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

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 £20 000/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 (£9830 versus £10 700). 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.

Family group conferencing for children and families: Evaluation of implementation, context and effectiveness (Family VOICE). Study protocol.

BACKGROUND: Family group conferences (FGCs) in child welfare bring immediate and wider family members together to decide on the best way to meet a child's needs. Unlike professionally led meetings, the aim is for decisions to be made by or with family members. Qualitative and mixed-method research with FGC participants tends to show positive experiences: most participants feel their voices are heard; FGCs facilitate family-driven solutions and closer relationships-within families and with social workers. Although there is existing literature on FGCs, there is a paucity of robust comparative UK evaluations, i.e., randomised controlled trials or quasi-experimental studies. Comparative studies internationally have focused on a narrow range of outcomes, not recognised the importance of context, and paid little attention to the quality of delivery. Some qualitative studies have considered process and context but there is scant measurement of these. The aims of this study are, firstly, to establish how FGCs improve outcomes for families and what factors vary their quality, and, secondly, to assess longer-term outcomes in terms of service use and associated costs. METHODS: Given the importance of process and context, evaluation informed by realist and complex systems approaches is needed. This multi-method evaluation includes a survey of FGC services in all UK local authorities (n = 212) to map service provision; co-design of programme theory and evaluation measures with family members who have experienced an FGC (n = 16-24) and practitioners (n = 16-24) in two sites; a prospective single-arm study of FGC variability and outcomes after six months; and comparison of service use and costs in FGC participants (n≥300 families) and a control group (n≥1000) after two years using a quasi-experiment. DISCUSSION: This is a pragmatic evaluation of an existing intervention, to identify what mechanisms and contexts influence effective process and longer-term outcomes. The study is registered with Research Registry (ref. 7432).

Evidence of quality of life for hospitalised patients with COVID-19: a scoping review.

Background: Information on the quality of life of people hospitalised with COVID-19 is important, both in assessing the burden of disease and the cost-effectiveness of treatments. However, there were potential barriers to collecting such evidence. Objective: To review the existing evidence on quality of life for people hospitalised with COVID-19, with a focus on the amount of evidence available and methods used. Design: A scoping review with systematic searches. Results: A total of 35 papers were selected for data extraction. The most common study type was economic evaluation (N = 13), followed by cross-sectional (N = 10). All economic evaluations used published utility values for other conditions to represent COVID-19 inpatients' quality of life. The most popular quality-of-life survey measure was the Pittsburgh Sleep Quality Index (N = 8). There were 12 studies that used a mental health-related survey and 12 that used a sleep-related survey. Five studies used EQ-5D, but only one collected responses from people in the acute phase of COVID-19. Studies reported a negative impact on quality of life for people hospitalised with COVID-19, although many studies did not include a formal comparison group. Limitations: Although it used systematic searches, this was not a full systematic review. Conclusion: Quality-of-life data were collected from people hospitalised with COVID-19 from relatively early in the pandemic. However, there was a lack of consensus as to what survey measures to use, and few studies used generic health measures. Economic evaluations for COVID-19 treatments did not use utilities collected from people with COVID-19. In future health crises, researchers should be vigilant for opportunities to collect quality-of-life data from hospitalised patients but should try to co-ordinate as well as ensuring generic health measures are used more. Funding: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number NIHR132254.

Quality of life can be measured using short, simple surveys. It is important to assess quality of life in this way, as it can show how health conditions affect people. Quality-of-life surveys can also be used to measure how treatments benefit people and to judge whether it is cost-effective to fund them. COVID-19 is a new disease, with new treatments developed to treat it. COVID-19 also created possible barriers to collecting quality-of-life survey data, especially from people in hospital at the start of the pandemic. This paper reviews studies which report data on quality of life for people hospitalised with COVID-19, especially how much evidence is available and how the studies were carried out. There were 35 studies included in the review. Of these, 13 assessed how cost-effective treatments for COVID-19 were. None of them collected survey responses directly from patients. Instead, they used data previously collected from people with other conditions such as influenza to represent people with COVID-19's quality of life. The studies which did collect data from patients used a wide variety of different surveys, which made comparing their results difficult. Mental health-related surveys were used by 12 studies, and a further 12 used sleep-related surveys. Relatively few studies used general surveys which could assess the overall effect of COVID-19 on people's quality of life. In future health crises, we recommend using more general quality-of-life measures. We also recommend that researchers co-ordinate to reduce the number of different surveys they use, as this will make comparing results easier.

