Differences in guideline-recommended heart failure medication between Dutch heart failure clinics: an analysis of the CHECK-HF registry

Background: Heart failure (HF) is associated with poor prognosis, high morbidity and mortality. The prognosis can be optimised by guideline adherence, which also can be used as a benchmark of quality of care. The purpose of this study was to evaluate differences in use of HF medication between Dutch HF clinics. Methods: The current analysis was part of a cross-sectional registry of 10,910 chronic HF patients at 34 Dutch outpatient clinics in the period of 2013 until 2016 (CHECK-HF), and focused on the differences in prescription rates between the participating clinics in patients with heart failure with reduced ejection fraction (HFrEF). Results: A total of 8,360 HFrEF patients were included with a mean age of 72.3 ± 11.8 years (ranging between 69.1 ± 11.9 and 76.6 ± 10.0 between the clinics), 63.9% were men (ranging between 54.3 and 78.1%), 27.3% were in New York Heart Association (NYHA) class III/IV (ranging between 8.8 and 62.1%) and the average estimated glomerular filtration rate (eGFR) was 59.6 ± 24.6 ml/min (ranging between 45.7 ± 23.5 and 97.1 ± 16.5). The prescription rates ranged from 58.9–97.4% for beta blockers (p < 0.01), 61.9–97.1% for renin-angiotensin system (RAS) inhibitors (p < 0.01), 29.9–86.8% for mineralocorticoid receptor antagonists (MRAs) (p < 0.01), 0.0–31.3% for ivabradine (p < 0.01) and 64.9–100.0% for diuretics (p < 0.01). Also, the percentage of patients who received the target dose differed significantly, 5.9–29.1% for beta blockers (p < 0.01), 18.4–56.1% for RAS inhibitors (p < 0.01) and 13.2–60.6% for MRAs (p < 0.01). Conclusions: The prescription rates and prescribed dosages of guideline-recommended medication differed significantly between HF outpatient clinics in the Netherlands, not fully explained by differences in patient profiles

Job Resources and Matching Active Coping Styles as Moderators of the Longitudinal Relation Between Job Demands and Job Strain

Background: Only in a few longitudinal studies it has been examined whether job resources should be matched to job demands to show stress-buffering effects of job resources (matching hypothesis), while there are no empirical studies in which the moderating effect of matching personal characteristics on the stress-buffering effect of job resources has been examined. Purpose: In this study, both the matching hypothesis and the moderating effect of matching active coping styles were examined with respect to the longitudinal relation between job demands, job resources, and job strain.Method: The study group consisted of 317 beginning teachers from Belgium. The two-wave survey data with a 1-year time lag were analyzed by means of structural equation modeling and multiple group analyses. Results: Data did not support the matching hypothesis. In addition, no support was found for the moderating effect of specific active coping styles, irrespective of the level of match. Conclusion: To show stress-buffering effects of job resources, it seems to make no difference whether or not specific types of job demands and job resources are matched, and whether or not individual differences in specific active coping styles are taken into account

Acute seizure risk in patients with encephalitis: development and validation of clinical prediction models from two independent prospective multicentre cohorts

ObjectiveIn patients with encephalitis, the development of acute symptomatic seizures is highly variable, but when present is associated with a worse outcome. We aimed to determine the factors associated with seizures in encephalitis and develop a clinical prediction model.MethodsWe analysed 203 patients from 24 English hospitals (2005–2008) (Cohort 1). Outcome measures were seizures prior to and during admission, inpatient seizures and status epilepticus. A binary logistic regression risk model was converted to a clinical score and independently validated on an additional 233 patients from 31 UK hospitals (2013–2016) (Cohort 2).ResultsIn Cohort 1, 121 (60%) patients had a seizure including 103 (51%) with inpatient seizures. Admission Glasgow Coma Scale (GCS) ≤8/15 was predictive of subsequent inpatient seizures (OR (95% CI) 5.55 (2.10 to 14.64), p&lt;0.001), including in those without a history of prior seizures at presentation (OR 6.57 (95% CI 1.37 to 31.5), p=0.025).A clinical model of overall seizure risk identified admission GCS along with aetiology (autoantibody-associated OR 11.99 (95% CI 2.09 to 68.86) and Herpes simplex virus 3.58 (95% CI 1.06 to 12.12)) (area under receiver operating characteristics curve (AUROC) =0.75 (95% CI 0.701 to 0.848), p&lt;0.001). The same model was externally validated in Cohort 2 (AUROC=0.744 (95% CI 0.677 to 0.811), p&lt;0.001). A clinical scoring system for stratifying inpatient seizure risk by decile demonstrated good discrimination using variables available on admission; age, GCS and fever (AUROC=0.716 (95% CI 0.634 to 0.798), p&lt;0.001) and once probable aetiology established (AUROC=0.761 (95% CI 0.6840.839), p&lt;0.001).ConclusionAge, GCS, fever and aetiology can effectively stratify acute seizure risk in patients with encephalitis. These findings can support the development of targeted interventions and aid clinical trial design for antiseizure medication prophylaxis.</jats:sec

Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

BACKGROUND: Medical schools differ, particularly in their teaching, but it is unclear whether such differences matter, although influential claims are often made. The Medical School Differences (MedDifs) study brings together a wide range of measures of UK medical schools, including postgraduate performance, fitness to practise issues, specialty choice, preparedness, satisfaction, teaching styles, entry criteria and institutional factors. METHOD: Aggregated data were collected for 50 measures across 29 UK medical schools. Data include institutional history (e.g. rate of production of hospital and GP specialists in the past), curricular influences (e.g. PBL schools, spend per student, staff-student ratio), selection measures (e.g. entry grades), teaching and assessment (e.g. traditional vs PBL, specialty teaching, self-regulated learning), student satisfaction, Foundation selection scores, Foundation satisfaction, postgraduate examination performance and fitness to practise (postgraduate progression, GMC sanctions). Six specialties (General Practice, Psychiatry, Anaesthetics, Obstetrics and Gynaecology, Internal Medicine, Surgery) were examined in more detail. RESULTS: Medical school differences are stable across time (median alpha = 0.835). The 50 measures were highly correlated, 395 (32.2%) of 1225 correlations being significant with p < 0.05, and 201 (16.4%) reached a Tukey-adjusted criterion of p < 0.0025. Problem-based learning (PBL) schools differ on many measures, including lower performance on postgraduate assessments. While these are in part explained by lower entry grades, a surprising finding is that schools such as PBL schools which reported greater student satisfaction with feedback also showed lower performance at postgraduate examinations. More medical school teaching of psychiatry, surgery and anaesthetics did not result in more specialist trainees. Schools that taught more general practice did have more graduates entering GP training, but those graduates performed less well in MRCGP examinations, the negative correlation resulting from numbers of GP trainees and exam outcomes being affected both by non-traditional teaching and by greater historical production of GPs. Postgraduate exam outcomes were also higher in schools with more self-regulated learning, but lower in larger medical schools. A path model for 29 measures found a complex causal nexus, most measures causing or being caused by other measures. Postgraduate exam performance was influenced by earlier attainment, at entry to Foundation and entry to medical school (the so-called academic backbone), and by self-regulated learning. Foundation measures of satisfaction, including preparedness, had no subsequent influence on outcomes. Fitness to practise issues were more frequent in schools producing more male graduates and more GPs. CONCLUSIONS: Medical schools differ in large numbers of ways that are causally interconnected. Differences between schools in postgraduate examination performance, training problems and GMC sanctions have important implications for the quality of patient care and patient safety

The Analysis of Teaching of Medical Schools (AToMS) survey: an analysis of 47,258 timetabled teaching events in 25 UK medical schools relating to timing, duration, teaching formats, teaching content, and problem-based learning

