Evaluation of venous thromboembolism risk assessment models for hospital inpatients: the VTEAM evidence synthesis.

Background: Pharmacological prophylaxis during hospital admission can reduce the risk of acquired blood clots (venous thromboembolism) but may cause complications, such as bleeding. Using a risk assessment model to predict the risk of blood clots could facilitate selection of patients for prophylaxis and optimise the balance of benefits, risks and costs. Objectives: We aimed to identify validated risk assessment models and estimate their prognostic accuracy, evaluate the cost-effectiveness of different strategies for selecting hospitalised patients for prophylaxis, assess the feasibility of using efficient research methods and estimate key parameters for future research. Design: We undertook a systematic review, decision-analytic modelling and observational cohort study conducted in accordance with Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines. Setting: NHS hospitals, with primary data collection at four sites. Participants: Medical and surgical hospital inpatients, excluding paediatric, critical care and pregnancy-related admissions. Interventions: Prophylaxis for all patients, none and according to selected risk assessment models. Main outcome measures: Model accuracy for predicting blood clots, lifetime costs and quality-adjusted life-years associated with alternative strategies, accuracy of efficient methods for identifying key outcomes and proportion of inpatients recommended prophylaxis using different models. Results: We identified 24 validated risk assessment models, but low-quality heterogeneous data suggested weak accuracy for prediction of blood clots and generally high risk of bias in all studies. Decision-analytic modelling showed that pharmacological prophylaxis for all eligible is generally more cost-effective than model-based strategies for both medical and surgical inpatients, when valuing a quality-adjusted life-year at £20,000. The findings were more sensitive to uncertainties in the surgical population; strategies using risk assessment models were more cost-effective if the model was assumed to have a very high sensitivity, or the long-term risks of post-thrombotic complications were lower. Efficient methods using routine data did not accurately identify blood clots or bleeding events and several pre-specified feasibility criteria were not met. Theoretical prophylaxis rates across an inpatient cohort based on existing risk assessment models ranged from 13% to 91%. Limitations: Existing studies may underestimate the accuracy of risk assessment models, leading to underestimation of their cost-effectiveness. The cost-effectiveness findings do not apply to patients with an increased risk of bleeding. Mechanical thromboprophylaxis options were excluded from the modelling. Primary data collection was predominately retrospective, risking case ascertainment bias. Conclusions: Thromboprophylaxis for all patients appears to be generally more cost-effective than using a risk assessment model, in hospitalised patients at low risk of bleeding. To be cost-effective, any risk assessment model would need to be highly sensitive. Current evidence on risk assessment models is at high risk of bias and our findings should be interpreted in this context. We were unable to demonstrate the feasibility of using efficient methods to accurately detect relevant outcomes for future research. Future work: Further research should evaluate routine prophylaxis strategies for all eligible hospitalised patients. Models that could accurately identify individuals at very low risk of blood clots (who could discontinue prophylaxis) warrant further evaluation. Study registration: This study is registered as PROSPERO CRD42020165778 and Researchregistry5216. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR127454) and will be published in full in Health Technology Assessment; Vol. 28, No. 20. See the NIHR Funding and Awards website for further award information.

People who are admitted to hospital are at risk of blood clots that can cause serious illness or death. Patients are often given low doses of blood-thinning drugs to reduce this risk. However, these drugs can cause side effects, such as bleeding. Hospitals currently use complex risk assessment models (risk scores, which usually include patient, disease, mobility and intervention factors) to determine the individual risk of blood clots and identify people most likely to benefit from blood-thinning drugs. There are a lot of different risk scores and we do not know which one is best. We also do not know how these scores compare to each other or whether using scores to decide who should get blood-thinning drugs provides good value for money to the NHS. We reviewed all previous studies of risk scores. We found that they did not predict blood clots very well and we could not recommend one score over another. We then created a mathematical model to simulate the use of blood-thinning drugs in people admitted to hospital. The model suggested that giving blood-thinning drugs to everyone who could have them would probably provide the best value for money, in medical patients. Our findings were the same, but less certain, for surgical patients. We also collected information from four NHS hospitals to explore possibilities for future research. Our work showed that routinely collected electronic data on blood clots and bleeding events is not very accurate and that using different scores could result in variable use of blood-thinning medications. Our findings suggest that it may be better value to the NHS and better for patients if we were to offer blood-thinning medications to everyone on admission to hospital, without using any risk score. However, this approach needs further research to ensure it is safe and effective. Such research would not be able to rely on routine electronic data to identify blood clots or bleeding events, in isolation.

