Showing papers by "Keele University published in 2019"
TL;DR: This work presents PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool to assess the ROB and concerns regarding the applicability of diagnostic and prognostic prediction model studies, and develops the accompanying explanation and elaboration document.
Abstract: Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance. PROBAST enables a focused and transparent approach to assessing the ROB and applicability of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be used more generally in critical appraisal of prediction model studies. Potential users include organizations supporting decision making, researchers and clinicians who are interested in evidence-based medicine or involved in guideline development, journal editors, and manuscript reviewers.
866 citations
TL;DR: The rationale behind the domains and signaling questions, how to use them, and how to reach domain-level and overall judgments about ROB and applicability of primary studies to a review question are described.
Abstract: Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.
576 citations
TL;DR: The minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox‐Snell R2.
Abstract: When designing a study to develop a new prediction model with binary or time-to-event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R2 , and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox-Snell R2 , which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.
425 citations
TL;DR: Systematic reviews and meta-analyses are needed that summarise the evidence about the prognostic value of particular factors and the key steps involved in this review process are described.
Abstract: Prognostic factors are associated with the risk of future health outcomes in individuals with a particular health condition or some clinical start point (eg, a particular diagnosis). Research to identify genuine prognostic factors is important because these factors can help improve risk stratification, treatment, and lifestyle decisions, and the design of randomised trials. Although thousands of prognostic factor studies are published each year, often they are of variable quality and the findings are inconsistent. Systematic reviews and meta-analyses are therefore needed that summarise the evidence about the prognostic value of particular factors. In this article, the key steps involved in this review process are described.
346 citations
TL;DR: This review defines the current knowledge base of several of theHypoxia-instigated modifications in cancer cell metabolism and exemplifies the correlation between metabolic change and its support of the hypoxic-adapted malignancy.
Abstract: While oxygen is critical to the continued existence of complex organisms, extreme levels of oxygen within a system, known as hypoxia (low levels of oxygen) and hyperoxia (excessive levels of oxygen), potentially promote stress within a defined biological environment. The consequences of tissue hypoxia, a result of a defective oxygen supply, vary in response to the gravity, extent and environment of the malfunction. Persistent pathological hypoxia is incompatible with normal biological functions, and as a result, multicellular organisms have been compelled to develop both organism-wide and cellular-level hypoxia solutions. Both direct, including oxidative phosphorylation down-regulation and inhibition of fatty-acid desaturation, and indirect processes, including altered hypoxia-sensitive transcription factor expression, facilitate the metabolic modifications that occur in response to hypoxia. Due to the dysfunctional vasculature associated with large areas of some cancers, sections of these tumors continue to develop in hypoxic environments. Crucial to drug development, a robust understanding of the significance of these metabolism changes will facilitate our understanding of cancer cell survival. This review defines our current knowledge base of several of the hypoxia-instigated modifications in cancer cell metabolism and exemplifies the correlation between metabolic change and its support of the hypoxic-adapted malignancy.
323 citations
TL;DR: The findings suggest that upward appearance comparisons on social media may promote increased body image concerns in young adult women.
148 citations
TL;DR: Results show a strong association between health literacy, internet access and use, and the movement towards digitisation of health information and services should also consider digital skills development to enable people to utilise digital technology more effectively, especially among traditionally hard-to-reach communities.
Abstract: This article aims to examine the socio-demographic characteristics associated with access and use of Internet for health-related purposes and its relationship with health literacy. Data were drawn from a health literacy survey (N = 1046) and analysed using logistic regression. Results show a strong association between health literacy, internet access and use. Socio-demographic characteristics particularly age, education, income, perceived health and social isolation also predict internet access. Thus, in addition to widening access, the movement towards digitisation of health information and services should also consider digital skills development to enable people to utilise digital technology more effectively, especially among traditionally hard-to-reach communities.
148 citations
TL;DR: This editorial aims to provide some general guidance on how to report the most common types of non-randomised pilot and feasibility studies that are submitted to the journal.
