Development and validation of a prediction rule for patients suspected of acute coronary syndrome in primary care: a cross-sectional study

Objective To develop and validate a symptom-based prediction rule for early recognition of acute coronary syndrome (ACS) in patients with acute chest discomfort who call out-of-hours services for primary care (OHS-PC). Design Cross-sectional study. A diagnostic prediction rule was developed with multivariable regression analyses. All models were validated with internal-external cross validation within seven OHS-PC locations. Both age and sex were analysed as statistical interaction terms, applying for age non-linear effects. Setting Seven OHS-PC in the Netherlands. Participants 2192 patients who called OHS-PC for acute chest discomfort (pain, pressure, tightness or discomfort) between 2014 and 2017. Backed up recordings of telephone triage conversations were analysed. Primary and secondary outcomes measures Diagnosis of ACS retrieved from the patient's medical records in general practice, including hospital specialists discharge letters. Performance of the prediction rules was calculated with the c-statistic and the final model was chosen based on net benefit analyses. Results Among the 2192 patients who called the OHS-PC with acute chest discomfort, 8.3% females and 15.3% males had an ACS. The final diagnostic model included seven predictors (sex, age, acute onset of chest pain lasting less than 12 hours, a pressing/heavy character of the pain, radiation of the pain, sweating and calling at night). It had an adjusted c-statistic of 0.77 (95% CI 0.74 to 0.79) with good calibration. Conclusion The final prediction model for ACS has good discrimination and calibration and shows promise for replacing the existing telephone triage rules for patients with acute chest discomfort in general practice and OHS-PC. Trial registration number NTR7331

Perinatal thalamic injury: MRI predictors of electrical status epilepticus in sleep and long-term neurodevelopment

Objective: Perinatal thalamic injury is associated with epilepsy with electrical status epilepticus in sleep (ESES). The aim of this study was to prospectively quantify the risk of ESES and to assess neuroimaging predictors of neurodevelopment. Methods: We included patients with perinatal thalamic injury. MRI scans were obtained in the neonatal period, around three months of age and during childhood. Thalamic and total brain volumes were obtained from the three months MRI. Diffusion characteristics were assessed. Sleep EEGs distinguished patients into ESES (spike-wave index (SWI) >85%), ESES-spectrum (SWI 50–85%) or no ESES (SWI < 50%). Serial Intelligence Quotient (IQ)/Developmental Quotient (DQ) scores were obtained during follow-up. Imaging and EEG findings were correlated to neurodevelopmental outcome. Results: Thirty patients were included. Mean thalamic volume at three months was 8.11 (±1.67) ml and mean total brain volume 526.45 (±88.99) ml. In the prospective cohort (n = 23) 19 patients (83%) developed ESES (-spectrum) abnormalities after a mean follow-up of 96 months. In the univariate analysis, larger thalamic volume, larger total brain volume and lower SWI correlated with higher mean IQ/DQ after 2 years (Pearson's r = 0.74, p = 0.001; Pearson's r = 0.64, p = 0.005; and Spearman's rho -0.44, p = 0.03). In a multivariable mixed model analysis, thalamic volume was a significant predictor of IQ/DQ (coefficient 9.60 [p < 0.001], i.e., corrected for total brain volume and SWI and accounting for repeated measures within patients, a 1 ml higher thalamic volume was associated with a 9.6 points higher IQ). Diffusion characteristics during childhood correlated with IQ/DQ after 2 years. Significance: Perinatal thalamic injury is followed by electrical status epilepticus in sleep in the majority of patients. Thalamic volume and diffusion characteristics correlate to neurodevelopmental outcome

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Home-based initiatives for acute management of COVID-19 patients needing oxygen: differences across The Netherlands

Objective: During the COVID-19 pandemic new collaborative-care initiatives were developed for treating and monitoring COVID-19 patients with oxygen at home. Aim was to provide a structured overview focused on differences and similarities of initiatives of acute home-based management in the Netherlands. Methods: Initiatives were eligible for evaluation if (i) COVID-19 patients received oxygen treatment at home; (ii) patients received structured remote monitoring; (iii) it was not an ‘early hospital discharge’ program; (iv) at least one patient was included. Protocols were screened, and additional information was obtained from involved physicians. Design choices were categorised into: eligible patient group, organization medical care, remote monitoring, nursing care, and devices used. Results: Nine initiatives were screened for eligibility; five were included. Three initiatives included low-risk patients and two were designed specifically for frail patients. Emergency department (ED) visit for an initial diagnostic work-up and evaluation was mandatory in three initiatives before starting home management. Medical responsibility was either assigned to the general practitioner or hospital specialist, most often pulmonologist or internist. Pulse-oximetry was used in all initiatives, with additional monitoring of heart rate and respiratory rate in three initiatives. Remote monitoring staff’s qualification and authority varied, and organization and logistics were covered by persons with various backgrounds. All initiatives offered remote monitoring via an application, two also offered a paper diary option. Conclusions: We observed differences in the organization of interprofessional collaboration for acute home management of hypoxemic COVID-19 patients. All initiatives used pulse-oximetry and an app for remote monitoring. Our overview may be of help to healthcare providers and organizations to set up and implement similar acute home management initiatives for critical episodes of COVID-19 (or other acute disorders) that would otherwise require hospital care

