Coverage of the Coeliac Artery During Thoracic Endovascular Aortic Repair: A Systematic Review and Meta-Analysis.

OBJECTIVE: To investigate the clinical impact of coeliac artery (CA) coverage during thoracic endovascular aortic repair (TEVAR). METHODS: This systematic review and meta-analysis was conducted according to the PRISMA guidelines. Electronic databases were searched from 1989 to 2020 for studies reporting visceral ischaemia, spinal cord ischaemia (SCI), 30 day/in hospital mortality, endoleaks, re-intervention, and caudal stent graft migration following CA coverage in patients undergoing TEVAR. Meta-analysis was conducted using random effects modelling. The quality of the evidence was graded using the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach. RESULTS: Fifteen observational studies with 236 patients (108 male, age range 61.3 - 79 years) were included. The pooled visceral ischaemia rate was 13% with significant heterogeneity between studies (95% confidence intervals [CI] 4 - 24; I2 = 72%, p < .001). The SCI rate was 5% (95% CI 2 - 9; I2 = 0%); the 30 day/in hospital mortality was 4% (95% CI 1 - 7; I2 = 0%); the overall endoleak rate was 21% (95% CI 13 - 29; I2 = 35%) with a 5% (95% CI 0 - 13; I2 = 38%) rate of type Ib and 2% (95% CI 0 - 8; I2 = 43%) rate of type II endoleak from retrograde CA flow. The re-intervention rate was 13% (95% CI 6 - 22; I2 = 54%); the caudal stent graft migration rate was 3% (95% CI 0 - 9, I2 = 0%). The certainty of the body of evidence was judged to be very low for all outcomes. CONCLUSION: CA coverage during TEVAR is associated with high rates of visceral ischaemia, spinal cord ischaemia, 30 day/in hospital mortality, endoleaks, and re-intervention. Although the literature is of poor quality and questions remain over effects estimates, there is evidence that CA coverage should be avoided if at all possible, during TEVAR. REGISTRATION: PROSPERO registration number 244084.

COVID-19 vaccinations among Black Asian and Minority Ethnic (BAME) groups: Learning the lessons from influenza.

BACKGROUND: The COVID-19 vaccination roll-out continues to grow at significant pace around the world. There is, however, growing concern regarding vaccine hesitancy amongst Black, Asian and Minority Ethnic (BAME) populations. Such inequalities have the potential for exposing, an already at-risk population, further. Whilst the COVID-19 vaccination programme is in its infancy, influenza programmes have been undertaken for over 50 years, and may provide invaluable insights. In this commentary, we aim to examine the lessons from influenza vaccinations, and how this can help reduce inequalities with COVID-19 vaccinations. MAIN TEXT: Several factors have been associated with both seasonal and pandemic influenza vaccine hesitancy amongst BAME groups. One of the most prevalent barriers in both types of immunisation programmes is the mistrust of medical organisations. This is often a multi-faceted issue, with previous negative healthcare discrimination, and historical unethical practices contributing towards this scepticism. This mistrust, however, is predominantly aimed towards healthcare systems, as opposed to individual physicians. In fact, physician endorsement is often a strong driver to vaccination, with Black patients who receive this support 8 times more likely to receive seasonal influenza vaccination. On the other hand, with H1N1 pandemic influenza vaccination, social norms or community influence, was an important determinant. In both seasonal and pandemic immunisation programmes, a significant amount of concern regarding side-effects, including misinformation, was reported amongst BAME groups. CONCLUSIONS: The use of community-based approaches, with local advocacy, has the potential to counteract misinformation, and concerns regarding side-effects. Moreover, using consistent physician endorsement not only in media campaigns but also through messaging would potentially help to address longstanding healthcare mistrust amongst minority ethnic groups. Close attention regarding how the vaccination programme, and the health policies introduced as a consequence, affect BAME communities in order to prevent widening inequalities in the future.

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis.

