SurvInt: a simple tool to obtain precise parametric survival extrapolations.

BACKGROUND: Economic evaluation of emerging health technologies is mandated by agencies such as the National Institute for Health and Care Excellence (NICE) to ensure their cost is proportional to their benefit. To avoid bias, NICE stipulate that the benefit of a treatment is assessed across the lifetime of the patient population, which can be many decades. Unfortunately, follow-up from a clinical trial will not usually cover the required period and the observed follow-up will require extrapolation. For survival data this is often done by selecting a preferred model from a set of candidate parametric models. This approach is limited in that the choice of model is restricted to those originally fitted. What if none of the models are consistent with clinical prediction or external data? METHOD/RESULTS: This paper introduces SurvInt, a tool that estimates the parameters of common parametric survival models which interpolate key survival time co-ordinates specified by the user, which could come from external trials, real world data or expert clinical opinion. This is achieved by solving simultaneous equations based on the survival functions of the parametric models. The application of SurvInt is shown through two examples where traditional parametric modelling did not produce models that were consistent with external data or clinical opinion. Additional features include model averaging, mixture cure models, background mortality, piecewise modelling, restricted mean survival time estimation and probabilistic sensitivity analysis. CONCLUSIONS: SurvInt allows precise parametric survival models to be estimated and carried forward into economic models. It provides access to extrapolations that are consistent with multiple data sources such as observed data and clinical predictions, opening the door to precise exploration of regions of uncertainty/disagreement. SurvInt could avoid the need for post-hoc adjustments for complications such as treatment switching, which are often applied to obtain a plausible survival model but at the cost of introducing additional uncertainty. Phase III clinical trials are not designed with extrapolation in mind, and so it is sensible to consider alternative approaches to predict future survival that incorporate external information.

Fewer native and periprosthetic femoral fracture patients receive an orthogeriatric review and expedited surgery compared to hip fracture patients.

INTRODUCTION: Disproportionate emphasis has been attributed to hip fracture over other femoral fractures through implementation of Best Practice Tariff (BPT).This retrospective comparative observational cohort study aimed to evaluate the epidemiology of native and periprosthetic femoral fractures and establish any disparities in their management relative to hip fractures. METHODS: All patients ⩾60 years admitted with a native or periprosthetic femoral fracture during July 2016-June 2018 were identified using our hospital database. Results were compared to National Hip Fracture Database data over the same period. RESULTS: 58 native femoral, 87 periprosthetic and 1032 hip fractures were identified. (46/58) 79% and 76/87 (89%) of native and periprosthetic femoral fractures were managed operatively. Surgery was performed <36 hours for 34/46 (74%) of native femoral and 33/76 (43%) of periprosthetic fractures compared to 826/1032 (80%) for hips. Median time to surgery was longer in periprosthetic femoral than hip fracture patients (44.7 vs. 21.6 hours; p < 0.0001). Orthogeriatrician review occurred in 24/58 (41%) and 48/87 (55%) of native and periprosthetic fractures compared to 1017/1032 (99%) for hips (p < 0.0001). One year mortality was 35%, 20% and 26% for native femoral, periprosthetic and hip fracture patients. Cox proportional hazard ratio was higher for native femoral than hip fracture patients (1.75; 95% CI, 1.12-2.73). CONCLUSIONS: This study demonstrates large disparities in management of other femoral and periprosthetic fractures compared to hip fractures, specifically time to surgery and orthogeriatrician review. This may have resulted in the comparatively higher mortality rate of native femoral fracture patients. Expansion of the BPT to include the whole femur is likely to improve outcomes.

Mode of birth and development of maternal postnatal post-traumatic stress disorder: A mixed-methods systematic review and meta-analysis.

BACKGROUND: Post-traumatic stress disorder (PTSD) affects approximately 3% of women in the postnatal period, but less is known about risk factors for PTSD than other postnatal mental illnesses. This review aimed to analyze the literature on the impact of mode of birth on postnatal PTSD. METHODS: Searches were undertaken of CINAHL, the Cochrane Library, MEDLINE, PsycINFO, and Scopus for studies investigating the link between mode of birth and postnatal PTSD in high-resource countries from January 1990 to February 2021. Quantitative and qualitative data were collected and synthesized. Meta-analysis was performed with four of the studies, and the rest were analyzed narratively. RESULTS: Twelve quantitative studies, presenting data on 5567 women, and two qualitative studies, with 92 women, were included in the review. Most studies found a significant relationship between mode of birth and maternal PTSD symptoms. Meta-analysis found cesarean birth was more closely associated with PTSD than vaginal delivery (VD) (P = 0.005), emergency cesarean birth (EmCB) more than elective cesarean birth (ElCB) (P < 0.001), instrumental vaginal delivery (IVD) more than spontaneous vaginal delivery (SVD) (P < 0.001), and EmCB more than SVD (P < 0.001). Women who developed PTSD after EmCB felt less in control and less supported than those who did not develop it after the same procedure. Request for repeat ElCB appeared more common among women with pre-existing postnatal PTSD, but this may subsequently leave them feeling dissatisfied and their fears of childbirth unresolved. CONCLUSIONS: Modes of birth involving emergency intervention may be risk factors for the development of postnatal PTSD. Ensuring that women feel supported and in control during emergency obstetric interventions may mediate against this risk.

