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.

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.

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.

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.

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.