Long-term risk prediction after major lower limb amputation: 1-year results of the PERCEIVE study.

BACKGROUND: Decision-making when considering major lower limb amputation is complex and requires individualized outcome estimation. It is unknown how accurate healthcare professionals or relevant outcome prediction tools are at predicting outcomes at 1-year after major lower limb amputation. METHODS: An international, multicentre prospective observational study evaluating healthcare professional accuracy in predicting outcomes 1 year after major lower limb amputation and evaluation of relevant outcome prediction tools identified in a systematic search of the literature was undertaken. Observed outcomes at 1 year were compared with: healthcare professionals' preoperative predictions of death (surgeons and anaesthetists), major lower limb amputation revision (surgeons) and ambulation (surgeons, specialist physiotherapists and vascular nurse practitioners); and probabilities calculated from relevant outcome prediction tools. RESULTS: A total of 537 patients and 2244 healthcare professional predictions of outcomes were included. Surgeons and anaesthetists had acceptable discrimination (C-statistic = 0.715), calibration and overall performance (Brier score = 0.200) when predicting 1-year death, but performed worse when predicting major lower limb amputation revision and ambulation (C-statistics = 0.627 and 0.662 respectively). Healthcare professionals overestimated the death and major lower limb amputation revision risks. Consultants outperformed trainees, especially when predicting ambulation. Allied healthcare professionals marginally outperformed surgeons in predicting ambulation. Two outcome prediction tools (C-statistics = 0.755 and 0.717, Brier scores = 0.158 and 0.178) outperformed healthcare professionals' discrimination, calibration and overall performance in predicting death. Two outcome prediction tools for ambulation (C-statistics = 0.688 and 0.667) marginally outperformed healthcare professionals. CONCLUSION: There is uncertainty in predicting 1-year outcomes following major lower limb amputation. Different professional groups performed comparably in this study. Two outcome prediction tools for death and two for ambulation outperformed healthcare professionals and may support shared decision-making.

Identifying critically ill children in Malawi: A modified qSOFA score for low-resource settings.

In low-resource settings, a reliable bedside score for timely identification of children at risk of dying, could help focus resources and improve survival. The rapid bedside Liverpool quick Sequential Organ Failure Assessment (LqSOFA) uses clinical parameters only and performed well in United Kingdom cohorts. A similarly quick clinical assessment-only score has however not yet been developed for paediatric populations in sub-Saharan Africa. In a development cohort of critically ill children in Malawi, we calculated the LqSOFA scores using age-adjusted heart rate and respiratory rate, capillary refill time and Blantyre Coma Scale, and evaluated its prognostic performance for mortality. An improved score, the Blantyre qSOFA (BqSOFA), was developed (omitting heart rate, adjusting respiratory rate cut-off values and adding pallor), subsequently validated in a second cohort of Malawian children, and compared with an existing score (FEAST-PET). Prognostic performance for mortality was evaluated using area under the receiver operating characteristic curve (AUC). Mortality was 15.4% in the development (N = 493) and 22.0% in the validation cohort (N = 377). In the development cohort, discriminative ability (AUC) of the LqSOFA to predict mortality was 0.68 (95%-CI: 0.60-0.76). The BqSOFA and FEAST-PET yielded AUCs of 0.84 (95%-CI:0.79-0.89) and 0.83 (95%-CI:0.77-0.89) in the development cohort, and 0.74 (95%-CI:0.68-0.79) and 0.76 (95%-CI:0.70-0.82) in the validation cohort, respectively. We developed a simple prognostic score for Malawian children based on four clinical parameters which performed as well as a more complex score. The BqSOFA might be used to promptly identify critically ill children at risk of dying and prioritize hospital care in low-resource settings.

