Publications
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Ravindran S, Shaw T, Broughton R, et al., 2021, THE JAG SURVEY OF UK ENDOSCOPY SERVICES: RESULTS FROM THE 2019 CENSUS, Publisher: BMJ PUBLISHING GROUP, Pages: A76-A76, ISSN: 0017-5749
Ruban A, Glaysher M, Miras A, et al., 2021, SAFETY PROFILE OF THE DUODENAL-JEJUNAL BYPASS LINER (ENDOBARRIER): A MULTICENTRE RANDOMISED CONTROL TRIAL, Publisher: BMJ PUBLISHING GROUP, Pages: A170-A170, ISSN: 0017-5749
Ravindran S, Bassett P, Shaw T, et al., 2021, Improving safety and reducing error in endoscopy (ISREE): a survey of UK services., Frontline Gastroenterol, Vol: 12, Pages: 593-600, ISSN: 2041-4137
BACKGROUND: The Joint Advisory Group on Gastrointestinal Endoscopy (JAG) 'Improving Safety and Reducing Error in Endoscopy' (ISREE) strategy was developed in 2018. In line with the strategy, a survey was conducted within the JAG census in 2019 to gain further insights and understanding of key safety-related areas within UK endoscopy. METHODS: Questions were developed using the ISREE strategy as a guide and adapted by key JAG stakeholders. They were incorporated into the 2019 JAG census of UK endoscopy services. Quantitative and qualitative statistical methods were employed to analyse the results. RESULTS: There was a 68% response rate. There was regional variability in the provision of out-of-hours GIB services (p<0.001). Across 1 month, 1535 incidents were reported across all services. There was a significantly higher proportion of reported incidents in acute services compared with others (p<0.001). Technical and training incidents were likely to be reported significantly differently to all other incident types. 74% of services have an endoscopy-specific sedation policy and 42% have a named sedation or anaesthetic lead for endoscopy. Services highlighted a desire for more anaesthetic-supported lists. Only 66% of services stated they have an effective strategy for supporting upskilling of endoscopists. Across acute services, 56% have access to human factors and endoscopic non-technical skills (ENTS) training. Patient feedback is used in several ways to improve services, develop training and promote shared learning among endoscopy users. CONCLUSIONS: The census provides a benchmark for key safety-related characteristics of endoscopy services. These results have highlighted key areas to develop, guided by the ISREE strategy.
Sounderajah V, Patel V, Varatharajan L, et al., 2020, Are disruptive innovations recognised in the healthcare literature? A systematic review, BMJ Innovations, Vol: 7, Pages: 208-216, ISSN: 2055-8074
The study aims to conduct a systematic review to characterise the spread and use of the concept of ‘disruptive innovation’ within the healthcare sector. We aim to categorise references to the concept over time, across geographical regions and across prespecified healthcare domains. From this, we further aim to critique and challenge the sector-specific use of the concept. PubMed, Medline, Embase, Global Health, PsycINFO, Maternity and Infant Care, and Health Management Information Consortium were searched from inception to August 2019 for references pertaining to disruptive innovations within the healthcare industry. The heterogeneity of the articles precluded a meta-analysis, and neither quality scoring of articles nor risk of bias analyses were required. 245 articles that detailed perceived disruptive innovations within the health sector were identified. The disruptive innovations were categorised into seven domains: basic science (19.2%), device (12.2%), diagnostics (4.9%), digital health (21.6%), education (5.3%), processes (17.6%) and technique (19.2%). The term has been used with increasing frequency annually and is predominantly cited in North American (78.4%) and European (15.2%) articles. The five most cited disruptive innovations in healthcare are ‘omics’ technologies, mobile health applications, telemedicine, health informatics and retail clinics. The concept ‘disruptive innovation’ has diffused into the healthcare industry. However, its use remains inconsistent and the recognition of disruption is obscured by other types of innovation. The current definition does not accommodate for prospective scouting of disruptive innovations, a likely hindrance to policy makers. Redefining disruptive innovation within the healthcare sector is therefore crucial for prospectively identifying cost-effective innovations.
