Handling missing items in the Exacerbations of Chronic Pulmonary Disease Tool (EXACT)

Within certain limits, missing items in the EXACT instrument can be imputed from the remaining answered items http://ow.ly/4mJQz

The 'top 100' drugs and classes in England An updated 'starter formulary' for trainee prescribers.

AIMS: Prescribing is a complex skill required of doctors and, increasingly, other healthcare professionals. Use of a personal formulary can help to develop this skill. In 2006-9, we developed a core list of the 100 most commonly prescribed drugs. Our aim in the present study was to update this 'starter formulary' to ensure its continued relevance for prescriber training. METHODS: We analysed large contemporary primary and secondary care datasets to identify the most frequently prescribed medicinal products. Items were classified into natural groups, broadly following their British National Formulary classification. The resulting drug groups were included in the core list if they comprised ≥0.1% prescriptions in both settings or ≥0.2-0.3% prescriptions in one setting. Drugs from emergency guidelines that did not qualify by prescribing frequency completed the list. RESULTS: Over 1 billion primary care items and approximately 1.8 million secondary care prescriptions were analysed. The updated list comprises 81 drug groups commonly prescribed in both settings; 6 from primary care; 7 from secondary care; and 6 from emergency guidelines. 88% of the formulary was unchanged. Notable changes include entry of newer anti-epileptics and dipeptidyl peptidase-4 inhibitors and exit of phenytoin and thiazolidinediones. CONCLUSIONS: The relative stability of the core drug list over 9 years and the current update ensure that learning based on this list remains relevant to practice. Trainee prescribers may be encouraged to use this 'starter formulary' to develop a sound basis of prescribing knowledge and skills that they can subsequently apply more widely

A retrospective analysis of 20,178 adult neurological infection admissions to United Kingdom critical care units from 2001 to 2020.

BACKGROUND: Neurological infection is an important cause of critical illness, yet little is known on the epidemiology of neurological infections requiring critical care. METHODS: We analysed data on all adults with proven or probable neurological infection admitted to UK (NHS) critical care units between 2001 and 2020 reported to the Intensive Care National Audit and Research Centre. Diagnoses, physiological variables, organ support and clinical outcomes were analysed over the whole period, and for consecutive 5-year intervals within it. Predictors of in-hospital mortality were identified using a backward stepwise regression model. RESULTS: We identified 20,178 critical care admissions for neurological infection. Encephalitis was the most frequent presentation to critical care, comprising 6725 (33.3%) of 20,178 cases. Meningitis- bacterial, viral or unspecified cases - accounted for 10,056 (49.8%) of cases. In-hospital mortality was high, at 3945/19,765 (20.0%) overall. Over the four consecutive 5-year periods, there were trends towards higher Glasgow Coma Scale scores on admission, longer critical care admissions (from median 4 [IQR 2-8] to 5 days [IQR 2-10]), and reduced in-hospital mortality (from 24.9 to 18.1%). We identified 12 independent predictors of in-hospital death which when used together showed good discrimination between patients who die and those who survive (AUC = 0.79). CONCLUSIONS: Admissions with neurological infection to UK critical care services are increasing and the mortality, although improving, remains high. To further improve outcomes from severe neurological infection, novel approaches to the evaluation of risk stratification, monitoring and management strategies are required

Factors affecting pharmacology learning in integrated PBL in diverse medical students: a mixed methods study.

INTRODUCTION: Problem-based learning (PBL) was introduced to address passive teaching limitations. However, it is not fully characterised as a teaching modality in pharmacology. The present study investigated the factors affecting pharmacology learning in an integrated PBL-based curriculum in diverse learners. METHODS: Year 1 undergraduate medical students from two cohorts at St. George's University of London and University of Nicosia, participated. Statistical analysis of pharmacology knowledge scores, at the beginning (pre-test) and end of the academic year (post-test), investigated readiness to benefit from PBL based on diverse student characteristics (educational background, age, gender, country of origin, ethnicity, native language, PBL experience). Focus groups/interviews and a survey investigated aspects of integrated PBL impacting learning in depth. RESULTS: Pre- and post-test scores were positively correlated. Students with biomedical sciences degrees performed better at the pharmacology pre- and post-tests, while post-graduate degree holders performed better only at the pre-test. Effect size was of moderate magnitude. However, progress in learning (post-test performance after controlling for pre-test scores) was unaffected. Qualitative analysis revealed three major themes: 1) PBL as a learning environment; 2) PBL as a learning environment in pharmacology; and 3) PBL as a learning environment and confidence in prescribing. Under theme one, skill development, knowledge acquisition through collaboration and self-directed learning, group dynamics and preferred teaching methods were discussed. Under theme two, contextual learning, depth of knowledge and material correctness were raised. Under theme 3, students expressed variability in prescribing confidence. They perceived that learning could be improved by better integration, further references earlier on, more lectures and PBL facilitators with greater content expertise. The survey findings were consistent with those from focus groups/interviews. CONCLUSION: Pharmacology learning in a PBL-based curriculum is facilitated by constructive, collaborative and contextual learning. While baseline pharmacology knowledge may be advantageous, the other aforementioned characteristics studied may not affect readiness to benefit from PBL. However, further instructional scaffolding is needed, for example through further resources, lectures and self-assessment. The results from our study can inform evidence-based curriculum reform to support student learning further. Addressing learning needs could ultimately contribute to reducing medication errors through effective training of future prescribers

FN3K expression in COPD: a potential comorbidity factor for cardiovascular disease.