Efficacy, safety, and dose-dependence of the analgesic effects of opioid therapy for people with osteoarthritis: systematic review and meta-analysis.

OBJECTIVE: To evaluate the efficacy and safety of opioids for analgesic therapy for people with osteoarthritis. STUDY DESIGN: Systematic review and meta-analysis of randomised, placebo-controlled trials of opioid therapies for treating the pain of osteoarthritis. The primary outcome was medium term pain relief (six weeks to less than 12 months). Quality of evidence was assessed with GRADE criteria. DATA SOURCES: MEDLINE, EMBASE, the Cochrane Database of Systematic Reviews and Central Register of Controlled Trials, CINAHL, PsycINFO, AMED, and the WHO International Clinical Trials Registry; trials published to 31 October 2020. DATA SYNTHESIS: We extracted pain, disability, health-related quality of life, and adverse events data for 36 eligible trials (overall dose range: 10-210 oral morphine milligram equivalents [MME] per day). Continuous pain and disability outcomes were converted to common 0-100-point scales; changes of less than ten points were deemed to be very small effects. Differences in dichotomous outcomes were expressed as risk ratios. Data were pooled for meta-analysis in random effects models. The evidence from 19 trials (8965 participants; dose range, 10-126 MME/day) for very small medium term pain relief (mean difference [MD], -4.59 points; 95% CI, -7.17 to -2.02 points) was low quality, as was that from 16 trials (6882 participants; dose range, 10-126 MME/day) for a very small effect on disability (MD, -4.15 points; 95% CI, -6.94 to -1.35 points). Opioid dose was not statistically significantly associated with either degree of pain relief or incidence of adverse events in a meta-regression analysis. Evidence that opioid therapy increased the risk of adverse events (risk ratio, 1.43; 95% CI, 1.29-1.59) was of very low quality. CONCLUSIONS: Opioid medications may provide very small pain and disability benefits for people with osteoarthritis, but may also increase the risk of adverse events. PROSPERO REGISTRATION: CRD42019142813 (prospective).

Biased Survival Predictions When Appraising Health Technologies in Heterogeneous Populations.

INTRODUCTION: Time-to-event data from clinical trials are routinely extrapolated using parametric models to estimate the cost effectiveness of novel therapies, but how this approach performs in the presence of heterogeneous populations remains unknown. METHODS: We performed a simulation study of seven scenarios with varying exponential distributions modelling treatment and prognostic effects across subgroup and complement populations, with follow-up typical of clinical trials used to appraise the cost effectiveness of therapies by agencies such as the UK National Institute for Health and Care Excellence (NICE). We compared established and emerging methods of estimating population life-years (LYs) using parametric models. We also proved analytically that an exponential model fitted to censored heterogeneous survival times sampled from two distinct exponential distributions will produce a biased estimate of the hazard rate and LYs. RESULTS: LYs are underestimated by the methods in the presence of heterogeneity, resulting in either under- or overestimation of the incremental benefit. In scenarios where the overestimation of benefit is likely, which is of interest to the healthcare provider, the method of taking the average LYs from all plausible models has the least bias. LY estimates from complete Kaplan-Meier curves have high variation, suggesting mature data may not be a reliable solution. We explore the effect of increasing trial sample size and accounting for detected treatment-subgroup interactions. CONCLUSIONS: The bias associated with heterogeneous populations suggests that NICE may need to be more cautious when appraising therapies and to consider model averaging or the separate modelling of subgroups when heterogeneity is suspected or detected.

Development of a model to demonstrate the impact of National Institute of Health and Care Excellence cost-effectiveness assessment on health utility for targeted medicines.

Advances in medical technology have led to a better understanding of heterogeneity of diseases and patients, and to the development of targeted medicines. This development is beneficial to society but can come at an increased cost to pharmaceutical manufacturers due to the costs associated with developing and manufacturing a diagnostic test. For such medicines, the conventional pricing structure, where a therapy is approved if it is deemed cost-effective, may not appropriately incentivize targeted drug development. We model the decision-making processes for both the healthcare provider and the pharmaceutical manufacturer, capturing their main priorities, and populate it with information from a recent appraisal by the National Institute of Health and Care Excellence. Healthcare providers prefer a stratified drug to be developed for a subgroup of the population when the drug is on average effective in the subgroup but with a detrimental effect in the complement. Whilst pharmaceutical manufacturers' preferences are similar, regions of disagreement exist. We show how preferences can be aligned by either penalizing the development of a non-stratified drug or rewarding the development of a stratified drug. The cost and position of alignment depends on the true value of health to the healthcare provider, among other parameters.