BACKGROUND: Significant morbidity, mortality, and financial burden are associated with cardiac rhythm abnormalities. Conventional investigative tools are often unsuccessful in detecting cardiac arrhythmias because of their episodic nature. Smartwatches have gained popularity in recent years as a health tool for the detection of cardiac rhythms. OBJECTIVE: This study aims to systematically review and meta-analyze the diagnostic accuracy of smartwatches in the detection of cardiac arrhythmias. METHODS: A systematic literature search of the Embase, MEDLINE, and Cochrane Library databases was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies reporting the use of a smartwatch for the detection of cardiac arrhythmia. Summary estimates of sensitivity, specificity, and area under the curve were attempted using a bivariate model for the diagnostic meta-analysis. Studies were examined for quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. RESULTS: A total of 18 studies examining atrial fibrillation detection, bradyarrhythmias and tachyarrhythmias, and premature contractions were analyzed, measuring diagnostic accuracy in 424,371 subjects in total. The signals analyzed by smartwatches were based on photoplethysmography. The overall sensitivity, specificity, and accuracy of smartwatches for detecting cardiac arrhythmias were 100% (95% CI 0.99-1.00), 95% (95% CI 0.93-0.97), and 97% (95% CI 0.96-0.99), respectively. The pooled positive predictive value and negative predictive value for detecting cardiac arrhythmias were 85% (95% CI 0.79-0.90) and 100% (95% CI 1.0-1.0), respectively. CONCLUSIONS: This review demonstrates the evolving field of digital disease detection. The current diagnostic accuracy of smartwatch technology for the detection of cardiac arrhythmias is high. Although the innovative drive of digital devices in health care will continue to gain momentum toward screening, the process of accurate evidence accrual and regulatory standards ready to accept their introduction is strongly needed. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020213237; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=213237.

The Ethical Digital Surgeon.

This viewpoint explores the ethical and regulatory consequences of the digital transformation of the operating room. Surgical robotics is undergoing significant change and future advances will center around the capture and use of data. The consequences of creating this surgical data pipeline must be understood and digital surgical systems must prioritize the safeguarding of patient data. Moreover, data protection laws and frameworks must adapt to the changing nature of surgical data. Finally, digital surgeons must understand changing data legislation and best practice on data governance to act as guardians not only for their own but also for their patients' data.

Robotic Camera Assistance: The Future of Laparoscopic and Thoracoscopic Surgery?

INTRODUCTION: Minimally invasive techniques have become the standard for a variety of procedures across all surgical specialties. There has been a recent move to integrate robotic technology into standard laparoscopic and thoracoscopic surgery with the aim of improving stability of the visual field with the use of robotic camera assistance. The aim of this study was to report on and examine the use of a headset-controlled robotic camera holder, FreeHand. METHODS: Between May 2013 and Dec 2016, 105 procedures were observed where the FreeHand robotic camera assistant was used. Observations were made of 43 consultant surgeons in 30 hospitals performing 21 different surgical procedures. During the surgery, the number of scope cleans and collisions were quantified, and surgeons were asked to score from 0 to 5 the setup, ergonomics, usability, and overall experience in a questionnaire. RESULTS: Overall surgeon satisfaction was rated as "good" for setup (4.29), ergonomics of the system (4.12), usability (4.39), and overall experience of the system (4.34). In 8 operations (7.6%), there was a conversion from robotic camera assistant to manual assistant. There were no reported adverse events attributable to the use of the system. CONCLUSION: This study demonstrates the breadth of surgical procedures that can be performed with a robotic camera assistant. The robotic camera assistant was found to be safe and simple to use and was positively perceived on assessment in multiple procedures spanning several surgical specialties. This work suggests that robotic camera assistants may offer significant benefits to laparoscopic and thoracoscopic surgeons.

Adoption of routine surgical video recording: a nationwide freedom of information act request across England and Wales.