RANTES/CCL5 and risk for coronary events: Results from the MONICA/KORA Augsburg case-cohort, Athero-express and CARDIoGRAM studies

Background: The chemokine RANTES (regulated on activation, normal T-cell expressed and secreted)/CCL5 is involved in the pathogenesis of cardiovascular disease in mice, whereas less is known in humans. We hypothesised that its relevance for atherosclerosis should be reflected by associations between CCL5 gene variants, RANTES serum concentrations and protein levels in atherosclerotic plaques and risk for coronary events. Methods and Findings: We conducted a case-cohort study within the population-based MONICA/KORA Augsburg studies. Baseline RANTES serum levels were measured in 363 individuals with incident coronary events and 1,908 non-cases (mean follow-up: 10.2±

Age at first birth in women is genetically associated with increased risk of schizophrenia

Prof. Paunio on PGC:n jäsenPrevious studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.Peer reviewe

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

CSG Next 2008-2013 : Harvesting Results. Preparing for the future

The Centre for Society and Genomics (CSG) was established in 2004, funded by NGI (the Netherlands Genomics Initiative). Funding was continued in 2008. This report summarises the basic outcomes of almost a decade of interactive societal research, in close collaboration with the other centres of the NGI network. There are two reasons for presenting these results. First of all, at the end of this year, the CSG Next programme (2008-2013), encompassing more than 50 research projects conducted at 10 Dutch universities, will be completed. Moreover, we are currently preparing ourselves for the years to come. The network of principal investigators, together with the research communities they represent and the societal and international networks they are involved in, have agreed to continue to work together, on the basis of mutual learning, transdisciplinary collaboration and collegial support. Notably, we offer our networks, experiences and expertise to help prepare the ground for promoting Responsible Research and Innovation (RRI) in the context of Horizon2020, together with our European colleagues. This report summarises what our type of research can achieve and how we want to continue our activities in the future. After a concise sketch of the life sciences landscape as it has evolved during the past seven decades or so, we explain how CSG came about and what kind of approach we have developed. Subsequently, we list our main results, notably in the form of project vignettes, so as to make the harvest of the CSG Next programme as tangible and concrete as possible. Finally, we explain how we see our role in the future. As is already indicated by the title: this is not merely a retrospective summary of our results (CSG harvest), but an invitation to readers (from academia, industry, policy and civil society) to reassemble and to optimally prepare ourselves for things to come, by strengthening and broadening our collaborative efforts, building on what we have achieved so far

CSG Next 2008-2013 : Harvesting Results. Preparing for the future

The Centre for Society and Genomics (CSG) was established in 2004, funded by NGI (the Netherlands Genomics Initiative). Funding was continued in 2008. This report summarises the basic outcomes of almost a decade of interactive societal research, in close collaboration with the other centres of the NGI network. There are two reasons for presenting these results. First of all, at the end of this year, the CSG Next programme (2008-2013), encompassing more than 50 research projects conducted at 10 Dutch universities, will be completed. Moreover, we are currently preparing ourselves for the years to come. The network of principal investigators, together with the research communities they represent and the societal and international networks they are involved in, have agreed to continue to work together, on the basis of mutual learning, transdisciplinary collaboration and collegial support. Notably, we offer our networks, experiences and expertise to help prepare the ground for promoting Responsible Research and Innovation (RRI) in the context of Horizon2020, together with our European colleagues. This report summarises what our type of research can achieve and how we want to continue our activities in the future. After a concise sketch of the life sciences landscape as it has evolved during the past seven decades or so, we explain how CSG came about and what kind of approach we have developed. Subsequently, we list our main results, notably in the form of project vignettes, so as to make the harvest of the CSG Next programme as tangible and concrete as possible. Finally, we explain how we see our role in the future. As is already indicated by the title: this is not merely a retrospective summary of our results (CSG harvest), but an invitation to readers (from academia, industry, policy and civil society) to reassemble and to optimally prepare ourselves for things to come, by strengthening and broadening our collaborative efforts, building on what we have achieved so far