Functional mechanism study of the allelochemical myrigalone A identifies a group of ultrapotent inhibitors of ethylene biosynthesis in plants.

Allelochemicals represent a class of natural products released by plants as root, leaf, and fruit exudates that interfere with the growth and survival of neighboring plants. Understanding how allelochemicals function to regulate plant responses may provide valuable new approaches to better control plant function. One such allelochemical, Myrigalone A (MyA) produced by Myrica gale, inhibits seed germination and seedling growth through an unknown mechanism. Here, we investigate MyA using the tractable model Dictyostelium discoideum and reveal that its activity depends on the conserved homolog of the plant ethylene synthesis protein 1-aminocyclopropane-1-carboxylic acid oxidase (ACO). Furthermore, in silico modeling predicts the direct binding of MyA to ACO within the catalytic pocket. In D. discoideum, ablation of ACO mimics the MyA-dependent developmental delay, which is partially restored by exogenous ethylene, and MyA reduces ethylene production. In Arabidopsis thaliana, MyA treatment delays seed germination, and this effect is rescued by exogenous ethylene. It also mimics the effect of established ACO inhibitors on root and hypocotyl extension, blocks ethylene-dependent root hair production, and reduces ethylene production. Finally, in silico binding analyses identify a range of highly potent ethylene inhibitors that block ethylene-dependent response and reduce ethylene production in Arabidopsis. Thus, we demonstrate a molecular mechanism by which the allelochemical MyA reduces ethylene biosynthesis and identify a range of ultrapotent inhibitors of ethylene-regulated responses.

A phase 1/2 study of pepinemab in children, adolescents, or young adults with recurrent or refractory solid tumors: A children's oncology group consortium report (ADVL1614).

PURPOSE: Pepinemab, a humanized IgG4 monoclonal antibody, targets the SEMA4D (CD100) antigen to inhibit binding to its high-affinity receptors (plexin B1/PLXNB1, plexin B2/PLXNB2) and low-affinity receptor (CD72). SEMA4D blockade leads to increased cytotoxic T-cell infiltration, delayed tumor growth, and durable tumor rejection in murine tumor models. Pepinemab was well tolerated and improved T cell infiltration in clinical studies in adults with refractory tumors. SEMA4D was identified as a strong candidate proto-oncogene in a model of osteosarcoma. Based on these preclinical and clinical data, we conducted a phase 1/2 study to determine the recommended phase 2 dose (RP2D), pharmacokinetics, pharmacodynamics, and immunogenicity, of pepinemab in pediatric patients with recurrent/refractory solid tumors, and activity in osteosarcoma. EXPERIMENTAL DESIGN: Pepinemab was administered intravenously on Days 1 and 15 of a 28-day cycle at 20 mg/kg, the adult RP2D. Part A (phase 1) used a Rolling 6 design; Part B (phase 2) used a Simon 2-stage design in patients with osteosarcoma. Pharmacokinetics and target saturation were evaluated in peripheral blood. RESULTS: Pepinemab (20 mg/kg) was well tolerated and no dose-limiting toxicities were observed during Part A. There were no objective responses. Two patients with osteosarcoma achieved disease control and prolonged stable disease. Pepinemab pharmacokinetics were similar to adults. CONCLUSIONS: Pepinemab (20 mg/kg) is safe, well tolerated and resulted in adequate and sustained target saturation in pediatric patients. Encouraging disease control in two patients with osteosarcoma warrants further investigation with novel combination strategies to modulate the tumor microenvironment and antitumor immune response. CLINICAL TRIAL REGISTRY: This trial is registered as NCT03320330 at Clinicaltrials.gov. DISCLAIMER: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Concerted adoption as an emerging strategy for digital transformation of healthcare-lessons from Australia, Canada, and England.

