Characterization of Jupiter's secondary auroral oval and its response to hot plasma injections

We present Jovian auroral observations from the 2014 January Hubble Space Telescope (HST) campaign and characterize the auroral second oval feature with particular attention to the response to hot plasma injections. The location of the second oval feature lies between the Ganymede and Europa moon footprint contours between 150 and 240° system III longitude, corresponding to a source in the inner magnetosphere between 9 and 13 RJ. At the examined longitudes, this is in the same region of 11–16 RJ known as the pitch angle distribution boundary, beyond which electrons are thought to be scattered into a field-aligned configuration and cause auroral precipitation. The feature is enhanced in both brightness and longitudinal spread 1–3 days after large hot plasma injections. The precipitating electrons have a higher-energy and lower flux than the electrons generating large injection signatures. We suggest that wave-particle interactions are responsible for the scattering of electrons in this region. We also suggest that the plasma injections can act as a temperature anisotropy and particle source to enhance electron scattering into the aurora and the brightness of the second oval feature. Changes to the magnetic field topology around an injection may also generate shear Alfvén waves and therefore accelerate electrons parallel to the magnetic field resulting in precipitation

Physician-Confirmed and Administrative Definitions of Stroke in UK Biobank Reflect the Same Underlying Genetic Trait

BACKGROUND: Stroke in UK Biobank (UKB) is ascertained via linkages to coded administrative datasets and self-report. We studied the accuracy of these codes using genetic validation. METHODS: We compiled stroke-specific and broad cerebrovascular disease (CVD) code lists (Read V2/V3, ICD-9/-10) for medical settings (hospital, death record, primary care) and self-report. Among 408,210 UKB participants, we identified all with a relevant code, creating 12 stroke definitions based on the code type and source. We performed genome-wide association studies (GWASs) for each definition, comparing summary results against the largest published stroke GWAS (MEGASTROKE), assessing genetic correlations, and replicating 32 stroke-associated loci. RESULTS: The stroke case numbers identified varied widely from 3,976 (primary care stroke-specific codes) to 19,449 (all codes, all sources). All 12 UKB stroke definitions were significantly correlated with the MEGASTROKE summary GWAS results (rg.81-1) and each other (rg.4-1). However, Bonferroni-corrected confidence intervals were wide, suggesting limited precision of some results. Six previously reported stroke-associated loci were replicated using ≥1 UKB stroke definition. CONCLUSIONS: Stroke case numbers in UKB depend on the code source and type used, with a 5-fold difference in the maximum case-sample size. All stroke definitions are significantly genetically correlated with the largest stroke GWAS to date

Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: a systematic review

Background: Motor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets. We systematically evaluated the accuracy of such datasets in identifying MND cases. Methods: We performed an electronic search of MEDLINE, EMBASE, Cochrane Library and Web of Science for studies published between 01/01/1990-16/11/2015 that compared MND cases identified in routinely-collected, coded datasets to a reference standard. We recorded study characteristics and two key measures of diagnostic accuracy—positive predictive value (PPV) and sensitivity. We conducted descriptive analyses and quality assessments of included studies. Results: Thirteen eligible studies provided 13 estimates of PPV and five estimates of sensitivity. Twelve studies assessed hospital and/or death certificate-derived datasets; one evaluated a primary care dataset. All studies were from high income countries (UK, Europe, USA, Hong Kong). Study methods varied widely, but quality was generally good. PPV estimates ranged from 55–92% and sensitivities from 75–93%. The single (UK-based) study of primary care data reported a PPV of 85%. Conclusions: Diagnostic accuracy of routinely-collected health datasets is likely to be sufficient for identifying cases of MND in large-scale prospective epidemiological studies in high income country settings. Primary care datasets, particularly from countries with a widely-accessible national healthcare system, are potentially valuable data sources warranting further investigation

Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: a systematic review

Background: Motor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets. We systematically evaluated the accuracy of such datasets in identifying MND cases. Methods: We performed an electronic search of MEDLINE, EMBASE, Cochrane Library and Web of Science for studies published between 01/01/1990-16/11/2015 that compared MND cases identified in routinely-collected, coded datasets to a reference standard. We recorded study characteristics and two key measures of diagnostic accuracy—positive predictive value (PPV) and sensitivity. We conducted descriptive analyses and quality assessments of included studies. Results: Thirteen eligible studies provided 13 estimates of PPV and five estimates of sensitivity. Twelve studies assessed hospital and/or death certificate-derived datasets; one evaluated a primary care dataset. All studies were from high income countries (UK, Europe, USA, Hong Kong). Study methods varied widely, but quality was generally good. PPV estimates ranged from 55–92% and sensitivities from 75–93%. The single (UK-based) study of primary care data reported a PPV of 85%. Conclusions: Diagnostic accuracy of routinely-collected health datasets is likely to be sufficient for identifying cases of MND in large-scale prospective epidemiological studies in high income country settings. Primary care datasets, particularly from countries with a widely-accessible national healthcare system, are potentially valuable data sources warranting further investigation

Accuracy of identifying incident stroke cases from linked healthcare data in UK Biobank

Objective In UK Biobank (UKB), a large population-based prospective study, cases of many diseases are ascertained through linkage to routinely collected, coded national health datasets. We assessed the accuracy of these for identifying incident strokes. Methods In a regional UKB subpopulation (n = 17,249), we identified all participants with ≥1 code signifying a first stroke after recruitment (incident stroke-coded cases) in linked hospital admission, primary care, or death record data. Stroke physicians reviewed their full electronic patient records (EPRs) and generated reference standard diagnoses. We evaluated the number and proportion of cases that were true-positives (i.e., positive predictive value [PPV]) for all codes combined and by code source and type. Results Of 232 incident stroke-coded cases, 97% had EPR information available. Data sources were 30% hospital admission only, 39% primary care only, 28% hospital and primary care, and 3% death records only. While 42% of cases were coded as unspecified stroke type, review of EPRs enabled a pathologic type to be assigned in >99%. PPVs (95% confidence intervals) were 79% (73%–84%) for any stroke (89% for hospital admission codes, 80% for primary care codes) and 83% (74%–90%) for ischemic stroke. PPVs for small numbers of death record and hemorrhagic stroke codes were low but imprecise. Conclusions Stroke and ischemic stroke cases in UKB can be ascertained through linked health datasets with sufficient accuracy for many research studies. Further work is needed to understand the accuracy of death record and hemorrhagic stroke codes and to develop scalable approaches for better identifying stroke types

Reliability of intracerebral hemorrhage classification systems: a systematic review

BACKGROUND: Accurately distinguishing non-traumatic intracerebral hemorrhage (ICH) subtypes is important since they may have different risk factors, causal pathways, management, and prognosis. We systematically assessed the inter- and intra-rater reliability of ICH classification systems. METHODS: We sought all available reliability assessments of anatomical and mechanistic ICH classification systems from electronic databases and personal contacts until October 2014. We assessed included studies' characteristics, reporting quality and potential for bias; summarized reliability with kappa value forest plots; and performed meta-analyses of the proportion of cases classified into each subtype. SUMMARY OF REVIEW: We included 8 of 2152 studies identified. Inter- and intra-rater reliabilities were substantial to perfect for anatomical and mechanistic systems (inter-rater kappa values: anatomical 0.78-0.97 [six studies, 518 cases], mechanistic 0.89-0.93 [three studies, 510 cases]; intra-rater kappas: anatomical 0.80-1 [three studies, 137 cases], mechanistic 0.92-0.93 [two studies, 368 cases]). Reporting quality varied but no study fulfilled all criteria and none was free from potential bias. All reliability studies were performed with experienced raters in specialist centers. Proportions of ICH subtypes were largely consistent with previous reports suggesting that included studies are appropriately representative. CONCLUSIONS: Reliability of existing classification systems appears excellent but is unknown outside specialist centers with experienced raters. Future reliability comparisons should be facilitated by studies following recently published reporting guidelines

Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

OBJECTIVES: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. METHODS: This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. RESULTS: Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. CONCLUSION: Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs

Genetic associations with sporadic cerebral small vessel disease

Background: Cerebral small vessel disease (SVD) causes substantial cognitive, psychiatric and physical disabilities. Despite its common nature, SVD pathogenesis and molecular mechanisms remain poorly understood, and prevention and treatment are probably suboptimal. Identifying the genetic determinants of SVD will improve understanding and may help identify novel treatment targets. The aim of this thesis is to better understand genetic associations with SVD through investigating its pathological, radiological and clinical phenotypes. Methods: To unravel the genetic associations with SVD, I used three complementary approaches. First, I performed a systematic review looking at existing intracerebral haemorrhage (ICH) classification systems and their reliability, to help inform future studies of ICH genetics. Second, I performed a series of systematic reviews and meta-analyses, investigating associations between genetic polymorphisms and histopathologically confirmed cerebral amyloid angiopathy (CAA). Third, I performed meta-analyses of existing genome-wide datasets to determine associations of >1000 common single nucleotide polymorphisms (SNP) in the COL4A1/COL4A2 genomic region with clinico-radiological SVD phenotypes: ICH and its subtypes, ischaemic stroke and its subtypes, and white matter hyperintensities. Results: The reliability of existing ICH classification systems appeared excellent in eight studies conducted in specialist centres with experienced raters, although these existing systems have several limitations. In my systematic evaluation of CAA genetics, meta-analyses of 24 studies including 3520 participants showed robust evidence for a dose-dependent association between APOE ɛ4 and histopathological CAA. There was, however, no convincing association between APOE ɛ2 and presence of CAA in a meta-analysis of 11 studies including 1640 participants. Meta-analyses of five studies including 497 participants showed, contrary to an existing popular hypothesis, that while APOE 4 may increase the risk of developing severe CAA vasculopathy, there is no clear evidence to support a role of ɛ2. There were few data about the role of APOE in hereditary CAA, but in the three studies that had looked at this, there was no evidence for an association between APOE ɛ4 and CAA severity. There were too few studies and participants to draw firm conclusions about the effect of non-APOE ε2/ε3/ε4 genetic polymorphisms on CAA, but there were positive associations with TGF-β1, TOMM40 and CR1 genes in four studies. Finally, in my meta-analyses of the COL4A1/COL4A2 genomic region, three intronic SNPs in COL4A2 were associated with SVD phenotypes: significantly with deep ICH, and suggestively with lacunar ischaemic stroke and WMH. Conclusions: I have shown that while existing ICH classification systems appear to have very good reliability, further research is needed to determine their performance in different settings. For large population-based prospective studies of ICH genetics, anatomical systems are likely to be more feasible, scalable and appropriate, although they have limitations and will need to be further developed. Using systematic reviews and meta-analyses, I have confirmed a dose-related association between APOE ɛ4 and histopathological CAA, but also demonstrated that, despite popular acceptance, there is insufficient data to draw firm conclusions about the association with APOE ɛ2. I found some positive associations with CAA in other genes, which merit replication in further larger studies, and showed that there is currently insufficient data about the role of APOE in hereditary CAA. Finally, I identified a novel association between a locus in a known hereditary SVD gene – COL4A2 – and sporadic SVD. This highlights a new and successful approach for selecting candidate genes and can be expanded in future studies to include other known hereditary SVD genes

Accuracy of Electronic Health Record Data for Identifying Stroke Cases in Large-Scale Epidemiological Studies: A Systematic Review from the UK Biobank Stroke Outcomes Group

Long-term follow-up of population-based prospective studies is often achieved through linkages to coded regional or national health care data. Our knowledge of the accuracy of such data is incomplete. To inform methods for identifying stroke cases in UK Biobank (a prospective study of 503,000 UK adults recruited in middle-age), we systematically evaluated the accuracy of these data for stroke and its main pathological types (ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage), determining the optimum codes for case identification.We sought studies published from 1990-November 2013, which compared coded data from death certificates, hospital admissions or primary care with a reference standard for stroke or its pathological types. We extracted information on a range of study characteristics and assessed study quality with the Quality Assessment of Diagnostic Studies tool (QUADAS-2). To assess accuracy, we extracted data on positive predictive values (PPV) and-where available-on sensitivity, specificity, and negative predictive values (NPV).37 of 39 eligible studies assessed accuracy of International Classification of Diseases (ICD)-coded hospital or death certificate data. They varied widely in their settings, methods, reporting, quality, and in the choice and accuracy of codes. Although PPVs for stroke and its pathological types ranged from 6-97%, appropriately selected, stroke-specific codes (rather than broad cerebrovascular codes) consistently produced PPVs >70%, and in several studies >90%. The few studies with data on sensitivity, specificity and NPV showed higher sensitivity of hospital versus death certificate data for stroke, with specificity and NPV consistently >96%. Few studies assessed either primary care data or combinations of data sources.Particular stroke-specific codes can yield high PPVs (>90%) for stroke/stroke types. Inclusion of primary care data and combining data sources should improve accuracy in large epidemiological studies, but there is limited published information about these strategies