INTRODUCTION: Cigarette smoking and oxidative stress are common risk factors for the multi-morbidities associated with chronic obstructive pulmonary disease (COPD). Elevated levels of advanced glycation endproducts (AGE) increase the risk of cardiovascular disease (CVD) comorbidity and mortality. The enzyme fructosamine-3-kinase (FN3K) reduces this risk by lowering AGE levels. METHODS: The distribution and expression of FN3K protein in lung tissues from stable COPD and control subjects, as well as an animal model of COPD, was assessed by immunohistochemistry. Serum FN3K protein and AGE levels were assessed by ELISA in patients with COPD exacerbations receiving metformin. Genetic variants within the FN3K and FN3K-RP genes were evaluated for associations with cardiorespiratory function in the Subpopulations and Intermediate Outcome Measures in COPD Study cohort. RESULTS: This pilot study demonstrates that FN3K expression in the blood and human lung epithelium is distributed at either high or low levels irrespective of disease status. The percentage of lung epithelial cells expressing FN3K was higher in control smokers with normal lung function, but this induction was not observed in COPD patients nor in a smoking model of COPD. The top five nominal FN3K polymorphisms with possible association to decreased cardiorespiratory function (p<0.008-0.02), all failed to reach the threshold (p<0.0028) to be considered highly significant following multi-comparison analysis. Metformin enhanced systemic levels of FN3K in COPD subjects independent of their high-expression or low-expression status. DISCUSSION: The data highlight that low and high FN3K expressors exist within our study cohort and metformin induces FN3K levels, highlighting a potential mechanism to reduce the risk of CVD comorbidity and mortality

Increased airway glucose increases airway bacterial load in hyperglycaemia.

Diabetes is associated with increased frequency of hospitalization due to bacterial lung infection. We hypothesize that increased airway glucose caused by hyperglycaemia leads to increased bacterial loads. In critical care patients, we observed that respiratory tract bacterial colonisation is significantly more likely when blood glucose is high. We engineered mutants in genes affecting glucose uptake and metabolism (oprB, gltK, gtrS and glk) in Pseudomonas aeruginosa, strain PAO1. These mutants displayed attenuated growth in minimal medium supplemented with glucose as the sole carbon source. The effect of glucose on growth in vivo was tested using streptozocin-induced, hyperglycaemic mice, which have significantly greater airway glucose. Bacterial burden in hyperglycaemic animals was greater than control animals when infected with wild type but not mutant PAO1. Metformin pre-treatment of hyperglycaemic animals reduced both airway glucose and bacterial load. These data support airway glucose as a critical determinant of increased bacterial load during diabetes

Pharmacology and therapeutic implications of current drugs for type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a global epidemic that poses a major challenge to health-care systems. Improving metabolic control to approach normal glycaemia (where practical) greatly benefits long-term prognoses and justifies early, effective, sustained and safety-conscious intervention. Improvements in the understanding of the complex pathogenesis of T2DM have underpinned the development of glucose-lowering therapies with complementary mechanisms of action, which have expanded treatment options and facilitated individualized management strategies. Over the past decade, several new classes of glucose-lowering agents have been licensed, including glucagon-like peptide 1 receptor (GLP-1R) agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors and sodium/glucose cotransporter 2 (SGLT2) inhibitors. These agents can be used individually or in combination with well-established treatments such as biguanides, sulfonylureas and thiazolidinediones. Although novel agents have potential advantages including low risk of hypoglycaemia and help with weight control, long-term safety has yet to be established. In this Review, we assess the pharmacokinetics, pharmacodynamics and safety profiles, including cardiovascular safety, of currently available therapies for management of hyperglycaemia in patients with T2DM within the context of disease pathogenesis and natural history. In addition, we briefly describe treatment algorithms for patients with T2DM and lessons from present therapies to inform the development of future therapies

Measurement and interpretation of same-sign W boson pair production in association with two jets in pp collisions at s = 13 TeV with the ATLAS detector

This paper presents the measurement of fducial and diferential cross sections for both the inclusive and electroweak production of a same-sign W-boson pair in association with two jets (W±W±jj) using 139 fb−1 of proton-proton collision data recorded at a centre-of-mass energy of √s = 13 TeV by the ATLAS detector at the Large Hadron Collider. The analysis is performed by selecting two same-charge leptons, electron or muon, and at least two jets with large invariant mass and a large rapidity diference. The measured fducial cross sections for electroweak and inclusive W±W±jj production are 2.92 ± 0.22 (stat.) ± 0.19 (syst.)fb and 3.38±0.22 (stat.)±0.19 (syst.)fb, respectively, in agreement with Standard Model predictions. The measurements are used to constrain anomalous quartic gauge couplings by extracting 95% confdence level intervals on dimension-8 operators. A search for doubly charged Higgs bosons H±± that are produced in vector-boson fusion processes and decay into a same-sign W boson pair is performed. The largest deviation from the Standard Model occurs for an H±± mass near 450 GeV, with a global signifcance of 2.5 standard deviations

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p