OBJECTIVES: With an increasing focus on the digitalization of health and care settings, there is significant scope to learn from international approaches to promote concerted adoption of electronic health records. MATERIALS AND METHODS: We review three large-scale initiatives from Australia, Canada, and England, and extract common lessons for future health and social care transformation strategy. RESULTS: We discuss how, despite differences in contexts, concerted adoption enables sharing of experience and learning to streamline the digital transformation of health and care. DISCUSSION AND CONCLUSION: Concerted adoption can be accelerated through building communities of expertise and partnerships promoting knowledge transfer and circulation of expertise; commonalities in geographical and cultural contexts; and commonalities in technological systems.

Fair and equitable AI in biomedical research and healthcare: Social science perspectives.

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference "Fair medicine and AI" (online 3-5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.

Optimising ePrescribing in hospitals through the interoperability of systems and processes: a qualitative study in the UK, US, Norway and the Netherlands.

BACKGROUND: Investment in the implementation of hospital ePrescribing systems has been a priority in many economically-developed countries in order to modernise the delivery of healthcare. However, maximum gains in the safety, quality and efficiency of care are unlikely to be fully realised unless ePrescribing systems are further optimised in a local context. Typical barriers to optimal use are often encountered in relation to a lack of systemic capacity and preparedness to meet various levels of interoperability requirements, including at the data, systems and services levels. This lack of systemic interoperability may in turn limit the opportunities and benefits potentially arising from implementing novel digital heath systems. METHODS: We undertook n = 54 qualitative interviews with key stakeholders at nine digitally advanced hospital sites across the UK, US, Norway and the Netherlands. We included hospitals featuring 'standalone, best of breed' systems, which were interfaced locally, and multi-component and integrated electronic health record enterprise systems. We analysed the data inductively, looking at strategies and constraints for ePrescribing interoperability within and beyond hospital systems. RESULTS: Our thematic analysis identified 4 main drivers for increasing ePrescribing systems interoperability: (1) improving patient safety (2) improving integration & continuity of care (3) optimising care pathways and providing tailored decision support to meet local and contextualised care priorities and (4) to enable full patient care services interoperability in a variety of settings and contexts. These 4 interoperability dimensions were not always pursued equally at each implementation site, and these were often dependent on the specific national, policy, organisational or technical contexts of the ePrescribing implementations. Safety and efficiency objectives drove optimisation targeted at infrastructure and governance at all levels. Constraints to interoperability came from factors such as legacy systems, but barriers to interoperability of processes came from system capability, hospital policy and staff culture. CONCLUSIONS: Achieving interoperability is key in making ePrescribing systems both safe and useable. Data resources exist at macro, meso and micro levels, as do the governance interventions necessary to achieve system interoperability. Strategic objectives, most notably improved safety, often motivated hospitals to push for evolution across the entire data architecture of which they formed a part. However, hospitals negotiated this terrain with varying degrees of centralised coordination. Hospitals were heavily reliant on staff buy-in to ensure that systems interoperability was built upon to achieve effective data sharing and use. Positive outcomes were founded on a culture of agreement about the usefulness of access by stakeholders, including prescribers, policymakers, vendors and lab technicians, which was reflected in an alignment of governance goals with system design.

Early Experiences of Integrating an Artificial Intelligence-Based Diagnostic Decision Support System into Radiology Settings: A Qualitative Study.

BACKGROUND: Artificial Intelligence (AI) based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based e-learning imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. We performed semi-structured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations and Macro-environmental factors framework (TPOM). We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure. These under-researched factors require attention and further research.