Denning M, Ashrafian H, 2020, Leading for innovation, BMJ Leader, Vol: 4, Pages: 171-173, ISSN: 2398-631X
Innovation is any process that creates values for patients, staff, organisations, communities or government. In the context of rising healthcare demand and limited resources, innovation is necessary to facilitate the ongoing delivery of high-quality care. Healthcare is a complex industry with many characteristics that impact the innovation process. Historically, the focus of innovation has been new technological products rather than reimagining how routine care is delivered. However, rethinking how common, less complex conditions are managed, is likely to represent a greater opportunity to capture value going forwards. Strategy is necessary due to limited resources. Key elements include involves setting priorities, measuring improvement, cultivating an innovative culture and managing trade-offs. Innovation can solve a well-recognised problem or propose solution to a problem that is not as well recognized: this is classified as demand-pull or supply-push. Technological solutions can exist in as hard (equipment) or soft (processes, procedures, improvement strategies). Innovation can be classified by the degree to which it changes current technology and/or business models. The resultant classification includes: radical, disruptive, routine, or architectural innovation. Due to the complexity of healthcare, innovations do not always have predictable effects, and require testing. Successful innovations can be spread through diffusion and dissemination.
Clarke J, Flott K, Crespo R, et al., 2020, Assessing the Safety of Home Oximetry for Covid-19: A multi-site retrospective observational study
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objectives</jats:title><jats:p>To determine the safety and effectiveness of home oximetry monitoring pathways safe for Covid-19 patients in the English NHS</jats:p></jats:sec><jats:sec><jats:title>Design</jats:title><jats:p>This was a retrospective, multi-site, observational study of home oximetry monitoring for patients with suspected or proven Covid-19</jats:p></jats:sec><jats:sec><jats:title>Setting</jats:title><jats:p>This study analysed patient data from four Covid-19 home oximetry pilot sites in North West London, Slough, South Tees and Watford across primary and secondary care settings.</jats:p></jats:sec><jats:sec><jats:title>Participants</jats:title><jats:p>1338 participants were enrolled in a home oximetry programme at one of the four pilot sites. Participants were excluded if primary care data and oxygen saturations are rest at enrolment were not available. 908 participants were included in the analysis.</jats:p></jats:sec><jats:sec><jats:title>Interventions</jats:title><jats:p>Home oximetry monitoring was provided to participants with a known or suspected diagnosis of Covid-19. Participants were enrolled following attendance to accident and emergency departments, hospital admission or referral through primary care services.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Of 908 patients enrolled into four different Covid-19 home oximetry programmes in England, 771 (84.9%) had oxygen saturations at rest of 95% or more, and 320 (35.2%) were under 65 years of age and without comorbidities. 52 (5.7%) presented to hospital and 28 (3.1%) died following enrolment, of which 14 (50%) had Covid-19 as a named cause of death. All-cause mortality was significantly higher in patien
Joshi M, Ashrafian H, Khan S, et al., 2020, Sepsis, The Lancet, Vol: 396, Pages: 1805-1805, ISSN: 0140-6736
Ashrafian H, Sounderajah V, Glen R, et al., 2020, Metabolomics - the stethoscope for the 21st century, Medical Principles and Practice, Vol: 30, Pages: 301-310, ISSN: 1011-7571
Metabolomics offers systematic identification and quantification of all metabolic products from the human body. This field could provide clinicians with new sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualised level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice and discuss the translational challenges that the field faces. We searched PubMed, Medline and Embase for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and NMR based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use centre around scalability of data interpretation, standardisation of sample handling practice and e-infrastructure. Routine utilisation of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.