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).

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.

Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing.

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals "to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines". The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing - making anonymised individual-level clinical trial data available to other investigators for further use - is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council's Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic.

HD-DRUM, a Tablet-Based Drumming Training App Intervention for People With Huntington Disease: App Development Study.

BACKGROUND: Huntington disease (HD) is a neurodegenerative condition that leads to progressive loss of cognitive-executive and motor functions, largely due to basal ganglia (BG) atrophy. Currently, there are no therapeutic interventions tailored to address executive and motor dysfunction in people with HD. Music-based interventions may aid executive abilities by compensating for impaired BG-reliant timing and rhythm generation using external rhythmic beats. Here, we applied an integrated knowledge translation (IKT) framework to co-design a tablet-based rhythmic drumming training app (HD-DRUM) to stimulate executive and motor abilities in people with HD. OBJECTIVE: The primary aim was to develop the HD-DRUM app for at-home use that addressed the accessibility needs of people with HD and allowed for the quantification of performance improvements and adherence for controlled clinical evaluation. METHODS: The IKT framework was applied to iteratively refine the design of HD-DRUM. This process involved 3 phases of knowledge user engagement and co-design: a web-based survey of people with HD (n=29) to inform about their accessibility needs, usability testing of tablet-based touch screens as hardware solutions, and usability testing of the design and build of HD-DRUM to meet the identified accessibility needs of people affected by HD and their clinicians (n=12). RESULTS: The survey identified accessibility problems due to cognitive and motor control impairments such as difficulties in finding and navigating through information and using PC keyboards and mouses to interact with apps. Tablet-based touch screens were identified as feasible and accessible solutions for app delivery. Key elements to ensure that the app design and build met the needs of people with HD were identified and implemented. These included the facilitation of intuitive navigation through the app using large and visually distinctive buttons; the use of audio and visual cues as training guides; and gamification, positive feedback, and drumming to background music as a means to increase motivation and engagement. The co-design development process resulted in the proof-of-concept HD-DRUM app that is described here according to the Template for Intervention Description and Replication checklist. HD-DRUM can be used at home, allowing the quantification of performance improvements and adherence for clinical evaluation, matching of training difficulty to users' performance levels using gamification, and future scale-up to reach a wide range of interested users. CONCLUSIONS: Applying an IKT-based co-design framework involving knowledge user engagement allowed for the iterative refinement of the design and build of the tablet-based HD-DRUM app intervention, with the aim of stimulating BG-reliant cognitive and motor functions. Mapping the intervention against the Template for Intervention Description and Replication framework to describe complex interventions allowed for the detailed description of the HD-DRUM intervention and identification of areas that required refinement before finalizing the intervention protocol.

A tutorial comparing different covariate balancing methods with an application evaluating the causal effects of substance use treatment programs for adolescents.

Randomized controlled trials are the gold standard for measuring causal effects. However, they are often not always feasible, and causal treatment effects must be estimated from observational data. Observational studies do not allow robust conclusions about causal relationships unless statistical techniques account for the imbalance of pretreatment confounders across groups and key assumptions hold. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the observed imbalances between treatment groups by weighting the groups to look alike on the observed confounders. Notably, there are many methods available to estimate PSBW. However, it is unclear a priori which will achieve the best trade-off between covariate balance and effective sample size for a given application. Moreover, it is critical to assess the validity of key assumptions required for robust estimation of the needed treatment effects, including the overlap and no unmeasured confounding assumptions. We present a step-by-step guide to the use of PSBW for estimation of causal treatment effects that includes steps on how to evaluate overlap before the analysis, obtain estimates of PSBW using multiple methods and select the optimal one, check for covariate balance on multiple metrics, and assess sensitivity of findings (both the estimated treatment effect and statistical significance) to unobserved confounding. We illustrate the key steps using a case study examining the relative effectiveness of substance use treatment programs and provide a user-friendly Shiny application that can implement the proposed steps for any application with binary treatments.

