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Introduction: Hypothermia is a well‐recognised finding in trauma patients, which can occur even in warmer climates. It is an independent predictor of increased morbidity and mortality. It is associated with pre‐hospital intubation, although the reasons for this are likely to be multifactorial. Core temperature drop after induction of anaesthesia is a well‐known phenomenon in the context of elective surgery, and the mechanisms of this are well established. Methods: We conducted a prospective observational study to examine the behaviour of core temperature in patients undergoing pre‐hospital anaesthesia for traumatic injuries. Results: Between 2017 and 2021 data were collected on 48 patients. The data from 40 of these were included in the final analysis. Discussion: Our data do not show a decrease in the core temperatures of patients who receive pre‐hospital anaesthesia, unlike patients who are anaesthetised without pre‐warming, in operating theatres. The lack of a change could relate to patient, anaesthetic or environmental factors. [ABSTRACT FROM AUTHOR]

Parametric models are used to estimate the lifetime benefit of an intervention beyond the range of trial follow-up. Recent recommendations have suggested more flexible survival approaches and the use of external data when extrapolating. Both of these can be realized by using flexible parametric relative survival modeling. The overall aim of this article is to introduce and contrast various approaches for applying constraints on the long-term disease-related (excess) mortality including cure models and evaluate the consequent implications for extrapolation. We describe flexible parametric relative survival modeling approaches. We then introduce various options for constraining the long-term excess mortality and compare the performance of each method in simulated data. These methods include fitting a standard flexible parametric relative survival model, enforcing statistical cure, and forcing the long-term excess mortality to converge to a constant. We simulate various scenarios, including where statistical cure is reasonable and where the long-term excess mortality persists. The compared approaches showed similar survival fits within the follow-up period. However, when extrapolating the all-cause survival beyond trial follow-up, there is variation depending on the assumption made about the long-term excess mortality. Altering the time point from which the excess mortality is constrained enables further flexibility. The various constraints can lead to applying explicit assumptions when extrapolating, which could lead to more plausible survival extrapolations. The inclusion of general population mortality directly into the model-building process, which is possible for all considered approaches, should be adopted more widely in survival extrapolation in health technology assessment. • Extrapolation of all-cause survival from randomized controlled trial data is often required in the context of economic evaluation of novel interventions. External information can be used to guide long-term survival trends when extrapolating all-cause survival. This article investigates relative survival modeling approaches to incorporate general population mortality rates into long-term extrapolation. In doing so, we expand on existing approaches by clearly detailing how to apply various constraints on the long-term disease-specific mortality in this modeling framework. • Flexible parametric survival models can apply various extrapolation approaches in the framework. We compare models by imposing constraints on parameter estimation and applying those constraints at specific points of follow-up, including beyond the range of the data. We compare these methods in scenarios where cure is simulated to be reasonable and in cases where it is not. The result shows that different assumptions on the excess hazards can result in different extrapolations of all-cause survival. The various constraints can lead to more plausible survival extrapolation depending on the disease or treatment characteristics. • The approaches for all-cause survival extrapolation outlined offer a suite of possibilities to clearly describe an approach to extrapolation that automatically incorporates general population mortality rates while making explicit choices on how long disease-related mortality will affect the cohort. The choice of approach can be dictated by the clinical context in the decision-making process. [ABSTRACT FROM AUTHOR]

A long-term, constant, protective treatment effect is a strong assumption when extrapolating survival beyond clinical trial follow-up; hence, sensitivity to treatment effect waning is commonly assessed for economic evaluations. Forcing a hazard ratio (HR) to 1 does not necessarily estimate loss of individual-level treatment effect accurately because of HR selection bias. A simulation study was designed to explore the behavior of marginal HRs under a waning conditional (individual-level) treatment effect and demonstrate bias in forcing a marginal HR to 1 when the estimand is "survival difference with individual-level waning". Data were simulated under 4 parameter combinations (varying prognostic strength of heterogeneity and treatment effect). Time-varying marginal HRs were estimated in scenarios where the true conditional HR attenuated to 1. Restricted mean survival time differences, estimated having constrained the marginal HR to 1, were compared with true values to assess bias induced by marginal constraints. Under loss of conditional treatment effect, the marginal HR took a value >1 because of covariate imbalances. Constraining this value to 1 lead to restricted mean survival time difference bias of up to 0.8 years (57% increase). Inflation of effect size estimates also increased with the magnitude of initial protective treatment effect. Important differences exist between survival extrapolations assuming marginal versus conditional treatment effect waning. When a marginal HR is constrained to 1 to assess efficacy under individual-level treatment effect waning, the survival benefits associated with the new treatment will be overestimated, and incremental cost-effectiveness ratios will be underestimated. • Noncollapsibility of odds/hazard ratios (HRs) has been widely discussed in causal inference literature. Discussion of the impact in randomized controlled trial extrapolations is scarcer. This article explores the importance of understanding the difference between marginal and conditional HRs, specifically in the context of an economic evaluation/health technology assessment using randomized controlled trial data, carrying out an extrapolation assuming treatment effect waning. • If the individual-level treatment effect truly wanes after a period of protective treatment effect, a marginal (eg, unadjusted) HR > 1 from this point is to be expected. This reflects covariate imbalances between arms induced by the treatment's effect and demonstrates the difficulty in interpreting a HR that is dependent on both participant-level hazards and trial-level covariate distributions (and how these shift over time). • Forcing a marginal HR to 1 from the end of trial follow-up is not equivalent to individual-level treatment effect loss (inducing bias in restricted mean survival time difference of up to 0.8 years, increasing with treatment strength and prognostic power of marginal covariates). To most accurately assess sensitivity to treatment effect waning, constraints on the HR should be applied to an estimate that is conditional on all feasible prognostic factors. [ABSTRACT FROM AUTHOR]