Iqbal FM, Joshi M, Khan S, et al., 2020, Implementation of Wearable Sensors and Digital Alerting Systems in Secondary Care: Protocol for a Real-World Prospective Study Evaluating Clinical Outcomes (Preprint)
<sec> <title>BACKGROUND</title> <p>Advancements in wearable sensors have caused a resurgence in their use, particularly because their miniaturization offers ambulatory advantages while performing continuous vital sign monitoring. Digital alerts can be generated following early recognition of clinical deterioration through breaches of set parameter thresholds, permitting earlier intervention. However, a systematic real-world evaluation of these alerting systems has yet to be conducted, and their efficacy remains unknown.</p> </sec> <sec> <title>OBJECTIVE</title> <p>The aim of this study is to implement wearable sensors and digital alerting systems in acute general wards to evaluate the resultant clinical outcomes.</p> </sec> <sec> <title>METHODS</title> <p>Participants on acute general wards will be screened and recruited into a trial with a pre-post implementation design. In the preimplementation phase, the SensiumVitals monitoring system, which continuously measures temperature, heart, and respiratory rates, will be used for monitoring alongside usual care. In the postimplementation phase, alerts will be generated from the SensiumVitals system when pre-established thresholds for vital parameters have been crossed, requiring acknowledgement from health care staff; subsequent clinical outcomes will be analyzed.</p> </sec> <sec> <title>RESULTS</title> <p>Enrolment is currently underway, having started in September 2017, and is anticipated to end shortly. Data analysis is expected to be completed in 2021.</p> </sec&
Aggarwal R, Farag S, Martin G, et al., 2020, Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey (Preprint)
<sec> <title>BACKGROUND</title> <p>Considerable research is being conducted as to how artificial intelligence (AI) can be effectively applied to health care. However, for the successful implementation of AI, large amounts of health data are required for training and testing algorithms. As such, there is a need to understand the perspectives and viewpoints of patients regarding the use of their health data in AI research.</p> </sec> <sec> <title>OBJECTIVE</title> <p>We surveyed a large sample of patients for identifying current awareness regarding health data research, and for obtaining their opinions and views on data sharing for AI research purposes, and on the use of AI technology on health care data.</p> </sec> <sec> <title>METHODS</title> <p>A cross-sectional survey with patients was conducted at a large multisite teaching hospital in the United Kingdom. Data were collected on patient and public views about sharing health data for research and the use of AI on health data.</p> </sec> <sec> <title>RESULTS</title> <p>A total of 408 participants completed the survey. The respondents had generally low levels of prior knowledge about AI. Most were comfortable with sharing health data with the National Health Service (NHS) (318/408, 77.9%) or universities (268/408, 65.7%), but far fewer with commercial organizations such as technology companies (108/408, 26.4%). The majority endorsed AI research on health care data (357/408, 87.4%) and health care imaging (353/408, 86.4%) in a university setting, provided that concerns about privacy, reid
Suwa Y, Joshi M, Poynter L, et al., 2020, Obese patients and robotic colorectal surgery: systematic review and meta-analysis, BJS Open, Vol: 4, Pages: 1042-1053, ISSN: 2474-9842
BackgroundObesity is a major health problem, demonstrated to double the risk of colorectal cancer. The benefits of robotic colorectal surgery in obese patients remain largely unknown. This meta‐analysis evaluated the clinical and pathological outcomes of robotic colorectal surgery in obese and non‐obese patients.MethodsMEDLINE, Embase, Global Health, Healthcare Management Information Consortium (HMIC) and Midwives Information and Resources Service (MIDIRS) databases were searched on 1 August 2018 with no language restriction. Meta‐analysis was performed according to PRISMA guidelines. Obese patients (BMI 30 kg/m2 or above) undergoing robotic colorectal cancer resections were compared with non‐obese patients. Included outcome measures were: operative outcomes (duration of surgery, conversion to laparotomy, blood loss), postoperative complications, hospital length of stay and pathological outcomes (number of retrieved lymph nodes, positive circumferential resection margins and length of distal margin in rectal surgery).ResultsA total of 131 full‐text articles were reviewed, of which 12 met the inclusion criteria and were included in the final analysis. There were 3166 non‐obese and 1420 obese patients. A longer duration of surgery was documented in obese compared with non‐obese patients (weighted mean difference −21·99 (95 per cent c.i. −31·52 to −12·46) min; P < 0·001). Obese patients had a higher rate of conversion to laparotomy than non‐obese patients (odds ratio 1·99, 95 per cent c.i. 1·54 to 2·56; P < 0·001). Blood loss, postoperative complications, length of hospital stay and pathological outcomes were not significantly different in obese and non‐obese patients.ConclusionRobotic surgery in obese patients results in a significantly longer duration of surgery and higher conversion rates than in non‐obese patients. Further studies should focus on bette