Background: Surgical video contains data with significant potential to improve surgical outcome assessment, quality assurance, education, and research. Current utilisation of surgical video recording is unknown and related policies/governance structures are unclear. Methods: A nationwide Freedom of Information (FOI) request concerning surgical video recording, technology, consent, access, and governance was sent to all acute National Health Service (NHS) trusts/boards in England/Wales between 20th February and 20th March 2023. Findings: 140/144 (97.2%) trusts/boards in England/Wales responded to the FOI request. Surgical procedures were routinely recorded in 22 trusts/boards. The median estimate of consultant surgeons routinely recording their procedures was 20%. Surgical video was stored on internal systems (n = 27), third-party products (n = 29), and both (n = 9). 32/140 (22.9%) trusts/boards ask for consent to record procedures as part of routine care. Consent for recording included non-clinical purposes in 55/140 (39.3%) trusts/boards. Policies for surgeon/patient access to surgical video were available in 48/140 (34.3%) and 32/140 (22.9%) trusts/boards, respectively. Surgical video was used for non-clinical purposes in 64/140 (45.7%) trusts/boards. Governance policies covering surgical video recording, use, and/or storage were available from 59/140 (42.1%) trusts/boards. Interpretation: There is significant heterogeneity in surgical video recording practices in England and Wales. A minority of trusts/boards routinely record surgical procedures, with large variation in recording/storage practices indicating scope for NHS-wide coordination. Revision of surgical video consent, accessibility, and governance policies should be prioritised by trusts/boards to protect key stakeholders. Increased availability of surgical video is essential for patients and surgeons to maximally benefit from the ongoing digital transformation of surgery. Funding: KL is supported by an NIHR Academic Clinical Fellowship and acknowledges infrastructure support for this research from the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC).

Perceptions and priorities of perioperative staff and the public for sustainable surgery: a validated questionnaire study.

Surgery is a substantial contributor to healthcare-related emissions. Despite a drive to improve sustainability, few interventions have been adopted. Stakeholder engagement is considered a key barrier to implementation. This study aimed to determine the attitudes and beliefs of the perioperative staff and the public regarding sustainability initiatives in surgery, and whether differences exist between the two groups. Materials and Methods: Separate validated healthcare professional and public questionnaires were developed using a stepwise process. A systematic review was undertaken using Medline, Embase and Cochrane to identify key domains pertaining to sustainability and ensure content validity. Initial questionnaires were developed and refined using an iterative process of feedback from focus groups. Psychometric validation was conducted to remove question ambiguity. The final validated questionnaire was distributed to perioperative staff and the public using a multimodal approach involving online tools and in person. Results: Only 37.1% of perioperative staff reported the implementation of sustainability initiatives in their departments. Yet, staff (45.7%) and the public (48.2%) somewhat agreed that sustainability should influence a surgeon's procedural decision-making. Insufficient staff education regarding sustainability was a potential cause for the lack of adoption, with 71.4% reporting they had no formal training. Moreover, discrepancies in the perceived importance of sustainability may have contributed. Staff and the public agreed that outcomes (38.6 vs. 42.7%, P=0.767) and surgeon experience with a technique (28.6 vs. 40.0%, P=0.082) were more important than sustainability. However, 40.9% of the public did not consider operative time an important factor compared to sustainability, while 45.7% of staff would only tolerate procedures 25% longer. Conclusions: Engaging stakeholders is central to implementing long-term environmentally sustainable initiatives in surgery without compromising patient outcomes. More work is needed to understand the relative trade-offs considered by perioperative staff and the public, as well as provide both groups with more pertinent education on ecological outcomes.

Interventions for sustainable surgery: a systematic review.

OBJECTIVE: To systematically evaluate interventions designed to improve the sustainability of surgical practice with respect to their environmental and financial impact. BACKGROUND: Surgery contributes significantly to emissions attributed to healthcare due to its high resource and energy use. Several interventions across the operative pathway have, therefore, been trialed to minimize this impact. Few comparisons of the environmental and financial effects of these interventions exist. MATERIALS AND METHODS: A search of studies published up to 2nd February 2022 describing interventions to increase surgical sustainability was undertaken. Articles regarding the environmental impact of only anesthetic agents were excluded. Data regarding environmental and financial outcomes were extracted with a quality assessment completed dependent upon the study design. RESULTS: In all, 1162 articles were retrieved, of which 21 studies met inclusion criteria. Twenty-five interventions were described, which were categorized into five domains: 'reduce and rationalize', 'reusable equipment and textiles', 'recycling and waste segregation', 'anesthetic alternatives', and 'other'. Eleven of the 21 studies examined reusable devices; those demonstrating a benefit reported 40-66% lower emissions than with single-use alternatives. In studies not showing a lower carbon footprint, the reduction in manufacturing emissions was offset by the high environmental impact of local fossil fuel-based energy required for sterilization. The per use monetary cost of reusable equipment was 47-83% of the single-use equivalent. CONCLUSIONS: A narrow repertoire of interventions to improve the environmental sustainability of surgery has been trialed. The majority focuses on reusable equipment. Emissions and cost data are limited, with longitudinal impacts rarely investigated. Real-world appraisals will facilitate implementation, as will an understanding of how sustainability impacts surgical decision-making.