Background: The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making. Methods and findings: This nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only—as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI. Conclusions: In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study. Using hospital records of 34 million adults admitted to hospitals in England, Marlous Hall and team examine the long-term risk of major health outcomes following myocardial infarction. Author summary: Why was this study done?: Myocardial infarction (MI; heart attack) can have major long-term impact on individuals and result in a wide range of further health conditions. Existing studies have focussed on determining the short-term risk of a second heart attack, stroke, or major bleeding, but research describing the long-term risk of major health outcomes for specific age, sex, and deprivation groups was lacking. Nationally representative and robust information of a wide range of long-term health outcomes following a heart attack is critical for the development of treatment recommendations, which take account of an individuals' specific risk. What did the researchers do and find?: From the population of 56 million adults in England, we analysed hospital records for 34 million adults admitted to hospital (constituting 145 million admission records) to investigate the long-term health outcomes following a heart attack compared with individuals without a heart attack. Of 433,361 individuals with a heart attack, up to a third developed heart failure or renal failure, 7% had further heart attacks, and 38% died within the 9-year study period. Heart failure, atrial fibrillation, stroke, peripheral arterial disease, severe bleeding, renal failure, diabetes, and depression occurred more frequently for people who had a heart attack compared with those who did not, but the risk of cancer was lower overall and the risk of dementia did not differ overall. What do these findings mean?: Efforts should be made to prevent or limit the development of long-term health outcomes that follow a heart attack—the likelihood of which differ depending on the age, sex, and deprivation of an individual. These findings are based on the full population of adults admitted to hospital in England, address limitations of previous studies, and can be used to inform preventative strategies tailored to specific individuals surviving a heart attack. The study was limited to hospitalisation data only—therefore, some diagnoses made outside of hospital may have been missed. [ABSTRACT FROM AUTHOR]

When analyzing cohort data, we rarely consider potential simultaneous effects of multiple time-scales on estimated survival proportions and hazard ratios. Instead, often, only one time-scale, such as time-on-study or attained age, is taken as the main time-scale and the other time-scale(s) included in the model as time-fixed covariate(s). In this simulation study, we investigate the potential bias in estimated survival proportions and hazard ratios by not modeling multiple time-scales. We simulate data with two time-scales, under various scenarios, and a binary covariate as the exposure of interest. Flexible parametric models with one time-scale were fitted to the simulated data, including the exposure variable of interest and the other time-scale as a fixed covariate. For many of our simulated scenarios, the models with one time-scale showed small bias for the survival proportions. Bias in the log hazard ratios (for the covariate of interest) was observed when there were non-proportional hazards on the second time-scale. If one is modeling only one time-scale it is advisable to include non-proportional hazards and interactions with the time-scale. Exploration of approaches to modeling multiple time-scales is outside the scope of this study. [ABSTRACT FROM AUTHOR]

Simple Summary: It is difficult to predict which patients with non-metastatic clear cell renal cell carcinoma (ccRCC) will develop metastatic disease after nephrectomy. Recent studies suggest that immune cell infiltration within ccRCC tumors may impact tumor progression. This study assessed the number and type of immune cells in non-metastatic ccRCC tumors that were surgically removed and their ability to predict which patients developed metastatic disease. We found that higher levels of a specific immune cell (CD8+ T cells) were linked to a lower risk of progressive disease. Patients who did progress had more exhausted CD8+ T cells in the tumor microenvironment. Additionally, our study design accounted for tumor heterogeneity by sampling tumors in multiple locations and showed differences in the spatial distribution of CD8+ T cells in tumors that progressed to metastatic disease. With further validation, this study shows that CD8+ T cell infiltration within ccRCC tumors could be used as a prognostic biomarker to predict progression to metastatic disease. Predicting which patients will progress to metastatic disease after surgery for non-metastatic clear cell renal cell carcinoma (ccRCC) is difficult; however, recent data suggest that tumor immune cell infiltration could be used as a biomarker. We evaluated the quantity and type of immune cells infiltrating ccRCC tumors for associations with metastatic progression following attempted curative surgery. We quantified immune cell densities in the tumor microenvironment and validated our findings in two independent patient cohorts with multi-region sampling to investigate the impact of heterogeneity on prognostic accuracy. For non-metastatic ccRCC, increased CD8+ T cell infiltration was associated with a reduced likelihood of progression to metastatic disease. Interestingly, patients who progressed to metastatic disease also had increased percentages of exhausted CD8+ T cells. Finally, we evaluated the spatial heterogeneity of the immune infiltration and demonstrated that patients without metastatic progression had CD8+ T cells in closer proximity to ccRCC cells. These data strengthen the evidence for CD8+ T cell infiltration as a prognostic biomarker in non-metastatic ccRCC and demonstrate that multi-region sampling may be necessary to fully characterize immune infiltration within heterogeneous tumors. Tumor CD8+ T cell infiltration should be investigated as a biomarker in adjuvant systemic therapy clinical trials for high-risk non-metastatic RCC. [ABSTRACT FROM AUTHOR]

