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The use of routinely collected electronic healthcare records (EHR) for outcome assessment in clinical trials has been described as a 'disruptive' new technique more than a decade ago. Despite this potential, significant methodological issues and regulatory barriers have hampered the progress in this area. This article discusses the key considerations that trialists should take into account when incorporating EHR into their trials. These include considerations of the clinical relevance of the outcome, data timeliness and quality, ethical and regulatory issues, and some practical considerations for clinical trials units. In addition, this article describes the benefits of using EHR which include cost, reduced trial burden for participants and staff, follow up efficiencies, and improved health economic evaluation procedures. We also describe the major regulatory and start up costs of using EHR in clinical trials. This article focuses on the UK specific EHR landscape in clinical trials and would help researchers and trials units considering the use of this method of outcome data collection in their next trial. If the issues described are mitigated, this method will be a formidable tool for conducting pragmatic clinical trials.

The psychoactive properties of cannabis have been known forever. Since 1987, several prospective studies have suggested an increased risk of psychosis among cannabis users, with alternative explanations failing to account for such an effect. A cause-effect relationship has thus been implied. Further evidence has indicated that there is a dose-response relationship, and high-potency cannabis varieties confer the greatest risk of psychosis. As cannabis use has become more common over the last decades, one would expect a related increase in the number of schizophrenia cases. However, evidence in this regard remains equivocal for several reasons, including relying on databases that are not primarily designed to address such question and the issue that solid information regarding the incidence of schizophrenia is a relatively recent acquisition. Recent years have seen the development of online web publications, such as Google Trends and "Our World in Data", where data are explorable and interactable for tracking and comparing trends over specific periods and world regions. By using such databases, we believe that the question whether changes in cannabis use are associated with changes in schizophrenia rates can be answered, at least partly. Therefore, we tested these tools by evaluating trends in cannabis use and both cases and prevalence of schizophrenia in the United Kingdom, one of the countries where the incident rates for psychotic disorder have been suggested to be particularly increased by cannabis consumption. Crossing data from these tools revealed that interest in cannabis has been growing at the country level for over 10 years, with a parallel overlapping raise in psychosis cases and prevalence. Following up on this example, let us think of how many public health opportunities these public resources may offer. The question now is whether public health interventions for the benefit of the general population will follow suit.

Aims: Cardiovascular diseases (CVDs) are the leading cause of deaths globally. Mortality and incidence of CVDs are significantly higher in people with mood disorders. About 81.1% of CVD patients were reported with comorbidities in 2019, where the second most common comorbidity was due to major depressive disorder (MDD). This study, therefore, aimed to evaluate the genetic correlation between CVDs and mood disorders by using data from the UK Biobank towards understanding the influence of genetic factors on the comorbidity due to CVDs and mood disorders., Methods: The UK Biobank database provides genetic and health information from half a million adults, aged 40-69 years, recruited between 2006 and 2010. A total of 117,925 participants and 6,128,294 variants were included for analysis after applying exclusion criteria and quality control steps. This study focused on two CVD phenotypes, two mood disorders and 12 cardiometabolic-related traits to conduct association studies., Results: The results indicated a significant positive genetic correlation between CVDs and overall mood disorders and MDD specifically, showing substantial genetic overlap. Genetic correlation between CVDs and bipolar disorder was not significant. Furthermore, significant genetic correlation between mood disorders and cardiometabolic traits was also reported., Conclusions: The results of this study can be used to understand that CVDs and mood disorders share a great deal of genetic liability in individuals of European ancestry.