Biomarker-guided duration of antibiotic treatment in children hospitalised with confirmed or suspected bacterial infection: statistical analysis plan for the BATCH trial and PRECISE sub-study.

INTRODUCTION: The BATCH trial is a multi-centre randomised controlled trial to compare procalcitonin-guided management of severe bacterial infection in children with current management. PRECISE is a mechanistic sub-study embedded into the BATCH trial. This paper describes the statistical analysis plan for the BATCH trial and PRECISE sub-study. METHODS: The BATCH trial will assess the effectiveness of an additional procalcitonin test in children (aged 72 h to 18 years) hospitalised with suspected or confirmed bacterial infection to guide antimicrobial prescribing decisions. Participants will be enrolled in the trial from randomisation until day 28 follow-up. The co-primary outcomes are duration of intravenous antibiotic use and a composite safety outcome. Target sample size is 1942 patients, based on detecting a 1-day reduction in intravenous antibiotic use (90% power, two-sided) and on a non-inferiority margin of 5% risk difference in the composite safety outcome (90% power, one-sided), while allowing for up to 10% loss to follow-up. RESULTS: Baseline characteristics will be summarised overall, by trial arm, and by whether patients were recruited before or after the pause in recruitment due to the COVID-19 pandemic. In the primary analysis, duration of intravenous antibiotic use will be tested for superiority using Cox regression, and the composite safety outcome will be tested for non-inferiority using logistic regression. The intervention will be judged successful if it reduces the duration of intravenous antibiotic use without compromising safety. Secondary analyses will include sensitivity analyses, pre-specified subgroup analyses, and analysis of secondary outcomes. Two sub-studies, including PRECISE, involve additional pre-specified subgroup analyses. All analyses will be adjusted for the balancing factors used in the randomisation, namely centre and patient age. CONCLUSION: We describe the statistical analysis plan for the BATCH trial and PRECISE sub-study, including definitions of clinical outcomes, reporting guidelines, statistical principles, and analysis methods. The trial uses a design with co-primary superiority and non-inferiority endpoints. The analysis plan has been written prior to the completion of follow-up. TRIAL REGISTRATION: BATCH: ISRCTN11369832, registered 20 September 2017, doi.org/10.1186/ISRCTN11369832. PRECISE: ISRCTN14945050, registered 17 December 2020, doi.org/10.1186/ISRCTN14945050.

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.

Effectiveness and cost-effectiveness of a personalised self-management intervention for living with long COVID: protocol for the LISTEN randomised controlled trial.

BACKGROUND: Individuals living with long COVID experience multiple, interacting and fluctuating symptoms which can have a dramatic impact on daily living. The aim of the Long Covid Personalised Self-managemenT support EvaluatioN (LISTEN) trial is to evaluate effects of the LISTEN co-designed self-management support intervention for non-hospitalised people living with long COVID on participation in routine activities, social participation, emotional well-being, quality of life, fatigue, and self-efficacy. Cost-effectiveness will also be evaluated, and a detailed process evaluation carried out to understand how LISTEN is implemented. METHODS: The study is a pragmatic randomised effectiveness and cost-effectiveness trial in which a total of 558 non-hospitalised people with long COVID will be randomised to either the LISTEN intervention or usual care. Recruitment strategies have been developed with input from the LISTEN Patient and Public Involvement and Engagement (PPIE) advisory group and a social enterprise, Diversity and Ability, to ensure inclusivity. Eligible participants can self-refer into the trial via a website or be referred by long COVID services. All participants complete a range of self-reported outcome measures, online, at baseline, 6 weeks, and 3 months post randomisation (the trial primary end point). Those randomised to the LISTEN intervention are offered up to six one-to-one sessions with LISTEN-trained intervention practitioners and given a co-designed digital resource and paper-based book. A detailed process evaluation will be conducted alongside the trial to inform implementation approaches should the LISTEN intervention be found effective and cost-effective. DISCUSSION: The LISTEN trial is evaluating a co-designed, personalised self-management support intervention (the LISTEN intervention) for non-hospitalised people living with long COVID. The design has incorporated extensive strategies to minimise participant burden and maximise access. Whilst the duration of follow-up is limited, all participants are approached to consent for long-term follow-up (subject to additional funding being secured). TRIAL REGISTRATION: LISTEN ISRCTN36407216. Registered on 27/01/2022.