• Lung cancer survival improved in all Nordic countries 1990–2017. • Similar patterns of improval across age and in both women and men. • Previously observed survival disadvantage in Denmark no longer present. • Improvements seen in adenocarcinoma, squamous cell and small-cell carcinoma. • Three of four patients still die from lung cancer within five years of diagnosis. The aim of this study was to evaluate if the previously reported improvements in lung cancer survival were consistent across age at diagnosis and by lung cancer subtypes. Data on lung cancers diagnosed between 1990 and 2016 in Denmark, Finland, Iceland, Norway and Sweden were obtained from the NORDCAN database. Flexible parametric models were used to estimate age-standardized and age-specific relative survival by sex, as well as reference-adjusted crude probabilities of death and life-years lost. Age-standardised survival was also estimated by the three major subtypes; adenocarcincoma, squamous cell and small-cell carcinoma. Both 1- and 5-year relative survival improved continuously in all countries. The pattern of improvement was similar across age groups and by subtype. The largest improvements in survival were seen in Denmark, while improvements were comparatively smaller in Finland. In the most recent period, age-standardised estimates of 5-year relative survival ranged from 13% to 26% and the 5-year crude probability of death due to lung cancer ranged from 73% to 85%. Across all Nordic countries, survival decreased with age, and was lower in men and for small-cell carcinoma. Lung cancer survival has improved substantially since 1990, in both women and men and across age. The improvements were seen in all major subtypes. However, lung cancer survival remains poor, with three out of four patients dying from their lung cancer within five years of diagnosis. [ABSTRACT FROM AUTHOR]

The survival in patients diagnosed with cutaneous malignant melanoma (CMM) has improved in the Nordic countries in the last decades. It is of interest to know if these improvements are observed in all ages and for both women and men. Patients diagnosed with CMM in the Nordic countries in 1990–2016 were identified in the NORDCAN database. Flexible parametric relative survival models were fitted, except for Iceland where a non-parametric Pohar-Perme approach was used. A range of survival metrics were estimated by sex, both age-standardised and age-specific. The 5-year relative survival improved in all countries, in both women and men and across age. While the improvement was more pronounced in men, women still had a higher survival at the end of the study period. The survival was generally high, with age-standardised estimates of 5-year relative survival towards the end of the study period ranging from 85% in Icelandic men to 95% in Danish women. The age-standardised and reference-adjusted 5-year crude probability of death due to CMM ranged from 5% in Danish and Swedish women to 13% in Icelandic men. Although survival following CMM was relatively high in the Nordic countries in 1990, continued improvements in survival were observed throughout the study period in both women and men and across age. • Survival following cutaneous malignant melanoma is high in the Nordic countries. • The survival has continuously improved between 1990 and 2016. • Survival improvements were observed across age and in both men and women. [ABSTRACT FROM AUTHOR]

The goal of our study is to assess the environmental impact of the installation and use of the Giant Radio Array for Neutrino Detection (GRAND) prototype detection units, based on the life cycle assessment (LCA) methodology, and to propose recommendations that contribute to reduce the environmental impacts of the project at later stages. The functional unit, namely the quantified description of the studied system and of the performance requirements it fulfils, is to detect radio signals autonomously during 20 years, with 300 detection units deployed over 200 km 2 in the Gansu province in China (corresponding to the prototype GRANDProto300). We consider four main phases: the extraction of the materials and the production of the detection units (upstream phases), the use and the end-of-life phases (downstream phases), with transportation between each step. An inventory analysis is performed for the seven components of each detection unit, based on transparent assumptions. Most of the inventory data are taken from the Idemat2021 database (Industrial Design & Engineering Materials). Our results show that the components with the highest environmental impact are the antenna structure and the battery. The most pregnant indicators are 'resource use', mineral and metals'; 'resource use, fossils'; 'ionizing radiation, human health'; 'climate change'; and 'acidification'. Therefore, the actions that we recommend in the first place aim at reducing the impact of these components. They include limiting the mass of the raw material used in the antenna, changing the alloy of the antenna, considering another type of battery with an extended useful life, and the use of recycled materials for construction. As a pioneering study applying the LCA methodology to a large-scale physics experiment, this work can serve as a basis for future assessments by other collaborations. [ABSTRACT FROM AUTHOR]