Factors associated with attendance at screening for breast cancer: a systematic review and meta-analysis.

OBJECTIVE: Attendance at population-based breast cancer (mammographic) screening varies. This comprehensive systematic review and meta-analysis assesses all identified patient-level factors associated with routine population breast screening attendance. DESIGN: CINAHL, Cochrane Library, Embase, Medline, OVID, PsycINFO and Web of Science were searched for studies of any design, published January 1987-June 2019, and reporting attendance in relation to at least one patient-level factor. DATA SYNTHESIS: Independent reviewers performed screening, data extraction and quality appraisal. OR and 95% CIs were calculated for attendance for each factor and random-effects meta-analysis was undertaken where possible. RESULTS: Of 19 776 studies, 335 were assessed at full text and 66 studies (n=22 150 922) were included. Risk of bias was generally low. In meta-analysis, increased attendance was associated with higher socioeconomic status (SES) (n=11 studies; OR 1.45, 95% CI: 1.20 to 1.75); higher income (n=5 studies; OR 1.96, 95% CI: 1.68 to 2.29); home ownership (n=3 studies; OR 2.16, 95% CI: 2.08 to 2.23); being non-immigrant (n=7 studies; OR 2.23, 95% CI: 2.00 to 2.48); being married/cohabiting (n=7 studies; OR 1.86, 95% CI: 1.58 to 2.19) and medium (vs low) level of education (n=6 studies; OR 1.24, 95% CI: 1.09 to 1.41). Women with previous false-positive results were less likely to reattend (n=6 studies; OR 0.77, 95% CI: 0.68 to 0.88). There were no differences by age group or by rural versus urban residence. CONCLUSIONS: Attendance was lower in women with lower SES, those who were immigrants, non-homeowners and those with previous false-positive results. Variations in service delivery, screening programmes and study populations may influence findings. Our findings are of univariable associations. Underlying causes of lower uptake such as practical, physical, psychological or financial barriers should be investigated. TRIAL REGISTRATION NUMBER: CRD42016051597.

Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial-Based Cost-Effectiveness Data.

Economic evaluations conducted alongside randomized controlled trials are a popular vehicle for generating high-quality evidence on the incremental cost-effectiveness of competing health care interventions. Typically, in these studies, resource use (and by extension, economic costs) and clinical (or preference-based health) outcomes data are collected prospectively for trial participants to estimate the joint distribution of incremental costs and incremental benefits associated with the intervention. In this article, we extend the generalized linear mixed-model framework to enable simultaneous modeling of multiple outcomes of mixed data types, such as those typically encountered in trial-based economic evaluations, taking into account correlation of outcomes due to repeated measurements on the same individual and other clustering effects. We provide new wrapper functions to estimate the models in Stata and R by maximum and restricted maximum quasi-likelihood and compare the performance of the new routines with alternative implementations across a range of statistical programming packages. Empirical applications using observed and simulated data from clinical trials suggest that the new methods produce broadly similar results as compared with Stata's merlin and gsem commands and a Bayesian implementation in WinBUGS. We highlight that, although these empirical applications primarily focus on trial-based economic evaluations, the new methods presented can be generalized to other health economic investigations characterized by multivariate hierarchical data structures.

Extrapolating Parametric Survival Models in Health Technology Assessment Using Model Averaging: A Simulation Study.

Previous work examined the suitability of relying on routine methods of model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability of restricted mean survival time (RMST) estimates from trial data by assessing model plausibility and implementing model averaging. We compare our previous methods of selecting a model for extrapolation using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Our methods of model averaging include using equal weighting across models falling within established threshold ranges for AIC and BIC and using BIC-based weighted averages. We apply our plausibility assessment and implement model averaging to the output of our previous simulations, where 10,000 runs of 12 trial-based scenarios were examined. We demonstrate that removing implausible models from consideration reduces the mean squared error associated with the restricted mean survival time (RMST) estimate from each selection method and increases the percentage of RMST estimates that were within 10% of the RMST from the parameters of the sampling distribution. The methods of averaging were superior to selecting a single optimal extrapolation, aside from some of the exponential scenarios where BIC already selected the exponential model. The averaging methods with wide criterion-based thresholds outperformed BIC-weighted averaging in the majority of scenarios. We conclude that model averaging approaches should feature more widely in the appraisal of health technologies where extrapolation is influential and considerable uncertainty is present. Where data demonstrate complicated underlying hazard rates, funders should account for the additional uncertainty associated with these extrapolations in their decision making. Extended follow-up from trials should be encouraged and used to review prices of therapies to ensure a fair price is paid.

Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study.

