Mariner-9 based simulation of radiative convective temperature changes in the Martian dust-laden atmosphere-soil system

A numerical simulation of radiative, conductive, and convective heat transfer of the Martian dust-laden atmosphere-soil system is presented with particular emphasis given to heating/cooling in regions of sharp variation in temperature or absorption and its resultant impact on outgoing planetary spectral radiance, as measured by the Mariner 9 IRIS. Thermal coupling between the ground and atmospheric subsystems is modeled by the total heat flux balance at the interface. In the simulation procedure, local thermodynamic equilibrium (LTE) is assumed, and a combined strong-weak line transmission function permits short- and long-range exchanges of energy from the surface toward space. Direct absorption of insolation in the near-IR bands by both silicate dust and CO2 is incorporated

Adaptive designs in clinical trials: why use them, and how to run and report them

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial’s course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented. We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice

Point estimation for adaptive trial designs II: practical considerations and guidance

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred

Developing a model for decision-making around antibiotic prescribing for patients with COVID-19 pneumonia in acute NHS hospitals during the first wave of the COVID-19 pandemic: Qualitative results from the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients (PEACH Study)

\ua9 Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Objective To explore and model factors affecting antibiotic prescribing decision-making early in the pandemic. Design Semistructured qualitative interview study. Setting National Health Service (NHS) trusts/health boards in England and Wales. Participants Clinicians from NHS trusts/health boards in England and Wales. Method Individual semistructured interviews were conducted with clinicians in six NHS trusts/health boards in England and Wales as part of the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients study, a wider study that included statistical analysis of procalcitonin (PCT) use in hospitals during the first wave of the pandemic. Thematic analysis was used to identify key factors influencing antibiotic prescribing decisions for patients with COVID-19 pneumonia during the first wave of the pandemic (March to May 2020), including how much influence PCT test results had on these decisions. Results During the first wave of the pandemic, recommendations to prescribe antibiotics for patients with COVID-19 pneumonia were based on concerns about secondary bacterial infections. However, as clinicians gained more experience with COVID-19, they reported increasing confidence in their ability to distinguish between symptoms and signs caused by SARS-CoV-2 viral infection alone, and secondary bacterial infections. Antibiotic prescribing decisions were influenced by factors such as clinician experience, confidence, senior support, situational factors and organisational influences. A decision-making model was developed. Conclusion This study provides insight into the decision-making process around antibiotic prescribing for patients with COVID-19 pneumonia during the first wave of the pandemic. The importance of clinician experience and of senior review of decisions as factors in optimising antibiotic stewardship is highlighted. In addition, situational and organisational factors were identified that could be optimised. The model presented in the study can be used as a tool to aid understanding of the complexity of the decision-making process around antibiotic prescribing and planning antimicrobial stewardship support in the context of a pandemic. Trial registration number ISRCTN66682918

Performance of seven different paediatric early warning scores to predict critical care admission in febrile children presenting to the emergency department: a retrospective cohort study

Objective Paediatric Early Warning Scores (PEWS) are widely used in the UK, but the heterogeneity across tools and the limited data on their predictive performance represent obstacles to improving best practice. The standardisation of practice through the proposed National PEWS will rely on robust validation. Therefore, we compared the performance of the National PEWS with six other PEWS currently used in NHS hospitals, for their ability to predict critical care (CC) admission in febrile children attending the emergency department (ED). Design Retrospective single-centre cohort study. Setting Tertiary hospital paediatric ED. Participants A total of 11 449 eligible febrile ED attendances were identified from the electronic patient record over a 2-year period. Seven PEWS scores were calculated (Alder Hey, Bedside, Bristol, National, Newcastle and Scotland PEWS, and the Paediatric Observation Priority Score, using the worst observations recorded during their ED stay. Outcomes The primary outcome was CC admission within 48 hours, the secondary outcomes were hospital length of stay (LOS) >48 hours and sepsis-related mortality. Results Of 11 449 febrile children, 134 (1.2%) were admitted to CC within 48 hours of ED presentation, 606 (5.3%) had a hospital LOS >48 hours. 10 (0.09%) children died, 5 (0.04%) were sepsis-related. All seven PEWS demonstrated excellent discrimination for CC admission (range area under the receiver operating characteristic curves (AUC) 0.91–0.95) and sepsis-related mortality (range AUC 0.95–0.99), most demonstrated moderate discrimination for hospital LOS (range AUC 0.69–0.75). In CC admission threshold analyses, bedside PEWS (AUC 0.90; 95% CI 0.86 to 0.93) and National PEWS (AUC 0.90; 0.87–0.93) were the most discriminative, both at a threshold of ≥6. Conclusions Our results support the use of the proposed National PEWS in the paediatric ED for the recognition of suspected sepsis to improve outcomes, but further validation is required in other settings and presentations