Abstract: As the number of submissions to Pilot and Feasibility Studies increases, there is a need for good quality reporting guidelines to help researchers tailor their reports in a way that is consistent and helpful to other readers. The publication in 2016 of the CONSORT extension to pilot and feasibility trials filled a much-needed gap, but there still remains some uncertainty as to how to report pilot and feasibility studies that are not randomised. This editorial aims to provide some general guidance on how to report the most common types of non-randomised pilot and feasibility studies that are submitted to the journal. We recommend using the CONSORT extension to pilot and feasibility trials as the main reference document—it includes detailed elaboration and explanation of each item, and in most cases, simple adaptation, or non-use of items that are not applicable, will suffice. Several checklists found on the Equator website may provide helpful supplementary guidance, when used alongside the CONSORT extension, and we give some examples.
143 citations
TL;DR: In this article, the authors provide a comprehensive view of strategic green marketing and its impact on competitive advantage and reveal the moderating role of internal green marketing actions towards the development of a sustained competitive advantage.
143 citations
TL;DR: An overview of the primary and secondary control methods under the hierarchical control architecture for DC MGs is provided, specifically, inner loop and droop control approaches in primary control are reviewed.
Abstract: With the rapid development of power electronics technology, microgrid (MG) concept has been widely accepted in the field of electrical engineering. Due to the advantages of direct current (DC) distribution systems such as reduced losses and easy integration with energy storage resources, DC MGs have drawn increasing attentions nowadays. With the increase of distributed generation, a DC MG consisting of multiple sources is a hot research topic. The challenge in such a multi-source DC MG is to provide voltage support and good power sharing performance. As the control strategy plays an important role in ensuring MG’s power quality and efficiency, a comprehensive review of the state-of-art control approaches in DC MGs is necessary. This paper provides an overview of the primary and secondary control methods under the hierarchical control architecture for DC MGs. Specifically, inner loop and droop control approaches in primary control are reviewed. Centralized, distributed, and decentralized approach based secondary control is discussed in details. Key findings and future trends are also presented at last.
142 citations
Mayo Clinic1, Sandia National Laboratories2, University of New Mexico3, Manchester Metropolitan University4, Swansea University5, University of Strathclyde6, University of Liverpool7, Queen's University Belfast8, Leidos9, Trinity College, Dublin10, SINTEF11, Swiss Federal Laboratories for Materials Science and Technology12, University of Grenoble13, Karolinska Institutet14, Technical University of Denmark15, Stockholm University16, University of California, Los Angeles17, Keele University18, University of North Carolina at Chapel Hill19, Chinese Academy of Sciences20, University of Jena21, University of Texas Southwestern Medical Center22, University of Texas at Austin23, Santa Clara University24, University of Birmingham25, Newcastle University26, University of Porto27, University of Helsinki28, MedImmune29, Harvard University30, University of Pittsburgh31, Tel Aviv University32, Northeastern University33, University of Santiago de Compostela34, University of Fribourg35, University of Oklahoma36, Zhejiang University37, Wake Forest University38, University of Brescia39, Houston Methodist Hospital40, Cornell University41, University of California, San Diego42, University of Texas at Dallas43, Princess Margaret Cancer Centre44
TL;DR: Following the authors' call to join in the discussion over the suitability of implementing a reporting checklist for bio–nano papers, the community responds.
Abstract: Following our call to join in the discussion over the suitability of implementing a reporting checklist for bio–nano papers, the community responds.
TL;DR: In this paper, the authors used the Clinical Practice Research Datalink (CPRD) linked with the Index of Multiple Deprivation (IMD) data to identify patients diagnosed with Type 2 diabetes between 2007 and 2017.