The British Heart Foundation's (BHF) annual statistical compendium is a comprehensive source of accessible epidemiological data in relation to cardiovascular disease (CVD) in the UK. Using datasets with multiple years of data from the compendium we have analysed trends in mortality, morbidity, and treatment for CVD within the UK. CVD mortality in the UK has consistently declined over recent decades, from 1045 deaths per 100 000 in 1969, shortly after the BHF was founded, to 255 per 100 000 in 2019. Despite this remarkable improvement, inequalities in CVD mortality persist in the UK nations, for example in 2019 the death rate in Scotland was 326 deaths per 100 000 compared with 246 per 100 000 in England. Improvements in CVD mortality have been paralleled by increased use of primary prevention medications (anti-hypertensives and statins) and interventional procedures. In recent years, progress in mortality outcomes has stalled, probably due to a combination of factors including a rise in risk factors such as obesity and diabetes. In terms of morbidity, CVD remains a significant burden in the UK, accounting for at least 1.18 million hospital admissions and reflects the enormous economic burden of CVD, estimated at £19bn in the UK. Our results highlight the importance of accessible and comprehensive statistics in relation to the burden of CVD and the value of the BHF's annual compendium in drawing out conclusions and opportunities for future research. One key area is to improve the data on which estimation of prevalence is based. There is also a need for ongoing work to better understand the root causes of disparity between socio-economic groups in relation to CVD. One important way to address this will be to improve the consistency of reporting of CVD health data across all nations of the UK. Understanding the causes will inform UK healthcare planning in addition to providing analytical insights that will be applicable in other countries., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2022. For permissions, please e-mail: journals.permissions@oup.com.)