From ACEs to early relational health: Implications for clinical practice.

Children grow and develop in an environment of relationships. Safe, stable, nurturing relationships help build resilience and buffer the negative impact of adverse experiences. Promoting relational health in clinical practice shifts the focus from adverse childhood experiences (ACEs) to positive childhood experiences (PCEs). This approach evaluates a family's strengths and assets, and can be incorporated into both well-child and subspecialty care. While the optimal window for such interventions is in the prenatal period or as early as possible within the first 3 years of life, it is never too late to start. This statement describes how clinicians can bring a relational health approach to any medical encounter by understanding: what toxic stress is and how it can affect the developing brain, family relationships, and child development; how positive relationships, experiences, and behaviours can help buffer such effects and build resilience; observable signs of relational health and risk in parent-child interactions; the attributes of trustful, therapeutic relationships with families; and how to optimize these benefits through conversation and clinical practice.

Des expériences négatives de l'enfance à la santé relationnelle précoce : les conséquences pour la pratique clinique.

Les enfants grandissent et se développent dans un environnement de relations. Des relations sécuritaires, stables et bienveillantes contribuent à consolider la résilience et à atténuer les répercussions des expériences négatives. La promotion de la santé relationnelle en pratique clinique recentre l'attention accordée aux expériences négatives de l'enfance sur les expériences positives de l'enfance. Cette approche, qui évalue les forces et les atouts d'une famille, peut être intégrée à la fois aux rendez-vous réguliers de l'enfant en santé et aux soins surspécialisés. Il est optimal de réaliser de telles interventions pendant la période prénatale ou le plus rapidement possible avant l'âge de trois ans, mais il n'est jamais trop tard pour les entreprendre. Le présent document de principes décrit comment les cliniciens peuvent adopter une approche de santé relationnelle lors de chacune de leurs rencontres médicales s'ils comprennent ce qu'est le stress toxique et ses effets sur le cerveau en développement, les relations familiales et le développement de l'enfant; à quel point les relations, expériences et comportements positifs peuvent en atténuer les effets et renforcer la résilience; quels sont les signes observables de la santé relationnelle et des risques relationnels dans les interactions entre les parents et l'enfant; quelles sont les caractéristiques de relations thérapeutiques de confiance avec les familles et comment en optimiser les avantages par les échanges et la pratique clinique.

Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study.

OBJECTIVES: Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. MATERIALS AND METHODS: We performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework. RESULTS: We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. DISCUSSION: Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure. CONCLUSION: The socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research.

Evaluation of Digitalisation in Healthcare and the Quantification of the "Unmeasurable".

Evaluating healthcare digitalisation, where technology implementation and adoption transforms existing socio-organisational processes, presents various challenges for outcome assessments. Populations are diverse, interventions are complex and evolving over time, meaningful comparisons are difficult as outcomes vary between settings, and outcomes take a long time to materialise and stabilise. Digitalisation may also have unanticipated impacts. We here discuss the limitations of evaluating the digitalisation of healthcare, and describe how qualitative and quantitative approaches can complement each other to facilitate investment and implementation decisions. In doing so, we argue how existing approaches have focused on measuring what is easily measurable and elevating poorly chosen values to inform investment decisions. Limited attention has been paid to understanding processes that are not easily measured even though these can have significant implications for contextual transferability, sustainability and scale-up of interventions. We use what is commonly known as the McNamara Fallacy to structure our discussions. We conclude with recommendations on how we envisage the development of mixed methods approaches going forward in order to address shortcomings.