Abstract: The presence of additional chronic conditions has a significant impact on the treatment and management of type 2 diabetes (T2DM). Little is known about the patterns of comorbidities in this population. The aims of this study are to quantify comorbidity patterns in people with T2DM, to estimate the prevalence of six chronic conditions in 2027 and to identify clusters of similar conditions. We used the Clinical Practice Research Datalink (CPRD) linked with the Index of Multiple Deprivation (IMD) data to identify patients diagnosed with T2DM between 2007 and 2017. 102,394 people met the study inclusion criteria. We calculated the crude and age-standardised prevalence of 18 chronic conditions present at and after the T2DM diagnosis. We analysed longitudinally the 6 most common conditions and forecasted their prevalence in 2027 using linear regression. We used agglomerative hierarchical clustering to identify comorbidity clusters. These analyses were repeated on subgroups stratified by gender and deprivation. More people living in the most deprived areas had ≥ 1 comorbidities present at the time of diagnosis (72% of females; 64% of males) compared to the most affluent areas (67% of females; 59% of males). Depression prevalence increased in all strata and was more common in the most deprived areas. Depression was predicted to affect 33% of females and 15% of males diagnosed with T2DM in 2027. Moderate clustering tendencies were observed, with concordant conditions grouped together and some variations between groups of different demographics. Comorbidities are common in this population, and high between-patient variability in comorbidity patterns emphasises the need for patient-centred healthcare. Mental health is a growing concern, and there is a need for interventions that target both physical and mental health in this population.
TL;DR: In this article, the authors explore three key issues: consent, confidentiality and anonymity, and risk of harm in focus group discussion, and outline some strategies that can be adopted in the consent process, in a preliminary briefing session, during moderation of the focus group, and in subsequent debriefing.
Abstract: Focus group methodology generates distinct ethical challenges that do not correspond fully to those raised by one-to-one interviews. This paper explores, in both conceptual and practical terms, three key issues: consent; confidentiality and anonymity; and risk of harm. The principal challenge in obtaining consent lies in giving a clear account of what will take place in the group, owing to unpredictability of the discussion and interaction that will occur. As consent can be seen in terms of creating appropriate expectations in the participant, this may therefore be hard to achieve. Moreover, it is less straightforward for the participant to revoke consent than in one-to-one interviews. Confidentiality and anonymity are potentially problematic because of the researcher’s limited control over what participants may subsequently communicate outside the group. If the group discussion encourages over-disclosure by some participants, this problem becomes more acute. Harm in a focus group may arise from the discussion of sensitive topics, and this may be amplified by the public nature of the discussion. A balance should be struck between avoiding or closing down potentially distressing discussion and silencing the voices of certain participants to whom such discussion may be important or beneficial. As a means of addressing the above issues, we outline some strategies that can be adopted in the consent process, in a preliminary briefing session, during moderation of the focus group, and in a subsequent debriefing, and suggest that these strategies can be employed synergistically so as to reinforce each other.
TL;DR: Findings between ON and the following risk factors were mixed: age, SES, BMI, belonging to a health-related field, exercise engagement, vegetarianism/veganism, body dissatisfaction, and alcohol, tobacco, and drug use.
TL;DR: It is fundamental to promote physical activity in the aging adult, being that a negative effect on balance performance has been seen in the no-intervention control groups.
TL;DR: In this paper, the authors examine the state of sustainable luxury research in marketing and consumer behavior by critically reviewing and synthesizing the growing but fragmented body of scholarly work on sustainable-luxury marketing.
Abstract: Sustainability has become a pervasive issue for the luxury sector, gaining traction with brand managers, scholars, policy-makers, the media, and academia. The purpose of this article is to examine the state of sustainable luxury research in marketing and consumer behaviour by critically reviewing and synthesizing the growing but fragmented body of scholarly work on sustainable-luxury marketing. The article critically assesses where, how, and by whom research on sustainable luxury is being conducted, and it identifies gaps for future investigation. The article reviews research published between 2007 and 2018 within major peer-reviewed English-language scholarly publications in business, marketing, ethics, fashion, food and tourism journals. The research is identified using the keywords sustainable luxury, green luxury, eco-luxury, and organic luxury. Three core themes emerge from this review: (1) consumer concerns and practices; (2) organizational concerns and practices; and (3) international and cross-cultural issues. The review confirms that research on sustainable luxury is significantly under-developed. This article provides the first critical and comprehensive assessment and categorization of the emergent literature streams on sustainable luxury. The authors argue for a broader, deeper, and more critical research agenda on the relationship between sustainability and luxury. Potential avenues for future research on sustainable luxury are proposed, with calls for theoretical and cross-cultural reflections that tackle broader systemic and institutional issues within the field.