There are concerns that simultaneous pancreas-kidney (SPK) transplants from donation after circulatory death (DCD) donors have a higher risk of graft failure than those from donation after brain death (DBD) donors. A UK registry analysis of SPK transplants between 2005 and 2018 was performed. Pancreas survivals of those receiving organs from DCD or DBD donors were compared. Multivariable analyses were used to adjust for baseline differences between the two groups and to identify factors associated with pancreas graft loss. A total of 2228 SPK transplants were implanted; 403 (18.1%) were from DCD donors. DCD donors were generally younger, slimmer, less likely to have stroke as a cause of death, with lower terminal creatinines and shorter pancreas cold ischemic times than DBD donors. Median (IQR) follow-up was 4.2 (1.6-8.1) years. On univariable analysis, there were no statistically significant differences in 5-year death-censored pancreas graft survival between the two donor types (79.5% versus 80.4%; p = .86). Multivariable analysis showed no statistically significant differences in 5-year pancreas graft loss between transplants from DCD (n = 343) and DBD (n = 1492) donors (hazard ratio 1.26, 95% CI 0.76-1.23; p = .12). The findings from this study support the increased use of SPK transplants from DCD donors., (© 2021 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Background: Parenteral nutrition is commonly administered during therapeutic hypothermia. Randomised trials in critically ill children indicate that parenteral nutrition may be harmful., Objective: To examine the association between parenteral nutrition during therapeutic hypothermia and clinically important outcomes., Design: Retrospective, population-based cohort study using the National Neonatal Research Database; propensity scores were used to create matched groups for comparison., Setting: National Health Service neonatal units in England, Scotland and Wales., Participants: 6030 term and near-term babies, born 1/1/2010 and 31/12/2017, who received therapeutic hypothermia; 2480 babies in the matched analysis., Exposure: We compared babies that received any parenteral nutrition during therapeutic hypothermia with babies that did not., Main Outcome Measures: Primary outcome: blood culture confirmed late-onset infection; secondary outcomes: treatment for late onset infection, necrotising enterocolitis, survival, length of stay, measures of breast feeding, hypoglycaemia, central line days, time to full enteral feeds, discharge weight., Results: 1475/6030 babies (25%) received parenteral nutrition. In comparative matched analyses, the rate of culture positive late onset infection was higher in babies that received parenteral nutrition (0.3% vs 0.9%; difference 0.6; 95% CI 0.1, 1.2; p=0.03), but treatment for presumed infection was not (difference 0.8%, 95% CI -2.1 to 3.6, p=0.61). Survival was higher in babies that received parenteral nutrition (93.1% vs 90.0%; rate difference 3.1, 95% CI 1.5, 4.7; p<0.001)., Conclusions: Receipt of parenteral nutrition during therapeutic hypothermia is associated with higher late-onset infection but lower mortality. This finding may be explained by residual confounding. Research should address the risks and benefits of parenteral nutrition in this population., Competing Interests: Competing interests: CG reports grants from Medical Research Council and the National Institute for Health Research during the conduct of the study; grants from National Institute for Health Research, Mason Medical Research Foundation, Rosetrees Foundation and from Canadian Institute for Health Research outside the submitted work. He reports a grant from Chiesi Pharmaceuticals outside of the submitted work for a research study and a personal fee from Chiesi Pharmaceuticals to support attendance at an educational meeting. CG is vice-chair of the NIHR Research for Patient Benefit London Regional Assessment Panel, and has sat on the panel since 2016. CG was an unremunerated member of the Neonatal Data Analysis Unit (NDAU) steering board which oversees the National Neonatal Research Database (NNRD) until August 2020. DJ reports no potential conflicts of interest. NL reports no potential conflicts of interest. CB reports personal fees from AbbVie Pharmaceuticals outside of the submitted work; the personal fee was to support attendance at an educational meeting. CB sits on the NIHR HTA Prioritisation Panel for Maternal, Child and Mental Health Care since November 2019. KO reports no potential conflicts of interest. SO reports grants from the National Institute for Health Research, Medical Research Council, Global Challenges Research Fund, and the Arts and Humanities Research Council outside of the work on this study. JD reports grants from NIHR, the Canadian Institute for Health Research, IWK Heath Centre, Research Nova Scotia, during the study and for the study; grants from NIHR, and grants from Nutrinia, outside the submitted work. The grant from Nutrinia in 2018 was for part of his salary to work as an expert advisor on a trial. He was a member of the NIHR HTA General Board (from 2017 to 2018) and the NIHR HTA Maternity, Newborn and Child Health Panel (from 2013 to 2018)., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objective: To quantitatively analyse the number of doctors leaving the paediatric specialty training (ST) programme in the UK, to assist with evidence-based workforce planning., Design: Data were sought on those leaving the UK paediatrics training programme between 2014 and 2019 from Heads of Schools of Paediatrics and Freedom of Information Act requests., Setting: Retrospective data analysis., Outcome Measures: Overall attrition rate, attrition rate across level of training, attrition rate across geographical area, recorded reason for leaving., Results: All results must be interpreted with caution due to limitations in record keeping and analysis. The annual attrition rate across all ST levels between 2014 and 2019 is estimated at 3.7%-4.2% (ie, 749-845 trainees may have left the paediatric training programme over 2014-2019). No reason for leaving was recorded for three-quarters of individuals, around 630 doctors. Of those leaving paediatrics, significantly more (χ², p=0.015) did so at ST3 (20.3%) versus the next highest training year, ST2 (13.6%)., Conclusions: This project seems to demonstrate worryingly poor record-keeping of the true attrition rate of paediatric trainees by organisations responsible for workforce planning, including Health Education England, the Royal College of Paediatrics and Child Health and individual paediatric schools across the UK. To allow evidence-based workforce planning for the benefit of UK children, it is vital that accurate records on trainees who leave the training programme are kept and shared across the UK., Competing Interests: Competing interests: DC and MR are employed by the Royal College of Paediatrics and Child Health (RCPCH). NJ is Workforce Officer for RCPCH. SJC is Vice President of RCPCH. MGR is a member of RCPCH. This manuscript is not endorsed by RCPCH., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Aims/hypothesis: Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently., Methods: Using publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate <0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined co-localisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a co-localisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases., Results: Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near CTRB1/BCAR1, which has been previously identified; (2) chromosome 11p15.5, near the INS gene; (3) chromosome 4p16.3, near TMEM129 and (4) chromosome 1p31.3, near PGM1. In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified co-localisation on chromosome 9p24.2, near the GLIS3 gene, in this case with a concordant direction of effect., Conclusions/interpretation: Four of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases.