The efficacy of team-based learning in developing the generic capability of problem-solving ability and critical thinking skills in nursing education: A systematic review.

BACKGROUND: The gaps between employers' expectations, theory and practice are the most prominent and universal issues in nursing education. New graduate nurses seem to be academically equipped but cannot integrate their knowledge into practice as they enter the clinical settings. Employers expressed limited critical thinking skills are a common problem among new graduate nurses. OBJECTIVES: To examine the efficacy of team-based learning (TBL) in developing problem-solving ability and critical thinking skills among nursing students and to identify the strategies used and obstacles to TBL in nursing education. DESIGN: Systematic review (SR) of experimental and quasi-experimental studies. DATABASE SOURCES: A thorough database search was done on 19 April 2022 with four electronic databases: Academic Search Premier, CINAHL Complete, ERIC, Medline/PubMed. Eligible studies from the 1990s to 2022 were selected according to the inclusion and exclusion criteria of this SR. REVIEW METHODS: The PRISMA 2020 guideline was applied in this SR. The JBI Critical Appraisal Checklists were used to evaluate the quality of the selected studies. Four reviewers participated in the review process to ensure rigour and credibility of the study. RESULT: The six studies reviewed included one randomised control trial, one crossover study, and four quasi-experimental studies. Two studies assessed the effectiveness of TBL in developing critical thinking skills and had shown in favour of TBL with statistically significant differences. Five studies evaluated the effectiveness of TBL in enhancing problem-solving ability. Three of five studies showed favour in TBL groups. The teaching strategies and obstacles in TBL use in nursing education were identified. CONCLUSION: TBL may be considered a potential active-learner-centred approach to develop critical and problem-solving skills in nursing education. Further research is needed to identify the optimal duration of implementing TBL to develop nursing students' critical thinking skills and problem-solving abilities.

Large AI Models in Health Informatics: Applications, Challenges, and the Future.

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including: 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics.

Oseltamivir is protective for in-patient mortality in PCR confirmed influenza B and influenza A(H3N2) infections in an historic cohort of 1,048 patients hospitalised during the 2016-17 and 2017-18 influenza seasons.

Standard course oseltamivir 75mg two times daily for five days was associated with an 82% reduction of odds of in-patient death (OR 0.18 (0.07,0.51)) compared to no oseltamivir treatment (OR 1.0 Reference) in a final multivariable logistic regression model of a retrospective cohort of PCR confirmed influenza B and influenza A (H3N2) infected patients admitted to a large UK teaching hospital in influenza seasons 2016-17 and 2017-18. No difference of protective odds for standard course oseltamivir was observed between influenza B and influenza A (H3N2) nor between influenza seasons. These observations strongly support clinical guidelines for molecular testing for respiratory viruses on admission to hospital and prompt treatment of confirmed seasonal influenza B and A with oseltamivir 75mg twice daily for five days.

Machine learning for technical skill assessment in surgery: a systematic review.

Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive, and subject to bias. Machine learning (ML) has the potential to provide rapid, automated, and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66), and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed the performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment of basic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon.PROSPERO: CRD42020226071.

Patient perspectives on surgeon-specific outcome reports in bariatric surgery.