Jiwa N, Gandhewar R, Chauhan H, et al., 2020, Diagnostic accuracy of nipple aspirate fluid cytology in asymptomatic patients: a meta-analysis and systematic review of the literature, Annals of Surgical Oncology, Vol: 28, Pages: 3751-3760, ISSN: 1068-9265
PURPOSE: To calculate the diagnostic accuracy of nipple aspirate fluid (NAF) cytology. BACKGROUND: Evaluation of NAF cytology in asymptomatic patients conceptually offers a non-invasive method for either screening for breast cancer or else predicting or stratifying future cancer risk. METHODS: Studies were identified by performing electronic searches up to August 2019. A meta-analysis was conducted to attain an overall pooled sensitivity and specificity of NAF for breast cancer detection. RESULTS: A search through 938 studies yielded a total of 19 studies. Overall, 9308 patients were examined, with cytology results from 10,147 breasts [age (years), mean ± SD = 49.73 ± 4.09 years]. Diagnostic accuracy meta-analysis of NAF revealed a pooled specificity of 0.97 (95% CI 0.97-0.98), and sensitivity of 0.64 (95% CI 0.62-0.66). CONCLUSIONS: The diagnostic accuracy of nipple smear cytology is limited by poor sensitivity. If nipple fluid assessment is to be used for diagnosis, then emerging technologies for fluid biomarker analysis must supersede the current diagnostic accuracy of NAF cytology.
Loo RL, Chan Q, Antti H, et al., 2020, Manuscript Strategy for improved characterisation of human metabolic phenotypes using a COmbined Multiblock Principal components Analysis with Statistical Spectroscopy (COMPASS), Bioinformatics, Vol: 36, Pages: 5229-5236, ISSN: 1367-4803
MOTIVATION: Large-scale population omics data can provide insight into associations between gene-environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets. RESULTS: Here we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multiblock Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is achieved by implementing statistical correlation spectroscopy (STOCSY) on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset is used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS thus provides an efficient semi-automated approach for screening population datasets. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/cheminfo/COMPASS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Ruban A, Glaysher MA, Miras AD, et al., 2020, A duodenal sleeve bypass device added to intensive medical therapy for obesity with type 2 diabetes: a RCT, Efficacy and Mechanism Evaluation, Vol: 7, Pages: 1-130, ISSN: 2050-4365
BackgroundThe EndoBarrier® (GI Dynamics Inc., Boston, MA, USA) is an endoluminal duodenal–jejunal bypass liner developed for the treatment of patients with obesity and type 2 diabetes mellitus. Meta-analyses of its effects on glycaemia and weight have called for larger randomised controlled trials with longer follow-up.ObjectivesThe primary objective was to compare intensive medical therapy with a duodenal–jejunal bypass liner with intensive medical therapy without a duodenal–jejunal bypass liner, comparing effectiveness on the metabolic state as defined by the International Diabetes Federation as a glycated haemoglobin level reduction of ≥ 20%. The secondary objectives were to compare intensive medical therapy with a duodenal–jejunal bypass liner with intensive medical therapy without a duodenal–jejunal bypass liner, comparing effectiveness on the metabolic state as defined by the International Diabetes Federation as a glycated haemoglobin level of < 42 mmol/mol, blood pressure of < 135/85 mmHg, and the effectiveness on total body weight loss. Additional secondary outcomes were to investigate the cost-effectiveness and mechanism of action of the effect of a duodenal–jejunal bypass liner on brain reward system responses, insulin sensitivity, eating behaviour and metabonomics.DesignA multicentre, open-label, randomised controlled trial.SettingImperial College Healthcare NHS Trust and University Hospital Southampton NHS Foundation Trust.ParticipantsPatients aged 18–65 years with a body mass index of 30–50 kg/m2 and with inadequately controlled type 2 diabetes mellitus who were on oral glucose-lowering medications.InterventionsParticipants were randomised equally to receive intensive medical therapy alongside a duodenal–jejunal bypass liner device (n = 85) or intensive medical therapy alone for 12 months (n = 85), and were followed up