Medium-Chain Fatty Acids Rescue Motor Function and Neuromuscular Junction Degeneration in a Drosophila Model of Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease characterised by progressive degeneration of the motor neurones. An expanded GGGGCC (G4C2) hexanucleotide repeat in C9orf72 is the most common genetic cause of ALS and frontotemporal dementia (FTD); therefore, the resulting disease is known as C9ALS/FTD. Here, we employ a Drosophila melanogaster model of C9ALS/FTD (C9 model) to investigate a role for specific medium-chain fatty acids (MCFAs) in reversing pathogenic outcomes. Drosophila larvae overexpressing the ALS-associated dipeptide repeats (DPRs) in the nervous system exhibit reduced motor function and neuromuscular junction (NMJ) defects. We show that two MCFAs, nonanoic acid (NA) and 4-methyloctanoic acid (4-MOA), can ameliorate impaired motor function in C9 larvae and improve NMJ degeneration, although their mechanisms of action are not identical. NA modified postsynaptic glutamate receptor density, whereas 4-MOA restored defects in the presynaptic vesicular release. We also demonstrate the effects of NA and 4-MOA on metabolism in C9 larvae and implicate various metabolic pathways as dysregulated in our ALS model. Our findings pave the way to identifying novel therapeutic targets and potential treatments for ALS.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision.

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

ePrescribing-Based Antimicrobial Stewardship Practices in an English National Health Service Hospital: Qualitative Interview Study Among Medical Prescribers and Pharmacists.

BACKGROUND: Antimicrobial resistance, the ability of microorganisms to survive antimicrobial drugs, is a public health emergency. Although electronic prescribing (ePrescribing)-based interventions designed to reduce unnecessary antimicrobial usage exist, these often do not integrate effectively with existing workflows. As a result, ePrescribing-based interventions may have limited impact in addressing antimicrobial resistance. OBJECTIVE: We sought to understand the existing ePrescribing-based antimicrobial stewardship (AMS) practices in an English hospital preceding the implementation of functionality designed to improve AMS. METHODS: We conducted 18 semistructured interviews with medical prescribers and pharmacists with varying levels of seniority exploring current AMS practices and investigating potential areas for improvement. Participants were recruited with the help of local gatekeepers. Topic guides sought to explore both formal and informal practices surrounding AMS, and challenges and opportunities for ePrescribing-based intervention. We coded audio-recorded and transcribed data with the help of the Technology, People, Organizations, and Macroenvironmental factors framework, allowing emerging themes to be added inductively. We used NVivo 12 (QSR International) to facilitate coding. RESULTS: Antimicrobial prescribing and review processes were characterized by competing priorities and uncertainty of prescribers and reviewers around prescribing decisions. For example, medical prescribers often had to face trade-offs between individual patient benefit and more diffuse population health benefits, and the rationale for prescribing decisions was not always clear. Prescribing involved a complex set of activities carried out by various health care practitioners who each only had a partial and temporary view of the whole process, and whose relationships were characterized by deeply engrained hierarchies that shaped interactions and varied across specialties. For example, newly qualified doctors and pharmacists were hesitant to change a consultant's prescribing decision when reviewing prescriptions. Multidisciplinary communication, collaboration, and coordination promoted good AMS practices by reducing uncertainty. CONCLUSIONS: Design of ePrescribing-based interventions to improve AMS needs to take into account the multitude of actors and organizational complexities involved in the prescribing and review processes. Interventions that help reduce prescriber or reviewer uncertainty and improve multidisciplinary collaboration surrounding initial antimicrobial prescribing and subsequent prescription review are most likely to be effective. Without such attention, interventions are unlikely to fulfill their goal of improving patient outcomes and combatting antimicrobial resistance.

The Impact of Pre-Operative Anaemia on One Year Amputation Free Survival and Re-Admissions in Patients Undergoing Vascular Surgery for Peripheral Arterial Disease: a National Vascular Registry Study.