TL;DR: It is indicated that interventions that focus solely on education might be less likely to result in positive outcomes than interventions targeting behavioural change and the methodologically strongest evidence (RCTs) is available for body composition and stress.
Abstract: Background: Prior research has investigated various strategies to improve health, wellbeing and the job-related outcomes of nurses. However, the scope of this evidence is not clear and the types of intervention most likely to have positive outcomes are unknown. Objective: To provide an overview and synthesis of the effectiveness of interventions conducted with the goal of improving health, wellbeing and the job-related outcomes of nurses. Methods: A systematic database search was conducted from January 2000 to December 2018, with pre-defined criteria (Cochrane Central Register of Controlled Trials; MEDLINE and PubMed; EMBASE; CINAHL; PsycINFO; and BioMed Central). In total, 136 intervention studies with a total sample of 16,129 participants (range 9–3381) were included and evaluated. Data extraction, quality assessment and risk of bias analyses were performed. Results: Studies included randomised controlled trials (RCTs; n = 52, 38%), randomised crossover design studies (n = 2, 1.5%) and non-randomised pre-post studies with a control group (n = 31, 23%) and without a control group (n = 51, 37.5%). The majority of interventions focused on education, physical activity, mindfulness, or relaxation. Thirty-seven (27%) studies had a multimodal intervention approach. On average, studies had relatively small samples (median = 61; mode = 30) and were conducted predominantly in North America (USA/Canada, n = 53). The findings were mixed overall, with some studies reporting benefits and others finding no effects. Dietary habits was the most successfully improved outcome (8/9), followed by indices of body composition (20/24), physical activity (PA) (11/14), and stress (49/66), with >70% of relevant studies in each of these categories reporting improvements. The lowest success rate was for work-related outcomes (16/32). Separate analysis of RCTs indicated that interventions that focus solely on education might be less likely to result in positive outcomes than interventions targeting behavioural change. Conclusions: Interventions targeting diet, body composition, PA, or stress are most likely to have positive outcomes for nurses’ health and/or wellbeing. The methodologically strongest evidence (RCTs) is available for body composition and stress. Interventions relying solely on educational approaches are least likely to be effective. Organisational outcomes appear to be more challenging to change with lifestyle intervention, likely requiring more complex solutions including changes to the work environment. There is a need for more high-quality evidence since many studies had moderate or high risk of bias and low reporting quality.
TL;DR: This review aimed to evaluate randomized controlled trials comparing the effectiveness of PNE on pain and disability in CLBP.
Qatar University1, Hospital General Universitario Gregorio Marañón2, Mayo Clinic3, Keele University4, Universidade Federal de Minas Gerais5, German Sport University Cologne6, International Olympic Committee7, Manipal University8, National University of Singapore9, University of the West Indies10, University of Iceland11, University of Banja Luka12, Betsi Cadwaladr University Health Board13, Aristotle University of Thessaloniki14, Marmara University15, Inje University16, Fu Jen Catholic University17, University of Auckland18, Tbilisi State Medical University19, Isfahan University of Medical Sciences20, Palacký University, Olomouc21, Oulu University Hospital22, Lithuanian University of Health Sciences23, Edinburgh Napier University24, Shaare Zedek Medical Center25, Norfolk and Norwich University Hospital26, Frederiksberg Hospital27, Cardiovascular Institute of the South28, Karolinska Institutet29, University of British Columbia30, Moncton Hospital31, Beijing United Family Hospital32, University Health Network33
TL;DR: This study ascertained CR availability, volumes and its drivers, and density globally, finding that capacity is grossly insufficient, such that most patients will not derive the benefits associated with participation.
TL;DR: It is proposed that the minimum value of n should meet the following four key criteria: small optimism in predictor effect estimates as defined by a global shrinkage factor, and precise estimation of the mean predicted outcome value (model intercept).