Glover B, Teare J, Patel N, 2020, The endoscopic predictors of H. pylori status: a meta-analysis of diagnostic performance., Therapeutic Advances in Gastrointestinal Endoscopy, Vol: 13, Pages: 1-19, ISSN: 1179-5522
ObjectiveThe endoscopic findings associated with H. pylori naïve status, current infection or past infection is an area of ongoing interest. Previous studies have investigated parameters with potential diagnostic value. The aim of this study was to perform meta-analysis of the available literature to validate the diagnostic accuracy of mucosal features proposed in the Kyoto classification.Data SourcesThe databases of Medline and Embase, clinicaltrials.gov and the Cochrane library were systematically searched for relevant studies from October 1999 to October 2019.MethodsA bivariate random effects model was used to produce pooled diagnostic accuracy calculations for each of the studied endoscopic findings. Diagnostic odds ratios and sensitivity and specificity characteristics were calculated to identify significant predictors of H. pylori status.ResultsMeta-analysis included 4,380 patients in 15 studies. The most significant predictor of an H. pylori naïve status was a regular arrangement of collecting venules (RAC). (DOR 55.0, Sensitivity 78.3%, Specificity 93.8%) Predictors of active H. pylori infection were mucosal oedema (18.1, 63.7%, 91.1%) and diffuse redness (14.4, 66.5%, 89.0%). Map-like redness had high specificity for previous H. pylori eradication (99.0%), but poor specificity (13.0%).ConclusionsThe RAC, mucosal oedema, diffuse redness and map-like redness are important endoscopic findings for determining H. pylori status. This meta-analysis provides a tentative basis for developing future endoscopic classification systems.
Cruz Rivera S, Liu X, Chan A-W, et al., 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension, The Lancet Digital Health, Vol: 2, Pages: e549-e560, ISSN: 2589-7500
The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understan
McKinney SM, Sieniek M, Godbole V, et al., 2020, Addendum: International evaluation of an AI system for breast cancer screening., Nature, Vol: 586, Pages: E19-E19, ISSN: 0028-0836
Liu X, Cruz Rivera S, Moher D, et al., 2020, Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension., Lancet Digit Health, Vol: 2, Pages: e537-e548
The CONSORT 2010 statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders), and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
O'Sullivan S, Leonard S, Holzinger A, et al., 2020, Operational framework and training standard requirements for AI-empowered robotic surgery, INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY, Vol: 16, ISSN: 1478-5951
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Davids J, Manivannan S, Darzi A, et al., 2020, Simulation for skills training in neurosurgery: a systematic review, meta-analysis, and analysis of progressive scholarly acceptance., Neurosurgical Review, Vol: 44, Pages: 1853-1867, ISSN: 0344-5607
At a time of significant global unrest and uncertainty surrounding how the delivery of clinical training will unfold over the coming years, we offer a systematic review, meta-analysis, and bibliometric analysis of global studies showing the crucial role simulation will play in training. Our aim was to determine the types of simulators in use, their effectiveness in improving clinical skills, and whether we have reached a point of global acceptance. A PRISMA-guided global systematic review of the neurosurgical simulators available, a meta-analysis of their effectiveness, and an extended analysis of their progressive scholarly acceptance on studies meeting our inclusion criteria of simulation in neurosurgical education were performed. Improvement in procedural knowledge and technical skills was evaluated. Of the identified 7405 studies, 56 studies met the inclusion criteria, collectively reporting 50 simulator types ranging from cadaveric, low-fidelity, and part-task to virtual reality (VR) simulators. In all, 32 studies were included in the meta-analysis, including 7 randomised controlled trials. A random effects, ratio of means effects measure quantified statistically significant improvement in procedural knowledge by 50.2% (ES 0.502; CI 0.355; 0.649, p < 0.001), technical skill including accuracy by 32.5% (ES 0.325; CI - 0.482; - 0.167, p < 0.001), and speed by 25% (ES - 0.25, CI - 0.399; - 0.107, p < 0.001). The initial number of VR studies (n = 91) was approximately double the number of refining studies (n = 45) indicating it is yet to reach progressive scholarly acceptance. There is strong evidence for a beneficial impact of adopting simulation in the improvement of procedural knowledge and technical skill. We show a growing trend towards the adoption of neurosurgical simulators, although we have not fully gained progressive scholarly acceptance for VR-based simulation technologies in neurosurgical education.