OBJECTIVE: Anaemia is common among patients undergoing surgery, but its association with post-operative outcomes in patients with peripheral arterial disease (PAD) is unclear. The aim of this observational population based study was to examine the association between pre-operative anaemia and one year outcomes after surgical revascularisation for PAD. METHODS: This study used data from the National Vascular Registry, linked with an administrative database (Hospital Episode Statistics), to identify patients who underwent open surgical lower limb revascularisation for PAD in English NHS hospitals between January 2016 and December 2019. The primary outcome was one year amputation free survival. Secondary outcomes were one year re-admission rate, 30 day re-intervention rate, 30 day ipsilateral major amputation rate and 30 day death. Flexible parametric survival analysis and generalised linear regression were performed to assess the effect of anaemia on one year outcomes. RESULTS: The analysis included 13 641 patients, 57.9% of whom had no anaemia, 23.8% mild, and 18.3% moderate or severe anaemia. At one year follow up, 80.6% of patients were alive and amputation free. The risk of one year amputation or death was elevated in patients with mild anaemia (adjusted HR 1.3; 95% CI 1.15 - 1.41) and moderate or severe anaemia (aHR 1.5; 1.33 - 1.67). Patients with moderate or severe anaemia experienced more re-admissions over one year (adjusted IRR 1.31; 1.26 - 1.37) and had higher odds of 30 day re-interventions (aOR 1.22; 1.04 - 1.45), 30 day ipsilateral major amputation (aOR 1.53; 1.17 - 2.01), and 30 day death (aOR 1.39; 1.03 - 1.88) compared with patients with no anaemia. CONCLUSION: Pre-operative anaemia is associated with lower one year amputation free survival and higher one year re-admission rates following surgical revascularisation in patients with PAD. Research is required to evaluate whether interventions to correct anaemia improve outcomes after lower limb revascularisation.

Optometrists' Perspectives Regarding Artificial Intelligence Aids and Contributing Retinal Images to a Repository: Web-Based Interview Study.

BACKGROUND: A repository of retinal images for research is being established in Scotland. It will permit researchers to validate, tune, and refine artificial intelligence (AI) decision-support algorithms to accelerate safe deployment in Scottish optometry and beyond. Research demonstrates the potential of AI systems in optometry and ophthalmology, though they are not yet widely adopted. OBJECTIVE: In this study, 18 optometrists were interviewed to (1) identify their expectations and concerns about the national image research repository and their use of AI decision support and (2) gather their suggestions for improving eye health care. The goal was to clarify attitudes among optometrists delivering primary eye care with respect to contributing their patients' images and to using AI assistance. These attitudes are less well studied in primary care contexts. Five ophthalmologists were interviewed to discover their interactions with optometrists. METHODS: Between March and August 2021, 23 semistructured interviews were conducted online lasting for 30-60 minutes. Transcribed and pseudonymized recordings were analyzed using thematic analysis. RESULTS: All optometrists supported contributing retinal images to form an extensive and long-running research repository. Our main findings are summarized as follows. Optometrists were willing to share images of their patients' eyes but expressed concern about technical difficulties, lack of standardization, and the effort involved. Those interviewed thought that sharing digital images would improve collaboration between optometrists and ophthalmologists, for example, during referral to secondary health care. Optometrists welcomed an expanded primary care role in diagnosis and management of diseases by exploiting new technologies and anticipated significant health benefits. Optometrists welcomed AI assistance but insisted that it should not reduce their role and responsibilities. CONCLUSIONS: Our investigation focusing on optometrists is novel because most similar studies on AI assistance were performed in hospital settings. Our findings are consistent with those of studies with professionals in ophthalmology and other medical disciplines: showing near universal willingness to use AI to improve health care, alongside concerns over training, costs, responsibilities, skill retention, data sharing, and disruptions to professional practices. Our study on optometrists' willingness to contribute images to a research repository introduces a new aspect; they hope that a digital image sharing infrastructure will facilitate service integration.

Enhanced Sestrin expression through Tanshinone 2A treatment improves PI3K-dependent inhibition of glioma growth.