Objective: To explore the approach to participant coenrolment in publicly funded randomized controlled trials (RCTs) and to consider its impact on study recruitment., Study Design and Setting: A cross-sectional study of the UK National Institute for Health Research Journals Library was undertaken. RCTs funded between 2010 and 2019 were eligible. The reporting of coenrolment criteria was assessed through inspection of publicly available study protocols. Where present, the approach to coenrolment was examined, including circumstances in which it was permitted/prohibited and the mechanism for decision-making. For completed RCTs, the impact on recruitment was explored by comparing rates of early recruitment (completion before the expected end date) and extensions (completion after the expected end date) between studies, which did and did not permit coenrolment., Results: Of 219 eligible protocols, coenrolment was addressed in 94 (42.9%). Twenty-three (24.5%) of these did not allow recruitment to multiple studies, while 71 (75.5%) permitted it according to a series of caveats, including considerations of study outcomes, intervention type, and patient burden. The final decision for coenrolment rested with the local recruitment team in 57 (60.6%) and with the central organizing team in 37 (39.4%). Early completion of recruitment occurred in 8 of 64 (12.5%) RCTs where coenrolment was permitted and 5 of 20 (25.0%) where it was not (P = 0.285). An extension to recruitment time was required in 31 of 64 (48.4%) RCTs where coenrolment was permitted and 9 of 11 (45.0%) where it was not (P = 0.788)., Conclusions: The reporting of coenrolment in protocols of publicly funded RCTs is infrequent, and where present, the approach to decision-making is widely variable. In this study, policies of coenrolment were not associated with gains in trial recruitment., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: The COVID-19 pandemic and the associated lockdowns have caused significant disruptions across society, including changes in the number of emergency department (ED) visits. This study aims to investigate the impact of three pre-COVID-19 interventions and of the COVID-19 UK-epidemic and the first UK national lockdown on overcrowding within University College London Hospital Emergency Department (UCLH ED). The three interventions: target the influx of patients at ED (A), reduce the pressure on in-patients' beds (B) and improve ED processes to improve the flow of patents out from ED (C)., Methods: We collected overcrowding metrics (daily attendances, the proportion of people leaving within 4 h of arrival (four-hours target) and the reduction in overall waiting time) during 01/04/2017-31/05/2020. We then performed three different analyses, considering three different timeframes. The first analysis used data 01/04/2017-31/12-2019 to calculate changes over a period of 6 months before and after the start of interventions A-C. The second and third analyses focused on evaluating the impact of the COVID-19 epidemic, comparing the first 10 months in 2020 and 2019, and of the first national lockdown (23/03/2020-31/05/2020)., Results: Pre-COVID-19 all interventions led to small reductions in waiting time (17%, p < 0.001 for A and C; an 9%, p = 0.322 for B) but also to a small decrease in the number of patients leaving within 4 h of arrival (6.6,7.4,6.2% respectively A-C,p < 0.001). In presence of the COVID-19 pandemic, attendance and waiting time were reduced (40% and 8%; p < 0.001), and the number of people leaving within 4 h of arrival was increased (6%,p < 0.001). During the first lockdown, there was 65% reduction in attendance, 22% reduction in waiting time and 8% increase in number of people leaving within 4 h of arrival (p < 0.001). Crucially, when the lockdown was lifted, there was an increase (6.5%,p < 0.001) in the percentage of people leaving within 4 h, together with a larger (12.5%,p < 0.001) decrease in waiting time. This occurred despite the increase of 49.6%(p < 0.001) in attendance after lockdown ended., Conclusions: The mixed results pre-COVID-19 (significant improvements in waiting time with some interventions but not improvement in the four-hours target), may be due to indirect impacts of these interventions, where increasing pressure on one part of the ED system affected other parts. This underlines the need for multifaceted interventions and a system-wide approach to improve the pathway of flow through the ED system is necessary. During 2020 and in presence of the COVID-19 epidemic, a shift in public behaviour with anxiety over attending hospitals and higher use of virtual consultations, led to notable drop in UCLH ED attendance and consequential curbing of overcrowding. Importantly, once the lockdown was lifted, although there was an increase in arrivals at UCLH ED, overcrowding metrics were reduced. Thus, the combination of shifted public behaviour and the restructuring changes during COVID-19 epidemic, maybe be able to curb future ED overcrowding, but longer timeframe analysis is required to confirm this.