Cruz Rivera S, Liu X, Chan A-W, et al., 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension, Nature Medicine, Vol: 26, Pages: 1351-1363, ISSN: 1078-8956
The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, int
Liu X, Rivera SC, Moher D, et al., 2020, Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension, BMJ: British Medical Journal, Vol: 370, ISSN: 0959-535X
The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes.The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI. Both guidelines were developed through a staged consensus process, involving a literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants).The CONSORT-AI extension includes 14 new items, which were considered sufficiently important for AI interventions, that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and providing analysis of error cases.CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
Rivera SC, Liu X, Chan A-W, et al., 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension, BMJ: British Medical Journal, Vol: 370, Pages: 1-14, ISSN: 0959-535X
The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes.The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants).The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases.SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
Liu X, Cruz Rivera S, Moher D, et al., 2020, Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension., Nat Med, Vol: 26, Pages: 1364-1374
The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
Guo C, Ashrafian H, Ghafur S, et al., 2020, Challenges for the evaluation of digital health solutions-A call for innovative evidence generation approaches, NPJ DIGITAL MEDICINE, Vol: 3, ISSN: 2398-6352
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- Citations: 86
Guo C, Ashrafian H, Ghafur S, et al., 2020, Challenges for the evaluation of digital health solutions-A call for innovative evidence generation approaches., NPJ Digit Med, Vol: 3
The field of digital health, and its meaning, has evolved rapidly over the last 20 years. For this article we followed the most recent definition provided by FDA in 2020. Emerging solutions offers tremendous potential to positively transform the healthcare sector. Despite the growing number of applications, however, the evolution of methodologies to perform timely, cost-effective and robust evaluations have not kept pace. It remains an industry-wide challenge to provide credible evidence, therefore, hindering wider adoption. Conventional methodologies, such as clinical trials, have seldom been applied and more pragmatic approaches are needed. In response, several academic centers such as researchers from the Institute of Global Health Innovation at Imperial College London have initiated a digital health clinical simulation test bed to explore new approaches for evidence gathering relevant to solution type and maturity. The aim of this article is to: (1) Review current research approaches and discuss their limitations; (2) Discuss challenges faced by different stakeholders in undertaking evaluations; and (3) Call for new approaches to facilitate the safe and responsible growth of the digital health sector.
Ashrafian H, McHale D, 2020, The Genetics of Hereditary Hemihyperplasia in the Achaemenid Era
<p>Background: The first historical description of hemihperplasia through upper limb gigantism was described 2500 years ago. This takes place in the classical era within the context of the ancient Achaemenid dynasty where the emperor Artaxerxes I is described as suffering from arm gigantism.Methods: Pedigree analysis form classical sourcesResults: Artaxerxes’ family tree demonstrates a powerful pedigree collapse that is comparable with the consanguinity in the historical Spanish Royal line and that of the Hapsburgs. Such an extensive pedigree collapse may therefore offer an explanation for the pronounced phenotype of hemihyperplasia.Conclusion: The evaluation of ancient evidence with biological and genetic appraisal in presented in this case of hemihyperplasia can offer a deeper understanding of history whilst also highlighting the longstanding impact of genetic diseases on mankind.</p>
Flower B, Brown JC, Simmons B, et al., 2020, Clinical and laboratory evaluation of SARS-CoV-2 lateral flow assays for use in a national COVID-19 sero-prevalence survey, Thorax, Vol: 75, Pages: 1082-1088, ISSN: 0040-6376