The cost-effectiveness of procalcitonin for guiding antibiotic prescribing in individuals hospitalized with COVID-19: part of the PEACH study

\ua9 The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.Background: Many hospitals introduced procalcitonin (PCT) testing to help diagnose bacterial coinfection in individuals with COVID-19, and guide antibiotic decision-making during the COVID-19 pandemic in the UK. Objectives: Evaluating cost-effectiveness of using PCT to guide antibiotic decisions in individuals hospitalized with COVID-19, as part of a wider research programme. Methods: Retrospective individual-level data on patients hospitalized with COVID-19 were collected from 11 NHS acute hospital Trusts and Health Boards from England and Wales, which varied in their use of baseline PCT testing during the first COVID-19 pandemic wave. A matched analysis (part of a wider analysis reported elsewhere) created groups of patients whose PCT was/was not tested at baseline. A model was created with combined decision tree/Markov phases, parameterized with quality-of-life/unit cost estimates from the literature, and used to estimate costs and quality-adjusted life years (QALYs). Cost-effectiveness was judged at a \ua320000/QALY threshold. Uncertainty was characterized using bootstrapping. Results: People who had baseline PCT testing had shorter general ward/ICU stays and spent less time on antibiotics, though with overlap between the groups’ 95% CIs. Those with baseline PCT testing accrued more QALYs (8.76 versus 8.62) and lower costs (\ua39830 versus \ua310 700). The point estimate was baseline PCT testing being dominant over no baseline testing, though with uncertainty: the probability of cost-effectiveness was 0.579 with a 1 year horizon and 0.872 with a lifetime horizon. Conclusions: Using PCT to guide antibiotic therapy in individuals hospitalized with COVID-19 is more likely to be cost-effective than not, albeit with uncertainty

The PERCEIVE quantitative study: PrEdiction of Risk and Communication of outcome following major lower limb amputation: protocol for a collaboratiVE study

BACKGROUND: Accurate prediction of outcomes following surgery with high morbidity and mortality rates is essential for informed shared decision-making between patients and clinicians. It is unknown how accurately healthcare professionals predict outcomes following major lower-limb amputation (MLLA). Several MLLA outcome-prediction tools have been developed. These could be valuable in clinical practice, but most require validation in independent cohorts before routine clinical use can be recommended. The primary aim of this study is to evaluate the accuracy of healthcare professionals’ predictions of outcomes in adult patients undergoing MLLA for complications of chronic limb-threatening ischaemia (CLTI) or diabetes. Secondary aims include the validation of existing outcome-prediction tools. METHOD: This study is an international, multicentre prospective observational study including adult patients undergoing a primary MLLA for CLTI or diabetes. Healthcare professionals’ accuracy in predicting outcomes at 30-days (death, morbidity and MLLA revision) and 1-year (death, MLLA revision and ambulation) will be evaluated. Sixteen existing outcome-prediction tools specific to MLLA will be examined for validity. Data collection began on 1 October 2020; the end of follow-up will be 1 May 2022. The C-statistic, Hosmer–Lemeshow test, reclassification tables and Brier score will be used to evaluate the predictive performance of healthcare professionals and prediction tools, respectively. STUDY REGISTRATION AND DISSEMINATION: This study will be registered locally at each centre in accordance with local policies before commencing data collection, overseen by local clinician leads. Results will be disseminated to all centres, and any subsequent presentation(s) and/or publication(s) will follow a collaborative co-authorship model