Extrapolations of parametric survival models fitted to censored data are routinely used in the assessment of health technologies to estimate mean survival, particularly in diseases that potentially reduce the life expectancy of patients. Akaike's information criterion (AIC) and Bayesian information criterion (BIC) are commonly used in health technology assessment alongside an assessment of plausibility to determine which statistical model best fits the data and should be used for prediction of long-term treatment effects. We compare fit and estimates of restricted mean survival time (RMST) from 8 parametric models and contrast models preferred in terms of AIC, BIC, and log-likelihood, without considering model plausibility. We assess the methods' suitability for selecting a parametric model through simulation of data replicating the follow-up of intervention arms for various time-to-event outcomes from 4 clinical trials. Follow-up was replicated through the consideration of recruitment duration and minimum and maximum follow-up times. Ten thousand simulations of each scenario were performed. We demonstrate that the different methods can result in disagreement over the best model and that it is inappropriate to base model selection solely on goodness-of-fit statistics without consideration of hazard behavior and plausibility of extrapolations. We show that typical trial follow-up can be unsuitable for extrapolation, resulting in unreliable estimation of multiple parameter models, and infer that selecting survival models based only on goodness-of-fit statistics is unsuitable due to the high level of uncertainty in a cost-effectiveness analysis. This article demonstrates the potential problems of overreliance on goodness-of-fit statistics when selecting a model for extrapolation. When follow-up is more mature, BIC appears superior to the other selection methods, selecting models with the most accurate and least biased estimates of RMST.

Rapid antigen detection and molecular tests for group A streptococcal infections for acute sore throat: systematic reviews and economic evaluation.

BACKGROUND: Sore throat is a common condition caused by an infection of the airway. Most cases are of a viral nature; however, a number of these infections may be caused by the group A Streptococcus bacterium. Most viral and bacterial sore throat infections resolve spontaneously within a few weeks. Point-of-care testing in primary care has been recognised as an emerging technology for aiding targeted antibiotic prescribing for sore throat in cases that do not spontaneously resolve. OBJECTIVE: Systematically review the evidence for 21 point-of-care tests for detecting group A Streptococcus bacteria and develop a de novo economic model to compare the cost-effectiveness of point-of-care tests alongside clinical scoring tools with the cost-effectiveness of clinical scoring tools alone for patients managed in primary care and hospital settings. DATA SOURCES: Multiple electronic databases were searched from inception to March 2019. The following databases were searched in November and December 2018 and searches were updated in March 2019: MEDLINE [via OvidSP (Health First, Rockledge, FL, USA)], MEDLINE In-Process & Other Non-Indexed Citations (via OvidSP), MEDLINE Epub Ahead of Print (via OvidSP), MEDLINE Daily Update (via OvidSP), EMBASE (via OvidSP), Cochrane Database of Systematic Reviews [via Wiley Online Library (John Wiley & Sons, Inc., Hoboken, NJ, USA)], Cochrane Central Register of Controlled Trials (CENTRAL) (via Wiley Online Library), Database of Abstracts of Reviews of Effects (DARE) (via Centre for Reviews and Dissemination), Health Technology Assessment database (via the Centre for Reviews and Dissemination), Science Citation Index and Conference Proceedings [via the Web of Science™ (Clarivate Analytics, Philadelphia, PA, USA)] and the PROSPERO International Prospective Register of Systematic Reviews (via the Centre for Reviews and Dissemination). REVIEW METHODS: Eligible studies included those of people aged ≥ 5 years presenting with sore throat symptoms, studies comparing point-of-care testing with antibiotic-prescribing decisions, studies of test accuracy and studies of cost-effectiveness. Quality assessment of eligible studies was undertaken. Meta-analysis of sensitivity and specificity was carried out for tests with sufficient data. A decision tree model estimated costs and quality-adjusted life-years from an NHS and Personal Social Services perspective. RESULTS: The searches identified 38 studies of clinical effectiveness and three studies of cost-effectiveness. Twenty-six full-text articles and abstracts reported on the test accuracy of point-of-care tests and/or clinical scores with biological culture as a reference standard. In the population of interest (patients with Centor/McIsaac scores of ≥ 3 points or FeverPAIN scores of ≥ 4 points), point estimates were 0.829 to 0.946 for sensitivity and 0.849 to 0.991 for specificity. There was considerable heterogeneity, even for studies using the same point-of-care test, suggesting that is unlikely that any single study will have accurately captured a test's true performance. There is some randomised controlled trial evidence to suggest that the use of rapid antigen detection tests may help to reduce antibiotic-prescribing rates. Sensitivity and specificity estimates for each test in each age group and care setting combination were obtained using meta-analyses where appropriate. Any apparent differences in test accuracy may not be attributable to the tests, and may have been caused by known differences in the studies, latent characteristics or chance. Fourteen of the 21 tests reviewed were included in the economic modelling, and these tests were not cost-effective within the current National Institute for Health and Care Excellence's cost-effectiveness thresholds. Uncertainties in the cost-effectiveness estimates included model parameter inputs and assumptions that increase the cost of testing, and the penalty for antibiotic overprescriptions. LIMITATIONS: No information was identified for the elderly population or pharmacy setting. It was not possible to identify which test is the most accurate owing to the paucity of evidence. CONCLUSIONS: The systematic review and the cost-effectiveness models identified uncertainties around the adoption of point-of-care tests in primary and secondary care settings. Although sensitivity and specificity estimates are promising, we have little information to establish the most accurate point-of-care test. Further research is needed to understand the test accuracy of point-of-care tests in the proposed NHS pathway and in comparable settings and patient groups. STUDY REGISTRATION: The protocol of the review is registered as PROSPERO CRD42018118653. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 31. See the NIHR Journals Library website for further project information.

Sore throat is a common condition caused by an infection of the airway. Most cases are viral; however, a small number may be caused by the group A Streptococcus bacterium. Most viral and bacterial sore throat infections resolve spontaneously within a few weeks; however, some may be more serious and require antibiotics. Currently, National Institute for Health and Care Excellence guidance recommends the use of clinical scoring tools to identify patients for whom antibiotic treatment is appropriate. Ideally, a throat swab culture should be obtained to identify the organism causing the infection in cases in which diagnosis is uncertain. However, this takes time, causing potential delays in administering the correct treatment. Point-of-care tests can be administered at or near the site of the patient; therefore, they are much faster. Our review considered evidence for the test accuracy and cost-effectiveness of 21 point-of-care tests for detecting group A Streptococcus bacteria. We built an economic model, predicting costs and benefits for adults and children in a primary care or hospital setting. The findings will support the National Institute for Health and Care Excellence to make recommendations about the use of these point-of-care tests for detecting group A Streptococcus bacteria in the NHS in England and Wales. The clinical effectiveness review found 38 relevant studies; of these, 26 reported on the accuracy of point-of-care tests. These studies found wide variation in the accuracy of the tests. The quality of the evidence was weak and there was little information on some of the 21 tests. As the studies were all so different, it was not possible to identify which test is the most accurate. The economic model found considerable uncertainty about how costs and benefits would change if point-of-care tests were introduced in different care settings. Further research is needed to see whether or not point-of-care testing provides value for money.

Systematic review and meta-analysis of the metabolic effects of modified-release hydrocortisone versus standard glucocorticoid replacement therapy in adults with adrenal insufficiency.

CONTEXT: Published studies exploring the metabolic effects of Modified-Release Hydrocortisone (MR-HC) replacement in patients with adrenal insufficiency (AI). OBJECTIVE: To compare metabolic effects of MR-HC with Standard Glucocorticoid (SG) replacement in adults with AI. Randomized control trials (RCTs) were meta-analysed; non-RCT studies described narratively with critical appraisal. DATA SOURCES: PubMed/Medline, EMBASE, CINAHL and CENTRAL were searched to identify relevant articles, published before Aug 2019. STUDY SELECTION: All study types that reported metabolic profile (including anthropometric, glucose and lipid-related parameters), on patients switched from SG to MR-HC replacement. Following independent screening from two reviewers, 390 studies were identified, of which 9 studies were included for review (RCT, n = 2; non-RCT, n = 7). DATA EXTRACTION: Two independent reviewers assessed each paper for bias and data extraction. RESULTS: Meta-analysis from RCTs (n = 2), 104 patients were switched from SG to MR-HC replacement. Combining treatment effects, at 3-months post-therapy switch there was significant reduction in body weight (-0.82 kg; 95% CI: -1.24 kg to -0.40 kg; P < .001) and HbA1c (-0.13%; 95% CI: -0.214% to -0.045%; P = .003). In the sub-group with Diabetes Mellitus (DM), reduction in HbA1C was more pronounced (-0.52%; 95% CI: -0.82% to -0.23%; P < .001). Non-RCT studies showed improved anthropometric measures and glucose metabolism up to 48-months following switch from SG to MR-HC replacement. CONCLUSIONS: In adults with AI, replacement with MR-HC associates with significant improvements in anthropometric measurements and HbA1c compared with SG replacement, particularly those with DM.

A Systematic Review of Economic Evaluations Assessing the Cost-Effectiveness of Licensed Drugs Used for Previously Treated Epidermal Growth Factor Receptor (EGFR) and Anaplastic Lymphoma Kinase (ALK) Negative Advanced/Metastatic Non-Small Cell Lung Cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the most commonly diagnosed cancers. There are many published studies of cost-effectiveness analyses of licensed treatments, but no study has compared these studies or their approaches simultaneously. OBJECTIVE: To investigate the methodology used in published economic analyses of licensed interventions for previously treated advanced/metastatic NSCLC in patients without anaplastic lymphoma kinase or epidermal growth factor receptor expression. METHODS: A systematic review was performed, including a systematic search of key databases (e.g. MEDLINE, EMBASE, Web of Knowledge, Cost-effectiveness Registry) limited to the period from 01 January 2001 to 26 July 2019. Two reviewers independently screened, extracted data and quality appraised identified studies. The reporting quality of the studies was assessed by using the Consolidated Health Economic Evaluation Reporting Standards and the Philips' checklists. RESULTS: Thirty-one published records met the inclusion criteria, which corresponded to 30 individual cost-effectiveness analyses. Analytical approaches included partitioned survival models (n = 14), state-transition models (n = 7) and retrospective analyses of new or published data (n = 8). Model structure was generally consistent, with pre-progression, post-progression and death health states used most commonly. Other characteristics varied more widely, including the perspective of analysis, discounting, time horizon, usually to align with the country that the analysis was set in. CONCLUSIONS: There are a wide range of approaches in the modelling of treatments for advanced NSCLC; however, the model structures are consistent. There is variation in the exploration of sensitivity analyses, with considerable uncertainty remaining in most evaluations. Improved reporting is necessary to ensure transparency in future analyses.