BACKGROUND: Surgeon specific outcome reports (SSOR) in the UK can be accessed freely by the general public to promote transparency and informed decision-making. However, the views amongst bariatric patients concerning these data are unknown. OBJECTIVES: The aims of this study were to determine patient awareness, views and priorities for outcome reporting in bariatric surgery, and to provide recommendations for future surgeon-specific outcome reporting through the United Kingdom National Bariatric Surgery Registry. SETTING: Bariatric surgical unit in a UK university teaching hospital. METHODS: We adapted a previously validated questionnaire and surveyed the views of 150 patients in a single bariatric surgical unit. We collected data concerning awareness, views, and future priorities for outcome reporting. RESULTS: A full 73% of participants were unaware they could access SSOR. Of the participants that were unaware, 75% stated that they would have accessed SSOR had they been aware they could. Of the participants that had previously accessed SSOR, 86% stated they understood the data, although 61% indicated it did not influence their choice of surgeon. The majority of participants favored public release of outcome reports at the surgeon-level (75%) and hospital-level (83%). The 3 main priorities indicated by participants for future outcome reporting were complication rates (91%), patient reported outcome measures (90%), and reoperation rate (89%), all at the surgeon level. CONCLUSION: Patient awareness of outcome reporting is poor. Efforts must be made to increase awareness of SSOR. Patients should be incorporated as key stakeholders in determining future outcome reporting in bariatric surgery.

Surgical data science - from concepts toward clinical translation.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

A Delphi consensus statement for digital surgery.

The use of digital technology is increasing rapidly across surgical specialities, yet there is no consensus for the term 'digital surgery'. This is critical as digital health technologies present technical, governance, and legal challenges which are unique to the surgeon and surgical patient. We aim to define the term digital surgery and the ethical issues surrounding its clinical application, and to identify barriers and research goals for future practice. 38 international experts, across the fields of surgery, AI, industry, law, ethics and policy, participated in a four-round Delphi exercise. Issues were generated by an expert panel and public panel through a scoping questionnaire around key themes identified from the literature and voted upon in two subsequent questionnaire rounds. Consensus was defined if >70% of the panel deemed the statement important and <30% unimportant. A final online meeting was held to discuss consensus statements. The definition of digital surgery as the use of technology for the enhancement of preoperative planning, surgical performance, therapeutic support, or training, to improve outcomes and reduce harm achieved 100% consensus agreement. We highlight key ethical issues concerning data, privacy, confidentiality and public trust, consent, law, litigation and liability, and commercial partnerships within digital surgery and identify barriers and research goals for future practice. Developers and users of digital surgery must not only have an awareness of the ethical issues surrounding digital applications in healthcare, but also the ethical considerations unique to digital surgery. Future research into these issues must involve all digital surgery stakeholders including patients.

Investigating the Ethical and Data Governance Issues of Artificial Intelligence in Surgery: Protocol for a Delphi Study.

BACKGROUND: The rapid uptake of digital technology into the operating room has the potential to improve patient outcomes, increase efficiency of the use of operating rooms, and allow surgeons to progress quickly up learning curves. These technologies are, however, dependent on huge amounts of data, and the consequences of their mismanagement are significant. While the field of artificial intelligence ethics is able to provide a broad framework for those designing and implementing these technologies into the operating room, there is a need to determine and address the ethical and data governance challenges of using digital technology in this unique environment. OBJECTIVE: The objectives of this study are to define the term digital surgery and gain expert consensus on the key ethical and data governance issues, barriers, and future research goals of the use of artificial intelligence in surgery. METHODS: Experts from the fields of surgery, ethics and law, policy, artificial intelligence, and industry will be invited to participate in a 4-round consensus Delphi exercise. In the first round, participants will supply free-text responses across 4 key domains: ethics, data governance, barriers, and future research goals. They will also be asked to provide their understanding of the term digital surgery. In subsequent rounds, statements will be grouped, and participants will be asked to rate the importance of each issue on a 9-point Likert scale ranging from 1 (not at all important) to 9 (critically important). Consensus is defined a priori as a score of 7 to 9 by 70% of respondents and 1 to 3 by less than 30% of respondents. A final online meeting round will be held to discuss inclusion of statements and draft a consensus document. RESULTS: Full ethical approval has been obtained for the study by the local research ethics committee at Imperial College, London (20IC6136). We anticipate round 1 to commence in January 2021. CONCLUSIONS: The results of this study will define the term digital surgery, identify the key issues and barriers, and shape future research in this area. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/26552.