Abstract: In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a linear regression model is developed to predict an individual's outcome value conditional on values of multiple predictors (covariates). To improve model development and reduce the potential for overfitting, a suitable sample size is required in terms of the number of subjects (n) relative to the number of predictor parameters (p) for potential inclusion. We propose that the minimum value of n should meet the following four key criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9; (ii) small absolute difference of ≤ 0.05 in the apparent and adjusted R2 ; (iii) precise estimation (a margin of error ≤ 10% of the true value) of the model's residual standard deviation; and similarly, (iv) precise estimation of the mean predicted outcome value (model intercept). The criteria require prespecification of the user's chosen p and the model's anticipated R2 as informed by previous studies. The value of n that meets all four criteria provides the minimum sample size required for model development. In an applied example, a new model to predict lung function in African-American women using 25 predictor parameters requires at least 918 subjects to meet all criteria, corresponding to at least 36.7 subjects per predictor parameter. Even larger sample sizes may be needed to additionally ensure precise estimates of key predictor effects, especially when important categorical predictors have low prevalence in certain categories.
TL;DR: Why care for low back pain that is concordant with guidelines requires system-wide changes is discussed and policies that, by shifting resources from unnecessary care to guideline-concordant care forLow back pain, could be cost-neutral and have widespread impact are suggested.
Abstract: Low back pain is the leading cause of years lived with disability globally. In 2018, an international working group called on the World Health Organization to increase attention on the burden of low back pain and the need to avoid excessively medical solutions. Indeed, major international clinical guidelines now recognize that many people with low back pain require little or no formal treatment. Where treatment is required the recommended approach is to discourage use of pain medication, steroid injections and spinal surgery, and instead promote physical and psychological therapies. Many health systems are not designed to support this approach. In this paper we discuss why care for low back pain that is concordant with guidelines requires system-wide changes. We detail the key challenges of low back pain care within health systems. These include the financial interests of pharmaceutical and other companies; outdated payment systems that favour medical care over patients' self-management; and deep-rooted medical traditions and beliefs about care for back pain among physicians and the public. We give international examples of promising solutions and policies and practices for health systems facing an increasing burden of ineffective care for low back pain. We suggest policies that, by shifting resources from unnecessary care to guideline-concordant care for low back pain, could be cost-neutral and have widespread impact. Small adjustments to health policy will not work in isolation, however. Workplace systems, legal frameworks, personal beliefs, politics and the overall societal context in which we experience health, will also need to change.
TL;DR: The purpose of this paper is to introduce the basics of social media usage (with the focus on Twitter), provide perspective on best social media practices in academic and clinical cardiovascular medicine, and present a vision for social media and the future of cardiovascular medicine.
TL;DR: It is shown that most cites in Bohai Rim have high-high, low-low aggregation characteristics and the economic effects of per capita GDP, industrial structure upgrades on eco-efficiency are determined according to a spatial Durbin model.
University of Sydney1, Keele University2, Yale University3, University of Oxford4, The George Institute for Global Health5, International Society of Nephrology6, Pondicherry Institute of Medical Sciences7, University of Alberta8, University of Bristol9, Southmead Hospital10, North Bristol NHS Trust11, University of Guadalajara12, University of Calgary13, Translational Research Institute14, University of Queensland15, Princess Alexandra Hospital16, Icahn School of Medicine at Mount Sinai17, University of Zurich18, Brigham and Women's Hospital19, Deakin University20, University of Cape Town21, Stellenbosch University22, Pontifícia Universidade Católica do Paraná23, The Aga Khan University Hospital24, Ghent University Hospital25, World Health Organization26, Chang Gung University27, The Catholic University of America28
TL;DR: The purpose was to develop a strategic plan to improve worldwide access to integrated ESKD care, by identifying and prioritizing key activities across 8 themes, and an overarching performance framework were developed for each theme.
University of La Laguna1, Spanish National Research Council2, Max Planck Society3, University of Göttingen4, Heidelberg University5, Massachusetts Institute of Technology6, Queen Mary University of London7, Harvard University8, Vanderbilt University9, University of Oviedo10, Complutense University of Madrid11, Carnegie Institution for Science12, Keele University13, Cornell University14, Weizmann Institute of Science15, Princeton University16, University of California, Santa Cruz17, Search for extraterrestrial intelligence18, Ames Research Center19, Chinese Academy of Sciences20, University of California, Berkeley21, Institut de Ciències de l'Espai22, University of Hamburg23, Open University24, University of Tokyo25
TL;DR: In this paper, a transiting Earth-size planet around GJ 357, a nearby M2.5 V star, was detected using data from the Transiting Exoplanet Survey Satellite (TESS).