Derivation and internal validation of the screening to enhance prehospital identification of sepsis (SEPSIS) score in adults on arrival at the emergency department.

BACKGROUND: Prehospital recognition of sepsis may inform case management by ambulance clinicians, as well as inform transport decisions. The objective of this study was to develop a prehospital sepsis screening tool for use by ambulance clinicians. METHODS: We derived and validated a sepsis screening tool, utilising univariable logistic regression models to identify predictors for inclusion, and multivariable logistic regression to generate the SEPSIS score. We utilised a retrospective cohort of adult patients transported by ambulance (n = 38483) to hospital between 01 July 2013 and 30 June 2014. Records were linked using LinkPlus® software. Successful linkage was achieved in 33289 cases (86%). Eligible patients included adult, non-trauma, non-mental health, non-cardiac arrest cases. Of 33289 linked cases, 22945 cases were eligible. Eligible cases were divided into derivation (n = 16063, 70%) and validation (n = 6882, 30%) cohorts. The primary outcome measure was high risk of severe illness or death from sepsis, as defined by the National Institute for Health and Care Excellence Sepsis guideline. RESULTS: 'High risk of severe illness or death from sepsis' was present in 3.7% of derivation (n = 593) and validation (n = 254) cohorts. The SEPSIS score comprises the following variables: age, respiratory rate, peripheral oxygen saturations, heart rate, systolic blood pressure, temperature and level of consciousness (p < 0.001 for all variables). Area under the curve was 0.87 (95%CI 0.85-0.88) for the derivation cohort, and 0.86 (95%CI 0.84-0.88) for the validation cohort. In an undifferentiated adult medical population, for a SEPSIS score ≥ 5, sensitivity was 0.37 (0.31-0.44), specificity was 0.96 (0.96-0.97), positive predictive value was 0.27 (0.23-0.32), negative predictive value was 0.97 (0.96-0.97), positive likelihood value was 13.5 (9.7-18.73) and the negative likelihood value was 0.83 (0.78-0.88). CONCLUSION: This is the first screening tool developed to identify NICE high risk of severe illness or death from sepsis. The SEPSIS score is significantly associated with high risk of severe illness or death from sepsis on arrival at the Emergency Department. It may assist ambulance clinicians to identify those patients with sepsis in need of antibiotic therapy. However, it requires external validation, in clinical practice by ambulance clinicians, in an independent population.

Ankle injury rehabilitation (AIR): a feasibility randomised controlled trial comparing functional bracing to plaster cast in the treatment of adult ankle fractures.

BACKGROUND: Approximately 9% of a trauma surgeon's workload in the UK is managing ankle fractures. Following an ankle fracture immobilisation with a plaster cast or removable orthotic is usual. The aim of this research was to assess the feasibility of a large multi-centre randomised controlled trial (RCT) to evaluate the difference between plaster cast and a removable orthotic for the management of adults with an ankle fracture. METHODS: A feasibility randomised controlled trial was undertaken in a UK trauma hospital in adults with an ankle fracture for which the treating clinician would consider plaster cast a reasonable management option. Exclusions included open or pathological fracture, unable to adhere to trial procedures, had other lower limb injury or required close contact casting. Participants were randomised using an independent telephone service to receive either plaster cast or removable orthotic. The primary outcome was to determine the recruitment and follow-up rates at 6 weeks, 3 and 6 months to assess the feasibility of a full RCT. RESULTS: Eighty five eligible patients presented during the 10-month recruitment period, 50 consented. Two patients were randomised who did not fulfil the eligibility criteria (protocol deviations), and 1 patient from each group crossed over. Follow-up at each time point was 92% at 6 weeks; 74% at 3 months and 83% at 6 months. CONCLUSIONS: Recruitment and follow-up data demonstrated feasibility of conducting a larger-scale randomised controlled trial. The distributional properties of the patient-reported outcome measures will be used to determine future sample sizes. TRIAL REGISTRATIONS: This study is registered with the ISRCTN (ISRCTN17809322), assigned 5 November 2015 and approved by the NRES Committee (The Black Country, 15/WM/0340), protocol version 2.0 (17 November 2015). It is co-sponsored by the University Hospitals Coventry and Warwickshire NHS Trust and University of Warwick and funded by the NIHR Research for Patient Benefit (PB-PG-0614-34,009). The trial sponsors have no direct involvement in any aspects of study design, conduct or decision to submit the report for publication.

