New technologies for diagnosing active TB: the VANTDET diagnostic accuracy study

BackgroundTuberculosis (TB) is a devastating disease for which new diagnostic tests are desperately needed.ObjectiveTo validate promising new technologies [namely whole-blood transcriptomics, proteomics, flow cytometry and quantitative reverse transcription-polymerase chain reaction (qRT-PCR)] and existing signatures for the detection of active TB in samples obtained from individuals with suspected active TB.DesignFour substudies, each of which used samples from the biobank collected as part of the interferon gamma release assay (IGRA) in the Diagnostic Evaluation of Active TB study, which was a prospective cohort of patients recruited with suspected TB.SettingSecondary care.ParticipantsAdults aged ≥ 16 years presenting as inpatients or outpatients at 12 NHS hospital trusts in London, Slough, Oxford, Leicester and Birmingham, with suspected active TB.InterventionsNew tests using genome-wide gene expression microarray (transcriptomics), surface-enhanced laser desorption ionisation time-of-flight mass spectrometry/liquid chromatography–mass spectrometry (proteomics), flow cytometry or qRT-PCR.Main outcome measuresArea under the curve (AUC), sensitivity and specificity were calculated to determine diagnostic accuracy. Positive and negative predictive values were calculated in some cases. A decision tree model was developed to calculate the incremental costs and quality-adjusted life-years of changing from current practice to using the novels tests.ResultsThe project, and four substudies that assessed the previously published signatures, measured each of the new technologies and performed a health economic analysis in which the best-performing tests were evaluated for cost-effectiveness. The diagnostic accuracy of the transcriptomic tests ranged from an AUC of 0.81 to 0.84 for detecting all TB in our cohort. The performance for detecting culture-confirmed TB or pulmonary TB was better than for highly probable TB or extrapulmonary tuberculosis (EPTB), but was not high enough to be clinically useful. None of the previously described serum proteomic signatures for active TB provided good diagnostic accuracy, nor did the candidate rule-out tests. Four out of six previously described cellular immune signatures provided a reasonable level of diagnostic accuracy (AUC = 0.78–0.92) for discriminating all TB from those with other disease and latent TB infection in human immunodeficiency virus-negative TB suspects. Two of these assays may be useful in the IGRA-positive population and can provide high positive predictive value. None of the new tests for TB can be considered cost-effective.LimitationsThe diagnostic performance of new tests among the HIV-positive population was either underpowered or not sufficiently achieved in each substudy.ConclusionsOverall, the diagnostic performance of all previously identified ‘signatures’ of TB was lower than previously reported. This probably reflects the nature of the cohort we used, which includes the harder to diagnose groups, such as culture-unconfirmed TB or EPTB, which were under-represented in previous cohorts.Future workWe are yet to achieve our secondary objective of deriving novel signatures of TB using our data sets. This was beyond the scope of this report. We recommend that future studies using these technologies target specific subtypes of TB, specifically those groups for which new diagnostic tests are required.FundingThis project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and NIHR partnership

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

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