Uptake and accessibility of surgical robotics in England.

BACKGROUND: The distribution, utilisation and accessibility of surgical robotics in England is unknown. METHODS: A nationwide Freedom of Information (FOI) request was sent to all acute National Health Service (NHS) trusts. Accessibility was assessed for 32 843 Lower Super Output Areas in England. RESULTS: All 149 acute NHS trusts responded to the FOI request. Sixty-one robots are distributed between 48 trusts. The number of robots and robotic procedures has increased annually. Urological procedures comprise 84.2% of robotic procedures. Procedure volume varies between robotic centres ranging from 1 to 683 in 2018. Over 2.4 million people have a travel time of over 1 hour to their nearest robotic centre. DISCUSSION: National accessibility to robotic services and case volumes are variable and does not represent good value for the NHS. A national robotic surgery registry could improve the quality of robotic surgery and is needed to dynamically assess national provision of this technology.

Clinical outcomes of digital sensor alerting systems in remote monitoring: a systematic review and meta-analysis.

Advances in digital technologies have allowed remote monitoring and digital alerting systems to gain popularity. Despite this, limited evidence exists to substantiate claims that digital alerting can improve clinical outcomes. The aim of this study was to appraise the evidence on the clinical outcomes of digital alerting systems in remote monitoring through a systematic review and meta-analysis. A systematic literature search, with no language restrictions, was performed to identify studies evaluating healthcare outcomes of digital sensor alerting systems used in remote monitoring across all (medical and surgical) cohorts. The primary outcome was hospitalisation; secondary outcomes included hospital length of stay (LOS), mortality, emergency department and outpatient visits. Standard, pooled hazard ratio and proportion of means meta-analyses were performed. A total of 33 studies met the eligibility criteria; of which, 23 allowed for a meta-analysis. A 9.6% mean decrease in hospitalisation favouring digital alerting systems from a pooled random effects analysis was noted. However, pooled weighted mean differences and hazard ratios did not reproduce this finding. Digital alerting reduced hospital LOS by a mean difference of 1.043 days. A 3% mean decrease in all-cause mortality from digital alerting systems was noted. There was no benefit of digital alerting with respect to emergency department or outpatient visits. Digital alerts can considerably reduce hospitalisation and length of stay for certain cohorts in remote monitoring. Further research is required to confirm these findings and trial different alerting protocols to understand optimal alerting to guide future widespread implementation.

Characteristics and predictors of acute and chronic post-COVID syndrome: A systematic review and meta-analysis.

BACKGROUND: A significant proportion of individuals experience lingering and debilitating symptoms following acute COVID-19 infection. The National Institute for Health and Care Excellence (NICE) have coined the persistent cluster of symptoms as post-COVID syndrome. This has been further sub-categorised into acute post-COVID syndrome for symptoms persisting three weeks beyond initial infection and chronic post-COVID syndrome for symptoms persisting beyond twelve weeks. The aim of this review was to detail the prevalence of clinical features and identify potential predictors for acute and chronic post-COVID syndrome. METHODS: A systematic literature search, with no language restrictions, was performed to identify studies detailing characteristics and outcomes related to survivorship of post-COVID syndrome. The last search was performed on 6 March 2021 and all pre-dating published articles included. A means of proportion meta-analysis was performed to quantify characteristics of acute and chronic post-COVID syndrome. Study quality was assessed with a specific risk of bias tool. PROSPERO Registration: CRD42020222855. FINDINGS: A total of 43 studies met the eligibility criteria; of which, 38 allowed for meta-analysis. Fatigue and dyspnoea were the most prevalent symptoms in acute post-COVID (0·37 and 0·35) and fatigue and sleep disturbance in chronic post-COVID syndrome (0·48 and 0·44), respectively. The available evidence is generally of poor quality, with considerable risk of bias, and are of observational design. INTERPRETATION: In conclusion, this review highlights that flaws in data capture and interpretation, noted in the uncertainty within our meta-analysis, affect the applicability of current knowledge. Policy makers and researchers must focus on understanding the impact of this condition on individuals and society with appropriate funding initiatives and global collaborative research.