How Do Pharmaceutical Companies Model Survival of Cancer Patients? A Review of NICE Single Technology Appraisals in 2017.

OBJECTIVES: Before an intervention is publicly funded within the United Kingdom, the cost-effectiveness is assessed by the National Institute of Health and Care Excellence (NICE). The efficacy of an intervention across the patients' lifetime is often influential of the cost-effectiveness analyses, but is associated with large uncertainties. We reviewed committee documents containing company submissions and evidence review group (ERG) reports to establish the methods used when extrapolating survival data, whether these adhered to NICE Technical Support Document (TSD) 14, and how uncertainty was addressed. METHODS: A systematic search was completed on the NHS Evidence Search webpage limited to single technology appraisals of cancer interventions published in 2017, with information obtained from the NICE Web site. RESULTS: Twenty-eight appraisals were identified, covering twenty-two interventions across eighteen diseases. Every economic model used parametric curves to model survival. All submissions used goodness-of-fit statistics and plausibility of extrapolations when selecting a parametric curve. Twenty-five submissions considered alternate parametric curves in scenario analyses. Six submissions reported including the parameters of the survival curves in the probabilistic sensitivity analysis. ERGs agreed with the company's choice of parametric curve in nine appraisals, and agreed with all major survival-related assumptions in two appraisals. CONCLUSIONS: TSD 14 on survival extrapolation was followed in all appraisals. Despite this, the choice of parametric curve remains subjective. Recent developments in Bayesian approaches to extrapolation are not implemented. More precise guidance on the selection of curves and modelling of uncertainty may reduce subjectivity, accelerating the appraisal process.

Pembrolizumab for Previously Treated Advanced or Metastatic Urothelial Cancer: An Evidence Review Group Perspective of a NICE Single Technology Appraisal.

Pembrolizumab is an intravenously administered monoclonal antibody licensed for locally advanced or metastatic urothelial carcinoma after platinum-containing chemotherapy. This summary presents the perspective of Warwick Evidence, the Evidence Review Group (ERG) appointed by the National Institute of Health and Care Excellence (NICE) for the single technology appraisal of pembrolizumab for this indication. Pembrolizumab is manufactured by Merck, Sharp and Dohme (MSD). The major source of clinical effectiveness was the KEYNOTE-045 trial, where 542 patients received either pembrolizumab or clinician's choice of docetaxel, paclitaxel or vinflunine as a second-line treatment. No indirect treatment comparison was performed. The clinical effectiveness was assessed using hazard ratios for overall survival (OS) and progression-free survival (PFS) of the intention-to-treat (ITT) population, together with the subpopulations positive for programmed cell death 1 ligand 1 (PD-L1) expression (combined positive score [CPS] ≥ 1%) and strongly positive for PD-L1 expression (CPS ≥ 10%). In the ITT population, OS improved with pembrolizumab (HR 0.73, 95% CI 0.59-0.91) while PFS outcomes showed no difference (HR 0.98, 95% CI 0.81-1.19). Pembrolizumab demonstrated a better safety profile than its combined comparators, with fewer patients experiencing adverse events (60.9 vs 90.2%). Similar results were observed in populations expressing PD-L1. MSD estimated the cost effectiveness of pembrolizumab using a de novo partitioned survival model. The model had three health states: pre-progression, post-progression and death, where OS and PFS estimates excluded patients who received vinflunine. The largest uncertainty was over the selection of the parametric models used to extrapolate OS and PFS and the time point for when to begin their extrapolation. The company preferences for extrapolation were not well supported and the ERG disagreed with their selection for OS. Utility values were also contentious, with the company preferring to use pooled time-to-death-based utilities pooled across treatment arms, whilst the ERG preferred pooled progression-based utilities. The company preferred to use data from patients receiving vinflunine when calculating the utility values, which the ERG disagreed with as this is not recommended treatment within the UK. The company assumed a lifetime treatment effect for their model; however, the lack of evidence made it difficult to confidently provide a realistic estimate of treatment effect duration. Various durations were explored (3, 5 and 10 years). The first appraisal committee meeting concluded that pembrolizumab was not cost effective, largely due to uncertainty in the OS and PFS extrapolations. The company's second submission included an additional 4 months follow-up to survival data. The company in this new submission maintained their original assumptions in their base-case analysis, changing only the choice of parametric curve for PFS. This change resulted in the OS and PFS curves intersecting at 6 years in the pembrolizumab arm, at which point PFS identically followed OS. This resulted in no patients in the post-progression health state beyond this time point, and therefore, the majority of pembrolizumab's benefit came from pre-progression survival. Given the unclear PFS benefit, the ERG found this implausible and maintained their original base-case model assumptions. Considerable uncertainty remained over the specification of the extrapolations and the duration of treatment effect. Based on a new-value proposition submitted by the company, the appraisal committee concluded that pembrolizumab had plausible potential to be cost effective. Pembrolizumab was referred for funding through the Cancer Drugs Fund, so that further data could be collected with the aim of diminishing the outstanding uncertainties pertaining to its clinical effectiveness.