Abstract: We report the detection of a transiting Earth-size planet around GJ 357, a nearby M2.5 V star, using data from the Transiting Exoplanet Survey Satellite (TESS). GJ 357 b (TOI-562.01) is a transiting, hot, Earth-sized planet (T eq = 525 ± 11 K) with a radius of R b = 1.217 ± 0.084 R ⊕ and an orbital period of P b = 3.93 d. Precise stellar radial velocities from CARMENES and PFS, as well as archival data from HIRES, UVES, and HARPS also display a 3.93-day periodicity, confirming the planetary nature and leading to a planetary mass of M b = 1.84 ± 0.31 M ⊕ . In addition to the radial velocity signal for GJ 357 b, more periodicities are present in the data indicating the presence of two further planets in the system: GJ 357 c, with a minimum mass of M c = 3.40 ± 0.46 M ⊕ in a 9.12 d orbit, and GJ 357 d, with a minimum mass of M d = 6.1 ± 1.0 M ⊕ in a 55.7 d orbit inside the habitable zone. The host is relatively inactive and exhibits a photometric rotation period of P rot = 78 ± 2 d. GJ 357 b isto date the second closest transiting planet to the Sun, making it a prime target for further investigations such as transmission spectroscopy. Therefore, GJ 357 b represents one of the best terrestrial planets suitable for atmospheric characterization with the upcoming JWST and ground-based ELTs.
TL;DR: Investigating the phenomenon of a “blood microbiome” by providing a comprehensive description of bacterially derived nucleic acids using a range of complementary molecular and classical microbiological techniques provided further evidence of a core blood microbiome, and insight into the potential source of the bacterial DNA/RNA detected in the blood.
Abstract: The term microbiome describes the genetic material encoding the various microbial populations that inhabit our body. Whilst colonization of various body niches (e.g., the gut) by dynamic communities of microorganisms is now universally accepted, the existence of microbial populations in other "classically sterile" locations, including the blood, is a relatively new concept. The presence of bacteria-specific DNA in the blood has been reported in the literature for some time, yet the true origin of this is still the subject of much deliberation. The aim of this study was to investigate the phenomenon of a "blood microbiome" by providing a comprehensive description of bacterially derived nucleic acids using a range of complementary molecular and classical microbiological techniques. For this purpose we utilized a set of plasma samples from healthy subjects (n = 5) and asthmatic subjects (n = 5). DNA-level analyses involved the amplification and sequencing of the 16S rRNA gene. RNA-level analyses were based upon the de novo assembly of unmapped mRNA reads and subsequent taxonomic identification. Molecular studies were complemented by viability data from classical aerobic and anaerobic microbial culture experiments. At the phylum level, the blood microbiome was predominated by Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. The key phyla detected were consistent irrespective of molecular method (DNA vs. RNA), and consistent with the results of other published studies. In silico comparison of our data with that of the Human Microbiome Project revealed that members of the blood microbiome were most likely to have originated from the oral or skin communities. To our surprise, aerobic and anaerobic cultures were positive in eight of out the ten donor samples investigated, and we reflect upon their source. Our data provide further evidence of a core blood microbiome, and provide insight into the potential source of the bacterial DNA/RNA detected in the blood. Further, data reveal the importance of robust experimental procedures, and identify areas for future consideration.
TL;DR: The authors provide easily applicable and effective recommendations to prevent periprocedural RAO and maximize the chances of access in case of repeat catheterization or coronary artery bypass grafting surgery.