Point estimation for adaptive trial designs I: A methodological review.

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

Procalcitonin Evaluation of Antibiotic Use in COVID-19 Hospitalised Patients (PEACH): Protocol for a Retrospective Observational Study.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Although COVID-19 is a viral illness, many patients admitted to hospital are prescribed antibiotics, based on concerns that COVID-19 patients may experience secondary bacterial infections, and the assumption that they may respond well to antibiotic therapy. This has led to an increase in antibiotic use for some hospitalised patients at a time when accumulating antibiotic resistance is a major global threat to health. Procalcitonin (PCT) is an inflammatory marker measured in blood samples and widely recommended to help diagnose bacterial infections and guide antibiotic treatment. The PEACH study will compare patient outcomes from English and Welsh hospitals that used PCT testing during the first wave of the COVID-19 pandemic with those from hospitals not using PCT. It will help to determine whether, and how, PCT testing should be used in the NHS in future waves of COVID-19 to protect patients from antibiotic overuse. PEACH is a retrospective observational cohort study using patient-level clinical data from acute hospital Trusts and Health Boards in England and Wales. The primary objective is to measure the difference in antibiotic use between COVID-19 patients who did or did not have PCT testing at the time of diagnosis. Secondary objectives include measuring differences in length of stay, mortality, intensive care unit admission, and resistant bacterial infections between these groups.

Mid-Regional Pro-Adrenomedullin in Combination With Pediatric Early Warning Scores for Risk Stratification of Febrile Children Presenting to the Emergency Department: Secondary Analysis of a Nonprespecified United Kingdom Cohort Study.

OBJECTIVES: Current sepsis guidelines do not provide good risk stratification of subgroups in whom prompt IV antibiotics and fluid resuscitation might of benefit. We evaluated the utility of mid-regional pro-adrenomedullin (MR-proADM) in identification of patient subgroups at risk of requiring PICU or high-dependency unit (HDU) admission or fluid resuscitation. DESIGN: Secondary, nonprespecified analysis of prospectively collected dataset. SETTING: Pediatric Emergency Department in a United Kingdom tertiary center. PATIENTS: Children less than 16 years old presenting with fever and clinical indication for venous blood sampling ( n = 1,183). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcome measures were PICU/HDU admission or administration of fluid resuscitation, with a secondary outcome of definite or probable bacterial infection. Biomarkers were measured on stored plasma samples and children phenotyped into bacterial and viral groups using a previously published algorithm. Of the 1,183 cases, 146 children (12.3%) required fluids, 48 (4.1%) were admitted to the PICU/HDU, and 244 (20.6%) had definite or probable bacterial infection. Area under the receiver operating characteristic (AUC) was used to assess performance. MR-proADM better predicted fluid resuscitation (AUC, 0.73; 95% CI, 0.67-0.78), than both procalcitonin (AUC, 0.65; 95% CI, 0.59-0.71) and Pediatric Early Warning Score (PEWS: AUC, 0.62; 95% CI, 0.56-0.67). PEWS alone showed good accuracy for PICU/HDU admission 0.83 (0.78-0.89). Patient subgroups with high MR-proADM (≥ 0.7 nmol/L) and high procalcitonin (≥ 0.5 ng/mL) had increased association with PICU/HDU admission, fluid resuscitation, and bacterial infection compared with subgroups with low MR-proADM (< 0.7 nmol/L). For children with procalcitonin less than 0.5 ng/mL, high MR-proADM improved stratification for fluid resuscitation only. CONCLUSIONS: High MR-proADM and high procalcitonin were associated with increased likelihood of subsequent disease progression. Incorporating MR-proADM into clinical risk stratification may be useful in clinician decision-making regarding initiation of IV antibiotics, fluid resuscitation, and escalation to PICU/HDU admission.