Objective: To determine trends in the demographics and destinations of doctors who have recently completed paediatric training in the UK., Design: A survey was sent to all new paediatric certificate holders 1 year on from completing specialty training every year from 2011 to 2017., Setting: Retrospective survey., Outcome Measures: Demographics, career destinations, time to complete training, working patterns, subspecialty registration, numbers of job applications, and use of the period of grace are reported., Results: 1262 people who gained their paediatric certificate in the UK between 2011 and 2017 completed the survey (60.6% response rate). 58.5% (n=738) of respondents were female, and 32.4% (n=224) of women work less than full time, compared with 4.6% (n=23) of men. 85.9% (n=1056) of respondents were in a UK consultant post. 7.6% (n=94) were working overseas. 65.1% (n=722) remained in the region they trained in. 64.8% (n=1348) were registered for general paediatrics, whereas 35.2% (n=733) had subspecialised.Respondents who held a non-UK medical degree (47.5%, n=501) made more job applications on average (mean=2.2; 95% CI 2.0 to 2.5) than those with a UK degree (52.5%, n=554) (mean=1.1; 95% CI 1.0 to 1.2) (p<0.001). Average training time increased from 9.8 years (95% CI 9.4 to 10.2) to 11.3 years (95% CI 11.1 to 11.6) (p<0.001). Respondents' use of their grace period reduced from 42.7% (n=47) to 20.6% (n=29) (p<0.001)., Conclusions: The data reflect the diverse paediatric workforce and doctors' working patterns following the completion of paediatric training in the UK. The trends demonstrated are vital to consider for evidence-based workforce planning., Competing Interests: Competing interests: DC and MR are employed by RCPCH. NJ is workforce officer for RCPCH. SJC is vice president of RCPCH. MGR is a member of RCPCH. MGR completed this work during her time as a leadership fellow under Health Education England working across Yorkshire and the Humber Future Leaders Programme., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: In addition to researcher-designed sampling biases, population-representative surveys for biomarker measurement of STIs often have substantial missingness due to non-contact, non-consent and other study-implementation issues. STI prevalence estimates may be biased if this missingness is related to STI risk. We investigated how accounting for sampling, interview non-response and non-provision of biological samples affects prevalence estimates in the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3)., Methods: Natsal-3 was a multistage, clustered and stratified probability sample of 16-74 year-olds conducted between 2010 and 2012. Individuals were sampled from all private residential addresses in Britain; respondents aged 16-44 were further sampled to provide a urine specimen based on characteristics including self-reported sexual behaviours. We generated prevalence estimates and confidence intervals for six STIs in five stages: first without accounting for sampling or non-response, then applying inverse-probability weights cumulatively accounting for interview sampling, interview non-response, urine sampling and urine non-response., Results: Interview non-completion occurred for 42.3% of interview-sampled individuals; urine non-completion occurred for 43.5% of urine-sampled individuals. Interview-sampled individuals, interview respondents, those selected for urine samples and those providing urine samples were each in turn slightly more at-risk for most STIs, leading to lower prevalence estimates after incorporating each set of weights. Researcher-controlled sampling had more impact than respondent-controlled response., Conclusions: Accounting for both sampling structures and willingness to interview or provide urine specimens can affect national STI prevalence estimates. Using both types of weights, as was done in Natsal-3, is important in reporting on population-based biomarker surveys., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

Objective: To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPS CAD ) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPS CAD in 28 556 middle-aged participants of the Malmö Diet and Cancer Study, of whom 4122 (14.4%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed-16% for those in the lowest GPS CAD decile to 48% in the highest. We evaluated the discriminative capacity of the GPS CAD -as assessed by change in the C-statistic from a baseline model including age and sex-among 5685 individuals with PCE risk estimates available. The increment for the GPS CAD (+0.045, P <0.001) was higher than for any of 11 traditional risk factors (range +0.007 to +0.032). Minimal correlation was observed between GPS CAD and 10-year risk defined by the PCE ( r =0.03), and addition of GPS CAD improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPS CAD , even among individuals within a given PCE clinical risk stratum. We replicated key findings-noting strikingly consistent results-in 325 003 participants of the UK Biobank., Conclusions: GPS CAD -a risk estimator available from birth-stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPS CAD may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.