BACKGROUND: What subjects UK medical schools teach, what ways they teach subjects, and how much they teach those subjects is unclear. Whether teaching differences matter is a separate, important question. This study provides a detailed picture of timetabled undergraduate teaching activity at 25 UK medical schools, particularly in relation to problem-based learning (PBL). METHOD: The Analysis of Teaching of Medical Schools (AToMS) survey used detailed timetables provided by 25 schools with standard 5-year courses. Timetabled teaching events were coded in terms of course year, duration, teaching format, and teaching content. Ten schools used PBL. Teaching times from timetables were validated against two other studies that had assessed GP teaching and lecture, seminar, and tutorial times. RESULTS: A total of 47,258 timetabled teaching events in the academic year 2014/2015 were analysed, including SSCs (student-selected components) and elective studies. A typical UK medical student receives 3960 timetabled hours of teaching during their 5-year course. There was a clear difference between the initial 2 years which mostly contained basic medical science content and the later 3 years which mostly consisted of clinical teaching, although some clinical teaching occurs in the first 2 years. Medical schools differed in duration, format, and content of teaching. Two main factors underlay most of the variation between schools, Traditional vs PBL teaching and Structured vs Unstructured teaching. A curriculum map comparing medical schools was constructed using those factors. PBL schools differed on a number of measures, having more PBL teaching time, fewer lectures, more GP teaching, less surgery, less formal teaching of basic science, and more sessions with unspecified content. DISCUSSION: UK medical schools differ in both format and content of teaching. PBL and non-PBL schools clearly differ, albeit with substantial variation within groups, and overlap in the middle. The important question of whether differences in teaching matter in terms of outcomes is analysed in a companion study (MedDifs) which examines how teaching differences relate to university infrastructure, entry requirements, student perceptions, and outcomes in Foundation Programme and postgraduate training

The Analysis of Teaching of Medical Schools (AToMS) survey: an analysis of 47,258 timetabled teaching events in 25 UK medical schools relating to timing, duration, teaching formats, teaching content, and problem-based learning

Background What subjects UK medical schools teach, what ways they teach subjects, and how much they teach those subjects is unclear. Whether teaching differences matter is a separate, important question. This study provides a detailed picture of timetabled undergraduate teaching activity at 25 UK medical schools, particularly in relation to problem-based learning (PBL). Method The Analysis of Teaching of Medical Schools (AToMS) survey used detailed timetables provided by 25 schools with standard 5-year courses. Timetabled teaching events were coded in terms of course year, duration, teaching format, and teaching content. Ten schools used PBL. Teaching times from timetables were validated against two other studies that had assessed GP teaching and lecture, seminar, and tutorial times. Results A total of 47,258 timetabled teaching events in the academic year 2014/2015 were analysed, including SSCs (student-selected components) and elective studies. A typical UK medical student receives 3960 timetabled hours of teaching during their 5-year course. There was a clear difference between the initial 2 years which mostly contained basic medical science content and the later 3 years which mostly consisted of clinical teaching, although some clinical teaching occurs in the first 2 years. Medical schools differed in duration, format, and content of teaching. Two main factors underlay most of the variation between schools, Traditional vs PBL teaching and Structured vs Unstructured teaching. A curriculum map comparing medical schools was constructed using those factors. PBL schools differed on a number of measures, having more PBL teaching time, fewer lectures, more GP teaching, less surgery, less formal teaching of basic science, and more sessions with unspecified content. Discussion UK medical schools differ in both format and content of teaching. PBL and non-PBL schools clearly differ, albeit with substantial variation within groups, and overlap in the middle. The important question of whether differences in teaching matter in terms of outcomes is analysed in a companion study (MedDifs) which examines how teaching differences relate to university infrastructure, entry requirements, student perceptions, and outcomes in Foundation Programme and postgraduate training

The KNOMAD Methodology for Integration of Multi-Disciplinary Engineering Knowledge within Aerospace Production

The paper is associated with the integration of multi-disciplinary knowledge within a Knowledge Based Engineering (KBE)-enabled design framework. To support this integration effort, the KNOMAD methodology has been devised. KNOMAD stands for Knowledge Optimized Manufacture And Design and is a methodology for the analytical utilisation of multi-disciplinary engineering knowledge within design. The KNOMAD acronym can also be used to highlight KNOMAD’s formalized process of: (K)nowledge capture; (N)ormalisation; (O)rganizations; (M)odelling; (A)nalysis; and (D)elivery. The main contribution of the paper is to highlight the development of the KNOMAD methodology and to substantiate its individual steps with sufficient detail to support the application of KNOMAD in practice.Control & OperationsAerospace Engineerin