BackgroundAccurate antibody tests are essential to monitor the SARS-CoV-2 pandemic. Lateral flow immunoassays (LFIAs) can deliver testing at scale. However, reported performance varies, and sensitivity analyses have generally been conducted on serum from hospitalised patients. For use in community testing, evaluation of finger-prick self-tests, in non-hospitalised individuals, is required.MethodsSensitivity analysis was conducted on 276 non-hospitalised participants. All had tested positive for SARS-CoV-2 by RT-PCR and were ≥21d from symptom-onset. In phase I we evaluated five LFIAs in clinic (with finger-prick) and laboratory (with blood and sera) in comparison to a) PCR-confirmed infection and b) presence of SARS-CoV-2 antibodies on two “in-house” ELISAs. Specificity analysis was performed on 500 pre-pandemic sera. In phase II, six additional LFIAs were assessed with serum.Findings95% (95%CI [92.2, 97.3]) of the infected cohort had detectable antibodies on at least one ELISA. LFIA sensitivity was variable, but significantly inferior to ELISA in 8/11 assessed. Of LFIAs assessed in both clinic and laboratory, finger-prick self-test sensitivity varied from 21%-92% vs PCR-confirmed cases and 22%-96% vs composite ELISA positives. Concordance between finger-prick and serum testing was at best moderate (kappa 0.56) and, at worst, slight (kappa 0.13). All LFIAs had high specificity (97.2% - 99.8%).InterpretationLFIA sensitivity and sample concordance is variable, highlighting the importance of evaluations in setting of intended use. This rigorous approach to LFIA evaluation identified a test with high specificity (98.6% (95%CI [97.1, 99.4])), moderate sensitivity (84.4% with fingerprick (95%CI [70.5, 93.5])), and moderate concordance, suitable for seroprevalence surveys.
Atchison C, Pristerà P, Cooper E, et al., 2020, Usability and acceptability of home-based self-testing for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antibodies for population surveillance, Clinical Infectious Diseases, Vol: 2020, Pages: 1-10, ISSN: 1058-4838
BACKGROUND: This study assesses acceptability and usability of home-based self-testing for SARS-CoV-2 antibodies using lateral flow immunoassays (LFIA). METHODS: We carried out public involvement and pilot testing in 315 volunteers to improve usability. Feedback was obtained through online discussions, questionnaires, observations and interviews of people who tried the test at home. This informed the design of a nationally representative survey of adults in England using two LFIAs (LFIA1 and LFIA2) which were sent to 10,600 and 3,800 participants, respectively, who provided further feedback. RESULTS: Public involvement and pilot testing showed high levels of acceptability, but limitations with the usability of kits. Most people reported completing the test; however, they identified difficulties with practical aspects of the kit, particularly the lancet and pipette, a need for clearer instructions and more guidance on interpretation of results. In the national study, 99.3% (8,693/8,754) of LFIA1 and 98.4% (2,911/2,957) of LFIA2 respondents attempted the test and 97.5% and 97.8% of respondents completed it, respectively. Most found the instructions easy to understand, but some reported difficulties using the pipette (LFIA1: 17.7%) and applying the blood drop to the cassette (LFIA2: 31.3%). Most respondents obtained a valid result (LFIA1: 91.5%; LFIA2: 94.4%). Overall there was substantial concordance between participant and clinician interpreted results (kappa: LFIA1 0.72; LFIA2 0.89). CONCLUSION: Impactful public involvement is feasible in a rapid response setting. Home self-testing with LFIAs can be used with a high degree of acceptability and usability by adults, making them a good option for use in seroprevalence surveys.
Soosaipillai G, Archer S, Ashrafian H, et al., 2020, Breaking bad news training in the COVID-19 era and beyond, Journal of Medical Education and Curricular Development, Vol: 7, Pages: 1-4, ISSN: 2382-1205
COVID-19 has disrupted the status quo for healthcare education. As a result, redeployed doctors and nurses are caring for patients at the end of their lives and breaking bad news with little experience or training. This article aims to understand why redeployed doctors and nurses feel unprepared to break bad news through a content analysis of their training curricula. As digital learning has come to the forefront in health care education during this time, relevant digital resources for breaking bad news training are suggested.
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