Neurotrophins, cytokines, oxidative stress mediators and mood state in bipolar disorder: systematic review and meta-analyses.

BACKGROUND: A reliable biomarker signature for bipolar disorder sensitive to illness phase would be of considerable clinical benefit. Among circulating blood-derived markers there has been a significant amount of research into inflammatory markers, neurotrophins and oxidative stress markers.AimsTo synthesise and interpret existing evidence of inflammatory markers, neurotrophins and oxidative stress markers in bipolar disorder focusing on the mood phase of illness. METHOD: Following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) guidelines, a systematic review was conducted for studies investigating peripheral biomarkers in bipolar disorder compared with healthy controls. We searched Medline, Embase, PsycINFO, SciELO and Web of Science, and separated studies by bipolar mood phase (mania, depression and euthymia). Extracted data on each biomarker in separate mood phases were synthesised using random-effects model meta-analyses. RESULTS: In total, 53 studies were included, comprising 2467 cases and 2360 controls. Fourteen biomarkers were identified from meta-analyses of three or more studies. No biomarker differentiated mood phase in bipolar disorder individually. Biomarker meta-analyses suggest a combination of high-sensitivity C-reactive protein/interleukin-6, brain derived neurotrophic factor/tumour necrosis factor (TNF)-α and soluble TNF-α receptor 1 can differentiate specific mood phase in bipolar disorder. Several other biomarkers of interest were identified. CONCLUSIONS: Combining biomarker results could differentiate individuals with bipolar disorder from healthy controls and indicate a specific mood-phase signature. Future research should seek to test these combinations of biomarkers in longitudinal studies.Declaration of interestNone.

Combination of Everolimus with Sorafenib for Solid Renal Tumors in Tsc2+/- Mice Is Superior to Everolimus Alone.

Tuberous sclerosis (TSC) is an inherited tumor syndrome caused by mutations in TSC1 or TSC2 that lead to aberrant activation of mTOR and development of tumors in multiple organs including the kidneys. The mTOR inhibitors rapamycin and everolimus (rapalogs) have demonstrated clinical efficacy in treating TSC-associated tumors including renal angiomyolipomas. However, tumor responses are usually only partial, and regrowth occurs after drug withdrawal. TSC-associated tumors are highly vascular, and TSC patients with renal angiomyolipomas have elevated levels of circulating vascular endothelial growth factor (VEGF) A and VEGFD. Sorafenib inhibits multiple kinases including VEGF receptors and has been used to treat metastatic epithelioid angiomyolipoma in one case, but formal trials have not been undertaken. In this study, we investigated tumor angiogenesis and the therapeutic efficacy of everolimus in combination with sorafenib for renal tumors in Tsc2+/- mice. We found that these tumors exhibited remarkably variable angiogenesis despite consistent aberrant activation of mTOR and increased expression of HIF1α and VEGFA. Treatment of 11-month-old Tsc2+/- mice for 2 months with a combination of everolimus and sorafenib significantly reduced the number and size of solid renal tumors, whereas everolimus or sorafenib alone did not. These results suggest that inhibition of mTOR and multiple kinases including VEGF receptors using combination therapy could hold promise for the treatment of TSC-associated tumors that have responded inadequately to a rapalog alone.

Wind turbine wake visualization and characteristics analysis by Doppler lidar.

Wind power generation is growing fast as one of the most promising renewable energy sources that can serve as an alternative to fossil fuel-generated electricity. When the wind turbine generator (WTG) extracts power from the wind, the wake evolves and leads to a considerable reduction in the efficiency of the actual power generation. Furthermore, the wake effect can lead to the increase of turbulence induced fatigue loads that reduce the life time of WTGs. In this work, a pulsed coherent Doppler lidar (PCDL) has been developed and deployed to visualize wind turbine wakes and to characterize the geometry and dynamics of wakes. As compared with the commercial off-the-shelf coherent lidars, the PCDL in this work has higher updating rate of 4 Hz and variable physical spatial resolution from 15 to 60 m, which improves its capability to observation the instantaneous turbulent wind field. The wind speed estimation method from the arc scan technique was evaluated in comparison with wind mast measurements. Field experiments were performed to study the turbulent wind field in the vicinity of operating WTGs in the onshore and offshore wind parks from 2013 to 2015. Techniques based on a single and a dual Doppler lidar were employed for elucidating main features of turbine wakes, including wind velocity deficit, wake dimension, velocity profile, 2D wind vector with resolution of 10 m, turbulence dissipation rate and turbulence intensity under different conditions of surface roughness. The paper shows that the PCDL is a practical tool for wind energy research and will provide a significant basis for wind farm site selection, design and optimization.