Glioblastomas are a highly aggressive cancer type which respond poorly to current pharmaceutical treatments, thus novel therapeutic approaches need to be investigated. One such approach involves the use of the bioactive natural product Tanshinone IIA (T2A) derived from the Chinese herb Danshen, where mechanistic insight for this anti-cancer agent is needed to validate its use. Here, we employ a tractable model system, Dictyostelium discoideum, to provide this insight. T2A potently inhibits cellular proliferation of Dictyostelium, suggesting molecular targets in this model. We show that T2A rapidly reduces phosphoinositide 3 kinase (PI3K) and protein kinase B (PKB) activity, but surprisingly, the downstream complex mechanistic target of rapamycin complex 1 (mTORC1) is only inhibited following chronic treatment. Investigating regulators of mTORC1, including PKB, tuberous sclerosis complex (TSC), and AMP-activated protein kinase (AMPK), suggests these enzymes were not responsible for this effect, implicating an additional molecular mechanism of T2A. We identify this mechanism as the increased expression of sestrin, a negative regulator of mTORC1. We further show that combinatory treatment using a PI3K inhibitor and T2A gives rise to a synergistic inhibition of cell proliferation. We then translate our findings to human and mouse-derived glioblastoma cell lines, where both a PI3K inhibitor (Paxalisib) and T2A reduces glioblastoma proliferation in monolayer cultures and in spheroid expansion, with combinatory treatment significantly enhancing this effect. Thus, we propose a new approach for cancer treatment, including glioblastomas, through combinatory treatment with PI3K inhibitors and T2A.

A Public Health Laboratory Information System in Support of Health Emergencies: The Nebraska Public Health Laboratory COVID-19 Experience.

OBJECTIVES: Public health laboratories (PHLs) are essential components of US Public Health Service operations. The health information technology that supports PHLs is central to effective and efficient laboratory operations and overall public health response to infectious disease management. This analysis presents key information on how the Nebraska Public Health Laboratory (NPHL) information technology system evolved to meet the demands of the COVID-19 pandemic. MATERIALS AND METHODS: COVID-19 presented numerous, unforeseen information technology system challenges. The most notable challenges requiring changes to NPHL software systems and capability were improving efficiency of the laboratory operation due to high-volume testing, responding daily to demands for timely data for analysis by partner systems, interfacing with multiple testing (equipment) platforms, and supporting community-based specimen collection programs. RESULTS: Improvements to the NPHL information technology system enabled NPHL to perform >121 000 SARS-CoV-2 polymerase chain reaction tests from March 2020 through January 2022 at a sustainable rate of 2000 SARS-CoV-2 tests per day, with no increase in laboratory staffing. Electronic reporting of 62 000 rapid antigen tests eliminated paper reporting and extended testing services throughout the state. Collection of COVID-19 symptom data before specimen collection enabled NPHL to make data-driven decisions to perform pool testing and conserve testing kits when supplies were low. PRACTICE IMPLICATIONS: NPHL information technology applications proved essential for managing health care provider workload, prioritizing the use of scarce testing supplies, and managing Nebraska's overall pandemic response. The NPHL experience provides useful examples of a highly capable information technology system and suggests areas for additional attention in the PHL environment, including a focus on end users, collaboration with various partners, and investment in information technology.

Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies.

Artificial intelligence (AI) and machine learning medical tools have the potential to be transformative in care delivery; however, this change will only be realized if accompanied by effective governance that ensures patient safety and public trust. Recent digital health initiatives have called for tighter governance of digital health. A correct balance must be found between ensuring product safety and performance while also enabling the innovation needed to deliver better approaches for patients and affordable efficient health care for society. This requires innovative, fit-for-purpose approaches to regulation. Digital health technologies, particularly AI-based tools, pose specific challenges to the development and implementation of functional regulation. The approaches of regulatory science and "better regulation" have a critical role in developing and evaluating solutions to these problems and ensuring effective implementation. We describe the divergent approaches of the European Union and the United States in the implementation of new regulatory approaches in digital health, and we consider the United Kingdom as a third example, which is in a unique position of developing a new post-Brexit regulatory framework.