Medical Outpatients Department

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Research outputs from the Medical Outpatients Department at the RD&E.

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    Metabolic adverse events associated with systemic corticosteroid therapy-a systematic review and meta-analysis
    (BMJ, 2022-12-22) Kulkarni, S.; Durham, H.; Glover, L.; Ather, O.; Phillips, V.; Nemes, S.; Cousens, L.; Blomgran, P.; Ambery, P.
    OBJECTIVES: To assess the risk of new-onset or worsening hyperglycaemia, hypertension, weight gain and hyperlipidaemia with systemic corticosteroid therapy (CST) as reported in published randomised control trial (RCT) studies. DATA SOURCES: Literature search using MEDLINE, EMBASE, Cochrane library, Web of Science and Scopus STUDY ELIGIBILITY CRITERIA: Published articles on results of RCT with a systemic CST arm with numerical data presented on adverse effect (AE). PARTICIPANTS AND INTERVENTIONS: Reports of hyperglycaemia, hypertension, weight gain and hyperlipidaemia associated with systemic CST in patients or healthy volunteer's ≥17 years of age. STUDY APPRAISAL METHODS: Risk of bias tool, assessment at the level of AE and key study characteristics. RESULTS: A total of 5446 articles were screened to include 118 studies with 152 systemic CST arms (total participants=17 113 among which 8569 participants treated with CST). Pooled prevalence of hyperglycaemia in the CST arms within the studies was 10% (95% CI 7% to 14%), with the highest prevalence in respiratory illnesses at 22% (95% CI 9% to 35%). Pooled prevalence of severe hyperglycaemia, hypertension, weight gain and hyperlipidaemia within the corticosteroid arms was 5% (95% CI 2% to 9%), 6% (95% CI 4% to 8%), 13% (95% CI 8% to 18%), 8% (95% CI 4% to 17%), respectively. CST was significantly associated hyperglycaemia, hypertension and weight gain as noted in double-blinded placebo-controlled parallel-arms studies: OR of 2.13 (95% CI 1.66 to 2.72), 1.68 (95% CI 0.96 to 2.95) and 5.20 (95% CI 2.10 to 12.90), respectively. Intravenous therapy posed higher risk than oral therapy: OR of 2.39 (95% CI 1.16 to 4.91). LIMITATIONS: There was significant heterogeneity in the AE definitions and quality of AE reporting in the primary studies and patient populations in the studies. The impact of cumulative dose effect on incidental AE could not be calculated. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Systemic CST use is associated with increased risk of metabolic AEs, which differs for each disease group and route of administration. PROSPERO REGISTRATION NUMBER: CRD42020161270.
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    COVID-19 pandemic interim Foundation Year 1 post and confidence in core skills and competencies: a longitudinal survey
    (BMJ, 2022-11-02) Gatti, C. A.; Parker-Conway, K.; Okorie, M.
    OBJECTIVES: The interim Foundation Year 1 (FiY1) post was created in response to the COVID-19 pandemic to help bolster the workforce and manage increased clinical pressures. This study aimed to assess the impact of the FiY1 post on medical graduates' self-reported confidence in common tasks, core skills, competencies and procedures prior to starting FY1, as a measure of increasing preparedness for practice. SETTING: A longitudinal survey was performed at a tertiary teaching hospital in the South East of England. FiY1 posts ran from June to July 2020. PARTICIPANTS: Questionnaires were sent to 122 medical graduates from a single medical school (recipients included FiY1s and non-FiY1s) and to 69 FiY1s at a single Teaching Hospital NHS Trust, irrespective of medical school attended. Initial and follow-up questionnaires had 86 and 62 respondents, respectively. Of these, 39 graduates were matched; 26 were FiY1s and 13 non-FiY1s. The 39 matched results were analysed. PRIMARY OUTCOME MEASURES: Confidence levels in common FY1 tasks, core procedures and competencies were gathered before and after the FiY1 post through online questionnaires. Change in confidence comparing FiY1s and non-FiY1s was measured and analysed using linear regression. RESULTS: On a 5-point scale, the FiY1 post increased overall confidence in starting FY1 by 0.62 (95% CI 0.072 to 1.167, p=0.028). The FiY1 post increased confidence in performing venepuncture by 0.32 (95% CI 0.011 to 0.920, p=0.045), performing intravenous cannulation by 0.48 (95% CI 0.030 to 1.294, p=0.041) and recognising, assessing and initiating the management of the acutely ill patient by 0.32 (95% CI 0.030 to 1.301, p=0.041). CONCLUSIONS: The COVID-19 pandemic FiY1 post improved confidence in core skills and competencies. These findings may help guide future educational interventions in conjunction with further larger scale studies, ultimately aiding to bridge the transition gap between being a medical student and a doctor.
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    Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
    (Nature, 2022-05-01) Vasey, B.; Nagendran, M.; Campbell, B.; Clifton, D. A.; Collins, G. S.; Denaxas, S.; Denniston, A. K.; Faes, L.; Geerts, B.; Ibrahim, M.; Liu, X.; Mateen, B. A.; Mathur, P.; McCradden, M. D.; Morgan, L.; Ordish, J.; Rogers, C.; Saria, S.; Ting, D. S. W.; Watkinson, P.; Weber, W.; Wheatstone, P.; McCulloch, P.
    A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.
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    Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
    (Nature, 2022-08-12) Vasey, B.; Nagendran, M.; Campbell, B.; Clifton, D. A.; Collins, G. S.; Denaxas, S.; Denniston, A. K.; Faes, L.; Geerts, B.; Ibrahim, M.; Liu, X.; Mateen, B. A.; Mathur, P.; McCradden, M. D.; Morgan, L.; Ordish, J.; Rogers, C.; Saria, S.; Ting, D. S. W.; Watkinson, P.; Weber, W.; Wheatstone, P.; McCulloch, P.