Background: Although the need for whole blood is declining, so too are the number of first-time and repeat blood donors. To develop new recruitment and retention strategies, therefore, we need to draw on as wide a variation in blood donor motivations as possible. The primary aim of this study is to draw on a large survey of donors to develop a broad, theoretically instantiated typology of donor motivations to identify new and less common, yet practically important, motivations that have not been previously reported., Study Design and Methods: Using data from the UK Blood Donor Survey run by NHS Blood and Transplant/Public Health England Epidemiology Unit (N = 61 123 donors), we analyze fixed (N = 52 225) and free (N = 8867) responses to develop a more comprehensive typology of blood donor motivations based on theories from the biology, psychology, philosophy, economics, and sociology of altruism., Results: We identified 54 motivations, including a number of newly identified motivations, for blood donations which we organized into 12 superordinate categories (eg, "inspiration via moral elevation," "perceived social closeness," and "fungibility of donations"). These are linked to intervention suggestions such as donating blood in memoriam or donating blood as an alternative to other charitable acts., Conclusion: We present the most comprehensive account of blood donor motivations to-date. This work also offers a structure for coding free-text responses, developing motivational measures, and identifying tangible interventions. Thus, we feel that this is a valuable resource for blood donor researchers, marketers, and policy makers., (© 2020 The Authors. Transfusion published by Wiley Periodicals LLC. on behalf of AABB.)

Introduction: The health of the British population has been shown to be worsening by self-reported health and improving by self-reported limiting illness for those born before and after 1945. Little is known about the inequality in health difference across British birth cohorts by income., Methods: Repeated cross-sections from the British General Household Survey, 1979-2011, are used to create pseudo birth cohorts born, 1920-1970, and their gender stratified, age-adjusted limiting illness and self-rated health (SRH) are estimated by household income tertiles. Absolute and relative differences between the poorest and richest income groups are reported., Results: Absolute inequalities in limiting illness between the richest and poorest households have doubled in women and increased by one and a half times in men for those born in 1920-1922 compared with those born in 1968-1970. Relative inequalities in limiting illness increased by a half in women and doubled in men. Absolute inequalities in SRH between the richest and poorest households increased by almost half in women and more than half in men and relative inequalities increased by 18% in women and 14% in men for those born in 1920-1922 compared with those born in 1968-1970., Conclusion: Inequalities in self-reported health at the same age by household income have widened for successively later-born British cohorts., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Since the launch of their first trial in Acute myeloid leukaemia (AML) in 1959, the Medical Research Council (and latterly National Cancer Research Institute) has conducted randomised trials in AML uninterrupted for six decades. These sixty years have seen a transformation in the way we diagnose, characterise and treat the disease, (and indeed a sea change in clinical trial regulations) and a continuing improvement in outcomes. The increasing refinement of diagnosis, leading to the advent of tailored therapies, and the use of disease monitoring both have the potential to improve outcomes further, but the associated complexities will require an evolution in our approach to trial design. This article looks at the extent to which the guiding principles of the first AML trials remain relevant today, and the challenges facing the next generation of trials methodologists., (© 2019 British Society for Haematology and John Wiley & Sons Ltd.)

Objective: Schizophrenia is associated with a marked reduction in reproductive success, yet alleles that are common contribute substantially to the liability of the disorder. Among several possible explanations for this, it has been postulated that individuals who carry risk alleles but are unaffected are at some reproductive advantage, offsetting the effects of negative selection among those who are affected. The authors sought to test this hypothesis, isolating the effects of risk alleles on fecundity from the effects that are contingent on expressing schizophrenia., Methods: The burden of schizophrenia risk alleles, as indexed by a polygenic risk score (PRS), carried by 139,679 participants in the UK Biobank study who did not have schizophrenia was compared with the number of offspring of these individuals., Results: Higher schizophrenia liability in study subjects without manifest disorder was weakly but significantly associated with having more children (B=0.006, 95% CI=0.002, 0.010). The relationship was dependent on sex, with a positive correlation between number of children and liability among females (B=0.011, 95% CI=0.006, 0.016), whereas among males, higher liability was associated with being childless (odds ratio=0.96, 95% CI=0.94, 0.98). The negative effect on number of children associated with schizophrenia itself was twofold to 15-fold greater than the positive effect associated with PRS in unaffected individuals., Conclusions: These findings suggest that a complex relationship between liability and fecundity is consistent with sexual selection. Although the overall pattern of a weak positive correlation with liability may contribute to the persistence of schizophrenia risk alleles, these results indicate that the negative selection acting on individuals affected by schizophrenia in the general population is larger than any advantage conferred by genetic loading in unaffected individuals.

Aim: Few personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD)., Methods: Using logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC - UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old)., Results: The overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79-0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71-0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower -0.57 (95% CI 0.54-0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73-0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/., Conclusions: The risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution.