhibited the highest risk of the cardiac events (hazard ratio [HR]: 19; 95% confidence interval [CI] 8.9–41); group 2 and 3 demonstrated similar rates of cardiac events (group 2 HR: 5.1; 95% CI 2.2–12; group 3 HR: 3.7; 95%CI, 1.3–10). The event-free rates were the lowest in group 4 (p for trend < 0.001).Conclusions: K-means clustering divided HFpEF into 4 groups. Older patients with HFpEF may suffer from complication of RV afterload mismatch and renal dysfunction. Our study may be useful for stratified medicine for HFpEF.

Each year, the Heart editorial team selects the most meritorious research paper to receive the Heart Best Research Paper Award. Primary considerations are the relevance of the clinical question addressed by the research, the quality of the research study design and data presentation, and the interest generated by the paper among other researchers, clinical cardiologists and the general public. This year, we are pleased to announce the winner and two finalists for the 2020 Heart Best Paper Research Award, highlighting the first author of each paper and sincerely congratulating the entire research team on their outstanding research contribution (table 1). View this table: Table 1 Heart best research paper Award winner and Finalists 2020 Ralph K. Akyea MPH, MBChB (figure 1) is a second-year doctoral student and part-time research associate with the Primary Care Stratified Medicine Research Group, Division of Primary Care, University of Nottingham, UK. He received his medical degree from the University of Ghana Medical School and earned his master’s degree in public health from …

Personalized, stratified, or precision medicine (PM) introduces a new era in healthcare that tries to identify and predict optimum treatment outcomes for a patient or a cohort. It also introduces new scientific terminologies regarding therapeutic approaches and the need of their adoption from healthcare providers. Till today, evidence-based practice (EBP) was focusing on population averages and their variances among cohorts for clinical values that are essential for optimizing healthcare outcome. It can be stated that EBP and PM are complementary approaches for a modern healthcare system. Healthcare providers through EBP often see the forest (population averages) but miss the trees (individual patients), whereas utilization of PM may not see the forest for the trees. Nursing personnel (NP) play an important role in modern healthcare since they are consulting, educating, and providing care to patients whose needs often needs to be individualized (personalized nursing care, PNC). Based on the clinical issues earlier addressed from clinical pharmacology, EBP, and now encompassed in PM, this review tries to describe the challenges that NP have to face in order to meet the requisites of the new era in healthcare. It presents the demands that should be met for upgrading the provided education and expertise of NP toward an updated role in a modern healthcare system.

Background Coronary microvascular dysfunction (CMD) is defined by diminished flow reserve. Functional and structural CMD endotypes have recently been described, with normal and elevated minimal microvascular resistance, respectively. Objectives This study determined the mechanism of altered resting and maximal flow in CMD endotypes. Methods A total of 86 patients with angina but no obstructive coronary disease underwent coronary pressure and flow measurement during rest, exercise, and adenosine-mediated hyperemia and were classified as the reference group or as patients with CMD by a coronary flow reserve threshold of 2.5; functional or structural endotypes were distinguished by a hyperemic microvascular resistance threshold of 2.5 mm Hg/cm/s. Endothelial function was assessed by forearm blood flow (FBF) response to acetylcholine, and nitric oxide synthase (NOS) activity was defined as the inverse of FBF reserve to NG-monomethyl-L-arginine. Results Of the 86 patients, 46 had CMD (28 functional, 18 structural), and 40 patients formed the reference group. Resting coronary blood flow (CBF) (24.6 ± 2.0 cm/s vs. 16.6 ± 3.9 cm/s vs. 15.1 ± 4.7 cm/s; p, Central Illustration

Epidemiological studies reporting demographic, clinical and serological factors predictive of various outcomes in systemic sclerosis (SSc) range from the prediction of mortality to the development and progression of disease manifestations. However, predicting the disease trajectory in the individual patient is a challenging but important step towards a stratified approach to disease management. Recent technological advances provide the opportunity for new subgroupings of disease based on risk stratification, through the systematic analysis of high-dimensional clinical data combined with genes, their transcription products and their corresponding translated proteins. In addition, these variables offer a rich vein of research to identify non-invasive biomarkers for predicting organ involvement and to assess disease activity and response to therapy. Selection of patients with a clinical phenotype or molecular signature relevant to the therapy under study combined with recent efforts to standardise outcome measures, show promise for improving clinical trial design and the identification of effective targeted therapies.

Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes mellitus. It leads to distressing and expensive clinical sequelae such as foot ulceration, leg amputation, and neuropathic pain (painful-DPN). Unfortunately, DPN is often diagnosed late when irreversible nerve injury has occurred and its first presentation may be with a diabetic foot ulcer. Several novel diagnostic techniques are available which may supplement clinical assessment and aid the early detection of DPN. Moreover, treatments for DPN and painful-DPN are limited. Only tight glucose control in type 1 diabetes has robust evidence in reducing the risk of developing DPN. However, neither glucose control nor pathogenetic treatments are effective in painful-DPN and symptomatic treatments are often inadequate. It has recently been hypothesized that using various patient characteristics it may be possible to stratify individuals and assign them targeted therapies to produce better pain relief. We review the diagnostic techniques which may aid the early detection of DPN in the clinical and research environment, and recent advances in precision medicine techniques for the treatment of painful-DPN.

Stratified medicine for mental disorders aims to identify neurobiological and behavioral domains that might help to increase the validity of diagnoses of such disorders beyond the level of symptom reports, and to provide more individualized and better targeted treatments. This chapter provides an overview of how such an approach might be used for translating basic neurobiological and behavioral science findings to personalized diagnosis and treatment of anxiety disorders. Starting with the behavioral domain, it will first be demonstrated that defensive behaviors are fundamental for the understanding of anxiety disorders, and that these behaviors are dynamically organized depending upon the proximity of a specific threat. In the next section we review which neural networks are activated during encoding of threat-relevant information, as well as during the organization of the cascade of defensive responses. The last section of the chapter deals with the translation of these behavioral and neuronal findings from experimental psychopathology research to clinical populations. The chapter will demonstrate how such transdiagnostic models of defensive responses can be used to describe the psychopathology of anxiety disorders. Moreover, we will show how these behavioral parameters are modulated by genetic factors. Finally, evidence will be presented that such behavioral approaches might be helpful for selecting individually targeted treatments for patients with anxiety disorders.

Background The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection methods focus on a homogeneous set of variables and do not take into account the case of predictors belonging to functional groups; typically, genomic data can be grouped according to biological pathways or to different types of collected data. Another challenge is that the standard lasso penalisation is known to have a high false discovery rate. Results We evaluated different penalizations in a Cox model to select grouped variables in order to further penalize variables that, in addition to having a low effect, belong to a group with a low overall effect; and to favor the selection of variables that, in addition to having a large effect, belong to a group with a large overall effect. We considered the case of prespecified and disjoint groups and proposed diverse weights for the adaptive lasso method. In particular we proposed the product Max Single Wald by Single Wald weighting (MSW*SW) which takes into account the information of the group to which it belongs and of this biomarker. Through simulations, we compared the selection and prediction ability of our approach with the standard lasso, the composite Minimax Concave Penalty (cMCP), the group exponential lasso (gel), the Integrative L1-Penalized Regression with Penalty Factors (IPF-Lasso), and the Sparse Group Lasso (SGL) methods. In addition, we illustrated the methods using gene expression data of 614 breast cancer patients. Conclusions The adaptive lasso with the MSW*SW weighting method incorporates both the information in the grouping structure and the individual variable. It outperformed the competitors by reducing the false discovery rate without severely increasing the false negative rate.

Ischemic heart disease secondary to coronary vascular dysfunction causes angina and impairs quality of life and prognosis. About one-half of patients with symptoms and signs of ischemia turn out not to have obstructive coronary artery disease, and coronary vascular dysfunction may be relevant. Adjunctive tests of coronary vasomotion include guidewire-based techniques with adenosine and reactivity testing, typically by intracoronary infusion of acetylcholine. The CorMicA (Coronary Microvascular Angina) trial provided evidence that routine management guided by an interventional diagnostic procedure and stratified therapy improves angina and quality of life in patients with angina but no obstructive coronary artery disease. In this paper, the COVADIS study group provide a comprehensive review of why, how, and when coronary vascular dysfunction should be assessed invasively. They discuss the rationale through a shared understanding of vascular pathophysiology and clinical evidence. They propose a consensus approach to how an interventional diagnostic procedure is performed with focus on practical aspects. Finally, the authors discuss the clinical scenarios in patients with stable and acute coronary syndromes in which measurement of coronary vascular function may be helpful for patient care., Central Illustration, Highlights • Patients with symptoms of INOCA can have treatable coronary vasomotion disorders. • Epicardial and microvascular vasospasm may cause MI with no obstructive CAD and type 2 MI. • Invasive physiological testing (functional coronary angiography) helps exclude, diagnose, and treat these conditions. • Stratified medicine can transform the management and well-being of these patients.

Despite tremendous advances in making an accurate diagnosis, most therapeutic programs lack precision and target the “average model patient.” While “one-size-fits-all-approach” treatments can be very successful for some patients, it often fails for many others. However, this has changed with rapid scientific and technical advances facilitating the practice of evidence-based personalized precision medicine. The power of evidence derived from genomic and molecular scientific advances is undisputed. The whole process involves a stepwise approach in building the holistic picture referred to as stratified medicine with the ultimate aim of individualized or personalized therapeutic interventions. The new exciting opportunity for stratified and personalized medicine is introduced in this chapter. The chapter discusses the integrated organization and delivery of the genomic and molecular medicine service.

Objective To predict the next-year status in patients with rheumatoid arthritis using big data. Methods Joint index (JI) of upper/large (UL), upper/small (US), lower/large (LL), and lower/small (LS) was calculated as the sum of tender and swollen joint counts divided by the number of evaluable joints in each region of interest. Joint index vector V (x, y, z) was defined as x = JIUL + JIUS, y = JILL + JILS, and z = JIUL + JILL − JIUS − JILS. Low disease activity was defined as |Vxy| (= √x2 + y2) ≤ 0.1. Patients with |Vxy| > 0.1 were further classified into three groups: evenly affected (EVN): |z| ≤ 0.2, small joint dominant (SML): z 0.2. To predict the next-year V (x, y, z) of each patient, a transformation matrix was computed from the mean vectors of the EVN, SML, and LAR groups and their translation vectors. Results |Vxy| was correlated with Simplified Disease Activity Index (SDAI) (r = 0.82). Z of mean vector increased as the disability index of the Health Assessment Questionnaire (HAQ-DI) and the Steinbrocker class worsened. The LAR group had the worst HAQ-DI and the second highest SDAI after those in the SML group. Positive predictive value and likelihood ratio in predicting the LAR group were 58.7% and 5.9, respectively. Likelihood ratio was greater with treatment, at 7.2, 7.4, and 8.6 when targeted patients were treated with methotrexate, biologics, and both drugs, respectively. Conclusions Patients with high disease activity and poor functional state were predicted with high probability using joint index vectors.

Strategies for biotechnology must take account of opportunities for research, innovation and business growth. At a regional level, public-private collaborations provide potential for such growth and the creation of centres of excellence. By considering recent progress in areas such as genomics, healthcare diagnostics, synthetic biology, gene editing and bio-digital technologies, opportunities for smart, strategic and specialised investment are discussed. These opportunities often involve convergent or disruptive technologies, combining for example elements of pharma-science, molecular biology, bioinformatics and novel device development to enhance biotechnology and the life sciences. Analytical applications use novel devices in mobile health, predictive diagnostics and stratified medicine. Synthetic biology provides opportunities for new product development and increased efficiency for existing processes. Successful centres of excellence should promote public-private business partnerships, clustering and global collaborations based on excellence, smart strategies and innovation if they are to remain sustainable in the longer term.

The concept of personalized medicine is a new individualized approach which helps application of the targeted therapy. In fact, tailored medicine is mostly present in the field of life-threatening diseases such as oncology. However, skin diseases as such might be regarded as a potential area of implementation of this approach in the future. Stratified medicine in polygenetic and heterogeneous diseases, such as psoriasis, is more complex. Rapid development of science and novel molecular techniques led to better understanding of molecular pathogenetic pathways of many diseases including psoriasis. Identification of the particular immunopathogenetic pathways led to further development of targeted therapies such as biologic drugs. Actually the goal of individualized therapy is to determine the identical homogenous subgroups of patients, according to a biomarker, in which the response to that therapy will be the best and will carry the lowest risk of side effects. This review attempts to analyze the associations between polymorphisms of certain genes and the increased risk of developing psoriasis or psoriatic arthritis. The review of literature has also included the studies investigating the associations between gene polymorphisms and response to biologic therapy in psoriasis and psoriatic arthritis patients.

Purpose of review The precision medicine concept is both appealing and challenging. We review here the recent findings in the endotype-driven approach for major allergic diseases. Recent findings Stratified medicine for different allergic diseases can identify patients who are more likely to benefit or experience an adverse reaction in response to a given therapy and anticipate their long-term outcome and vital risk. In addition, this approach potentially facilitates drug development and prevention strategies. Summary The endotype-driven approach in allergic diseases has tremendous potential, but there are notable barriers in reaching the new world of precision medicine. Multidimensional endotyping integrating visible properties with multiple biomarkers is recommended for both type 2 and nontype 2 allergic diseases to provide evidence that a certain pathway is the key driver for a given patient. Significant healthcare system changes are required to achieve the expected targets.

Electronic medical record (EMR)-based precision oncology is a vision that is so far limited to a few pilot and basket studies, with the goal being the design of a proper treatment for cancer patients in real time, based on the panomics knowledge of the patient, and that of similar types of patients. It aims to deliver better treatment outcomes through the design of rational drug combinations, a lower number of futile therapies, reduced patient discomfort, and a healthy human society with a reduced risk of cancer. The concept of precision oncology began with a few cancer awareness programmes and preventative screenings almost a decade ago. However, the technique took an astronomical leap with the start of the Precision Medicine Initiative Cohort Program and Cancer Moonshot programme very recently. Both projects have invested heavily towards several goals, including the merging of cancer registries and EMR to find the best treatment options for a cancer patient, an idea which, if extended globally, will generate unprecedented possibilities for precision oncology. EMR serve as a broad platform merging a variety of patient information and expert advice to facilitate co-ordinated cancer care. In this article, a summary of the recent EMR-based precision oncology practices for prevention, diagnosis, prognosis, prediction, and their associated concerns and limitations is presented. Though the path of precision oncology is unchartered, the usefulness of real-time information derived from EMR or electronic health records will lead to better precision decision-based oncotherapies.

Introduction: The advancement of precision medicine into routine clinical practice has been highlighted as an agenda for national and international health care policy. A principle barrier to this advancement is in meeting requirements of the payer or reimbursement agency for health care. This special report aims to explain the economic case for precision medicine, by accounting for the explicit objectives defined by decision-makers responsible for the allocation of limited health care resources.Areas covered: The framework of cost-effectiveness analysis, a method of economic evaluation, is used to describe how precision medicine can, in theory, exploit identifiable patient-level heterogeneity to improve population health outcomes and the relative cost-effectiveness of health care. Four case studies are used to illustrate potential challenges when demonstrating the economic case for a precision medicine in practice.Expert commentary: The economic case for a precision medicine should be considered at an early stage during its research and development phase. Clinical and economic evidence can be generated iteratively and should be in alignment with the objectives and requirements of decision-makers. Programmes of further research, to demonstrate the economic case of a precision medicine, can be prioritized by the extent that they reduce the uncertainty expressed by decision-makers.

Taking real advantage of Europe's excellence in research to improve citizens' lives presents challenges that Europe's policymakers have not yet fully met. The EU has shown some clarity of assessment in recognising the needs, and some laudable determination to improve the situation, and it has intermittently taken some real steps to deliver on its ambitions to turn its research into valuable innovations. But Europe still faces harsh choices about whether it is actually going to do what it has so often discussed. The EU has to make some firm decisions about what research deserves support - and where. It must turn words into deeds to promote effective links between research and innovation. That requires a sharper focus on developing and retaining the right skill sets in Europe, on funding innovation, on creating an encouraging regulatory environment, and on building greater public understanding and engagement. Here, among other issues, the authors discuss where resources should be deployed, how to maximise the potential of personalised medicine, the time it takes for search to be turned into products ready for market, education, and the EU's regulatory role.

There is a steady increase in the application of molecular diagnostics to supplement histological diagnosis. In the UK, recent large-scale genomic programmes are paving the way for more consistent and global genomic assessment of cancer samples as standard of care. The aim of this review is to provide a brief overview of the development and breadth of genomic technologies and the challenges encountered in their application. It will describe the key genomic alterations detected, their relevance and the challenges involved in interpretation. It will review several large scale sequencing initiatives that have generated a body of data across different cancer types allowing a better understanding of the complexity of genomic aberrations in tumours and how these changes relate to tumour behaviour and potential treatment strategies. In the UK, molecular diagnostics is being embedded in the NHS, first through the Cancer Research UK Stratified Medicine Programme and now through the 100,000 Genomes Project. The opportunities, challenges and lessons learnt from these ambitious programmes will be discussed. The wider application of genomic sequencing is changing the face of clinical trials and paving the way for precision medicine; some of the major trials are reviewed and their potential impact for future studies. Finally, we will discuss the challenges of implementing more comprehensive molecular diagnostics in the NHS and what this might look like.

This article considers why and how self-knowledge is important to communication about risk and behaviour change by arguing for four claims. First, it is doubtful that genetic knowledge should properly be called ‘self-knowledge’ when its ordinary effects on self-motivation and behaviour change seem so slight. Second, temptations towards a reductionist, fatalist, construal of persons’ futures through a ‘molecular optic’ should be resisted. Third, any plausible effort to change people’s behaviour must engage with cultural self-knowledge, values and beliefs, catalysed by the communication of genetic risk. For example, while a Judaeo-Christian notion of self-knowledge is distinctively theological, people’s self-knowledge is plural in its insight and sources. Fourth, self-knowledge is found in compassionate, if tense, communion which yields freedom from determinism even amidst suffering. Stratified medicine thus offers a newly precise kind of humanising health care through societal solidarity with the riskiest. However, stratification may also mean that molecularly unstratified, ‘B’ patients’ experience involves accentuated suffering and disappointment, a concern requiring further research.