Short-term risk prediction after major lower limb amputation: PERCEIVE study.

BACKGROUND: The accuracy with which healthcare professionals (HCPs) and risk prediction tools predict outcomes after major lower limb amputation (MLLA) is uncertain. The aim of this study was to evaluate the accuracy of predicting short-term (30 days after MLLA) mortality, morbidity, and revisional surgery. METHODS: The PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study was launched on 1 October 2020. It was an international multicentre study, including adults undergoing MLLA for complications of peripheral arterial disease and/or diabetes. Preoperative predictions of 30-day mortality, morbidity, and MLLA revision by surgeons and anaesthetists were recorded. Probabilities from relevant risk prediction tools were calculated. Evaluation of accuracy included measures of discrimination, calibration, and overall performance. RESULTS: Some 537 patients were included. HCPs had acceptable discrimination in predicting mortality (931 predictions; C-statistic 0.758) and MLLA revision (565 predictions; C-statistic 0.756), but were poor at predicting morbidity (980 predictions; C-statistic 0.616). They overpredicted the risk of all outcomes. All except three risk prediction tools had worse discrimination than HCPs for predicting mortality (C-statistics 0.789, 0.774, and 0.773); two of these significantly overestimated the risk compared with HCPs. SORT version 2 (the only tool incorporating HCP predictions) demonstrated better calibration and overall performance (Brier score 0.082) than HCPs. Tools predicting morbidity and MLLA revision had poor discrimination (C-statistics 0.520 and 0.679). CONCLUSION: Clinicians predicted mortality and MLLA revision well, but predicted morbidity poorly. They overestimated the risk of mortality, morbidity, and MLLA revision. Most short-term risk prediction tools had poorer discrimination or calibration than HCPs. The best method of predicting mortality was a statistical tool that incorporated HCP estimation.

Practical guidance for running late-phase platform protocols for clinical trials: lessons from experienced UK clinical trials units.

BACKGROUND: Late-phase platform protocols (including basket, umbrella, multi-arm multi-stage (MAMS), and master protocols) are generally agreed to be more efficient than traditional two-arm clinical trial designs but are not extensively used. We have gathered the experience of running a number of successful platform protocols together to present some operational recommendations. METHODS: Representatives of six UK clinical trials units with experience in running late-phase platform protocols attended a 1-day meeting structured to discuss various practical aspects of running these trials. We report and give guidance on operational aspects which are either harder to implement compared to a traditional late-phase trial or are specific to platform protocols. RESULTS: We present a list of practical recommendations for trialists intending to design and conduct late-phase platform protocols. Our recommendations cover the entire life cycle of a platform trial: from protocol development, obtaining funding, and trial set-up, to a wide range of operational and regulatory aspects such as staffing, oversight, data handling, and data management, to the reporting of results, with a particular focus on communication with trial participants and stakeholders as well as public and patient involvement. DISCUSSION: Platform protocols enable many questions to be answered efficiently to the benefit of patients. Our practical lessons from running platform trials will support trial teams in learning how to run these trials more effectively and efficiently.

Practical guidance for planning resources required to support publicly-funded adaptive clinical trials.

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.