Abstract: Highlights •RAO remains the most frequent post-procedural complication of transradial access, restricting the use of the same radial artery for future procedures, as a conduit for coronary artery bypass grafting or for arteriovenous fistula creation in patients requiring hemodialysis. •The real-world reported incidence of RAO remains high, with wide variability in the uptake of RAO prevention strategies. •The paper reviews the main recent advances in the field of RAO prevention and provides a series of expert recommendations to prevent RAO. •This consensus document is meant to provide guidance to increase the adoption of simple and effective methods to achieve an institutional rate of RAO
TL;DR: Intensive patient education was not more effective than placebo patient education at reducing pain intensity and adding 2 hours of patient education to recommended first-line care for patients with acute low back pain did not improve pain outcomes.
Abstract: Importance Many patients with acute low back pain do not recover with basic first-line care (advice, reassurance, and simple analgesia, if necessary). It is unclear whether intensive patient education improves clinical outcomes for those patients already receiving first-line care. Objective To determine the effectiveness of intensive patient education for patients with acute low back pain. Design, Setting, and Participants This randomized, placebo-controlled clinical trial recruited patients from general practices, physiotherapy clinics, and a research center in Sydney, Australia, between September 10, 2013, and December 2, 2015. Trial follow-up was completed in December 17, 2016. Primary care practitioners invited 618 patients presenting with acute low back pain to participate. Researchers excluded 416 potential participants. All of the 202 eligible participants had low back pain of fewer than 6 weeks’ duration and a high risk of developing chronic low back pain according to Predicting the Inception of Chronic Pain (PICKUP) Tool, a validated prognostic model. Participants were randomized in a 1:1 ratio to either patient education or placebo patient education. Interventions All participants received recommended first-line care for acute low back pain from their usual practitioner. Participants received additional 2 × 1-hour sessions of patient education (information on pain and biopsychosocial contributors plus self-management techniques, such as remaining active and pacing) or placebo patient education (active listening, without information or advice). Main Outcomes and Measures The primary outcome was pain intensity (11-point numeric rating scale) at 3 months. Secondary outcomes included disability (24-point Roland Morris Disability Questionnaire) at 1 week, and at 3, 6, and 12 months. Results Of 202 participants randomized for the trial, the mean (SD) age of participants was 45 (14.5) years and 103 (51.0%) were female. Retention rates were greater than 90% at all time points. Intensive patient education was not more effective than placebo patient education at reducing pain intensity (3-month mean [SD] pain intensity: 2.1 [2.4] vs 2.4 [2.2]; mean difference at 3 months, –0.3 [95% CI, –1.0 to 0.3]). There was a small effect of intensive patient education on the secondary outcome of disability at 1 week (mean difference, –1.6 points on a 24-point scale [95% CI, –3.1 to –0.1]) and 3 months (mean difference, –1.7 points, [95% CI, –3.2 to –0.2]) but not at 6 or 12 months. Conclusions and Relevance Adding 2 hours of patient education to recommended first-line care for patients with acute low back pain did not improve pain outcomes. Clinical guideline recommendations to provide complex and intensive support to high-risk patients with acute low back pain may have been premature. Trial Registration Australian Clinical Trial Registration Number:12612001180808
TL;DR: A historical perspective about the evaluation of AI in healthcare is provided and key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance are examined.
Abstract: Objectives: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. Method: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. Results: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. Conclusion: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.
TL;DR: This work discusses how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome, and addresses how to deal with incomplete availability of study-specific results and summary estimates of the c-statistic, the observed:expected ratio and the calibration slope.
Abstract: It is widely recommended that any developed-diagnostic or prognostic-prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps to summarize overall performance across multiple settings, and reveals under which circumstances the model performs suboptimal (alternative poorer) and may need adjustment We discuss how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome We address how to deal with incomplete availability of study-specific results (performance estimates and their precision), and how to produce summary estimates of the c-statistic, the observed:expected ratio and the calibration slope Furthermore, we discuss the implementation of frequentist and Bayesian meta-analysis methods, and propose novel empirically-based prior distributions to improve estimation of between-study heterogeneity in small samples Finally, we illustrate all methods using two examples: meta-analysis of the predictive performance of EuroSCORE II and of the Framingham Risk Score All examples and meta-analysis models have been implemented in our newly developed R package "metamisc"