Purpose of review: Epilepsy genetics is shifting from the academic pursuit of gene discovery to a clinical discipline based on molecular diagnosis and stratified medicine. We consider the latest developments in epilepsy genetics and review how gene discovery in epilepsy is influencing the clinical classification of epilepsy and informing new therapeutic approaches and drug discovery. Recent findings: Recent studies highlighting the importance of mutation in GABA receptors, NMDA receptors, potassium channels, G-protein coupled receptors, mammalian target of rapamycin pathway and chromatin remodeling are discussed. Examples of precision medicine in epilepsy targeting gain-of-function mutations in KCNT1, GRIN2A, GRIN2D and SCN8A are presented. Potential reasons for the paucity of examples of precision medicine for loss-of-function mutations or in non-ion channel epilepsy genes are explored. We highlight how systems genetics and gene network analyses have suggested that pathways disrupted in epilepsy overlap with those of other neurodevelopmental traits including human cognition. We review how network-based computational approaches are now being applied to epilepsy drug discovery. Summary: We are living in an unparalleled era of epilepsy gene discovery. Advances in clinical care from this progress are already materializing through improved clinical diagnosis and stratified medicine. The application of targeted drug repurposing based on single gene defects has shown promise for epilepsy arising from gain-of-function mutations in ion-channel subunit genes, but important barriers remain to translating these approaches to non-ion channel epilepsy genes and loss-of-function mutations. Gene network analysis offers opportunities to discover new pathways for epilepsy, to decipher epilepsy's relationship to other neurodevelopmental traits and to frame a new approach to epilepsy drug discovery.

J&J's phase II trial of an immunomodulator for depression could inform the development of stratified medicine in psychiatry. J&J's phase II trial of an immunomodulator for depression could inform the development of stratified medicine in psychiatry.

Background: Stratified medicine refers to the use of tests that predict treatment response to drive treatment decisions for individual patient. The pharmacoeconomic implications of this approach in schizophrenia is unknown. We aimed to assess the cost-effectiveness of a hypothetical stratified medicine algorithm (SMA) compared with treatment as usual (TAU), for patients with schizophrenia who failed a first-line antipsychotic. Methods: A decision analytic model with embedded Markov process was constructed based on the best available evidence. This model simulated the health and cost outcomes for patients followed SMA or TAU over a lifetime horizon, from healthcare and social care perspective. In the base-case analysis, sensitivity and specificity of the stratifier were both set as 60%. Extensive sensitivity analyses were conducted to test the impact of uncertainty around the value of all important parameters, including but not limited to sensitivity and specificity of the stratifier. The primary outcome was the incremental cost per quality-adjusted life year (QALY) gained. Outcomes: When both sensitivity and specificity of the stratified test were set at 60%, SMA appeared to be the optimal strategy as it produces more QALYs and incurs less costs than TAU. This is robust to all scenarios tested. At a willingness-to-pay threshold of £20,000 per QALY, the probability for SMA to be the optimal strategy is 82.4%.. Interpretation: Our results suggest that SMA is very likely to be cost-effective comparing to TAU, even with the relatively modest specificity and sensitivity in recently reported pharmacogenetic studies. Funding Statement: Medical Research Council, UK (grant number MR/L011794/1), and the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. Declaration of Interests: The authors declare no competing interests.

Biomarker-guided trials have drawn considerable attention as they promise to lead to improvements in the benefit-risk ratio of treatments and enhanced opportunities for drug development. A variety of such designs have been proposed in the literature, many of which have been adopted in practice.Implementing such trial designs in practice can be challenging, and identifying those challenges was the main objective of a workshop organised by the MRC Hubs for Trials Methodology Research Network's Stratified Medicine Working Group in March 2017. Participants reflected on completed and ongoing biomarker-guided trials to identify the practical challenges encountered. Here, the key challenges identified during the workshop including those related to funding, ethical and regulatory issues, recruitment, monitoring of samples and laboratories, biomarker assessment, and data sharing and resources, are discussed.Despite the complexities often associated with biomarker-guided trials, the workshop concluded that they can play an important role in advancing the field of personalised medicine. Therefore, it is important that the practical challenges surrounding their implementation are acknowledged and addressed. Keywords: Clinical trial, Biomarker, Challenges, Personalised medicine