Background Sepsis and acute respiratory distress syndrome are two heterogeneous acute illnesses with high risk of death and for which there are many ‘statistically negative’ randomised controlled trials. We hypothesised that negative randomised controlled trials occur because of between-participant differences in response to treatment, illness manifestation (phenotype) and risk of outcomes (heterogeneity). Objectives To assess (1) heterogeneity of treatment effect, which tests whether or not treatment effect varies with a patient’s pre-randomisation risk of outcome; and (2) whether or not subphenotypes explain the treatment response differences in sepsis and acute respiratory distress syndrome demonstrated in randomised controlled trials. Study population We performed secondary analysis of two randomised controlled trials in patients with sepsis [i.e. the Vasopressin vs Noradrenaline as Initial Therapy in Septic Shock (VANISH) trial and the Levosimendan for the Prevention of Acute oRgan Dysfunction in Sepsis (LeoPARDS) trial] and one acute respiratory distress syndrome multicentre randomised controlled trial [i.e. the Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in Acute lung injury to Reduce Pulmonary dysfunction (HARP-2) trial], conducted in the UK. The VANISH trial is a 2 × 2 factorial randomised controlled trial of vasopressin (Pressyn AR®; Ferring Pharmaceuticals, Saint-Prex, Switzerland) and hydrocortisone sodium phosphate (hereafter referred to as hydrocortisone) (EfcortesolTM; Amdipharm plc, St Helier, Jersey) compared with placebo. The LeoPARDS trial is a two-arm-parallel-group randomised controlled trial of levosimendan (Simdax®; Orion Pharma, Espoo, Finland) compared with placebo. The HARP-2 trial is a parallel-group randomised controlled trial of simvastatin compared with placebo. Methods To test for heterogeneity of the effect on 28-day mortality of vasopressin, hydrocortisone and levosimendan in patients with sepsis and of simvastatin in patients with acute respiratory distress syndrome. We used the total Acute Physiology And Chronic Health Evaluation II (APACHE II) score as the baseline risk measurement, comparing treatment effects in patients with baseline APACHE II scores above (high) and below (low) the median using regression models with an interaction between treatment and baseline risk. To identify subphenotypes, we performed latent class analysis using only baseline clinical and biomarker data, and compared clinical outcomes across subphenotypes and treatment groups. Results The odds of death in the highest APACHE II quartile compared with the lowest quartile ranged from 4.9 to 7.4, across the three trials. We did not observe heterogeneity of treatment effect for vasopressin, hydrocortisone and levosimendan. In the HARP-2 trial, simvastatin reduced mortality in the low-APACHE II group and increased mortality in the high-APACHE II group. In the VANISH trial, a two-subphenotype model provided the best fit for the data. Subphenotype 2 individuals had more inflammation and shorter survival. There were no treatment effect differences between the two subphenotypes. In the LeoPARDS trial, a three-subphenotype model provided the best fit for the data. Subphenotype 3 individuals had the greatest inflammation and lowest survival. There were no treatment effect differences between the three subphenotypes, although survival was lowest in the levosimendan group for all subphenotypes. In the HARP-2 trial, a two-subphenotype model provided the best fit for the data. The inflammatory subphenotype was associated with fewer ventilator-free days and higher 28-day mortality. Limitations The lack of heterogeneity of treatment effect and any treatment effect differences between sepsis subphenotypes may be secondary to the lack of statistical power to detect such effects, if they truly exist. Conclusions We highlight lack of heterogeneity of treatment effect in all three trial populations. We report three subphenotypes in sepsis and two subphenotypes in acute respiratory distress syndrome, with an inflammatory phenotype with greater risk of death as a consistent finding in both sepsis and acute respiratory distress syndrome. Future work Our analysis highlights the need to identify key discriminant markers to characterise subphenotypes in sepsis and acute respiratory distress syndrome with an observational cohort study. Funding This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and National Institute for Health Research (NIHR) partnership. This will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 10. See the NIHR Journals Library website for further project information.

QuestionScreening with low dose computed tomography (LDCT) reduces lung-cancer mortality; however, the most effective strategy for optimising participation is unknown. Here we present data from the Yorkshire Lung Screening Trial, including response to invitation, screening eligibility and uptake of community-based LDCT screening.MethodsIndividuals aged 55 to 80, identified from primary care records as having ever smoked, were randomised prior to consent to invitation to telephone lung cancer risk assessment or usual care. The invitation strategy included General Practitioner endorsement, pre-invitation and two reminder invitations. After telephone triage, those at higher risk were invited to a Lung Health Check (LHC) with immediate access to a mobile CT scanner.ResultsOf 44 943 individuals invited, 50.8% (n=22 815) responded and underwent telephone-based risk assessment (16.7% and 7.3% following first and second reminders respectively). A lower response rate was associated with current smoking status (adjOR 0.44, 95%CI 0.42–0.46) and socio-economic deprivation (adjOR 0.58, 95% CI 0.54–0.62 most versus least deprived quintile). Of those responding, 34.4% (n=7853) were potentially eligible for screening and offered a LHC, of whom 86.8% (n=6819) attended. Lower uptake was associated with current smoking status (adjOR 0.73, 95%CI 0.62–0.87) and socio-economic deprivation (adjOR 0.78, 95% CI 0.62–0.98). In total 6,650 individuals had a baseline LDCT scan, representing 99.7% of eligible LHC attendees.ConclusionTelephone risk assessment followed by a community-based LHC is an effective strategy for lung cancer screening implementation. However, lower participation associated with current smoking status and socio-economic deprivation underlines the importance of research to ensure equitable access to screening.

IntroductionIncorporating spirometry into low-dose CT (LDCT) screening for lung cancer may help identify people with undiagnosed chronic obstructive pulmonary disease (COPD), although the downstream impacts are not well described.MethodsParticipants attending a Lung Health Check (LHC) as part of the Yorkshire Lung Screening Trial were offered spirometry alongside LDCT screening. Results were communicated to the general practitioner (GP), and those with unexplained symptomatic airflow obstruction (AO) fulfilling agreed criteria were referred to the Leeds Community Respiratory Team (CRT) for assessment and treatment. Primary care records were reviewed to determine changes to diagnostic coding and pharmacotherapy.ResultsOf 2391 LHC participants undergoing prebronchodilator spirometry, 201 (8.4%) fulfilled the CRT referral criteria of which 151 were invited for further assessment. Ninety seven participants were subsequently reviewed by the CRT, 46 declined assessment and 8 had already been seen by their GP at the time of CRT contact. Overall 70 participants had postbronchodilator spirometry checked, of whom 20 (29%) did not have AO. Considering the whole cohort referred to the CRT (but excluding those without AO postbronchodilation), 59 had a new GP COPD code, 56 commenced new pharmacotherapy and 5 were underwent pulmonary rehabilitation (comprising 2.5%, 2.3% and 0.2% of the 2391 participants undergoing LHC spirometry).ConclusionsDelivering spirometry alongside lung cancer screening may facilitate earlier diagnosis of COPD. However, this study highlights the importance of confirming AO by postbronchodilator spirometry prior to diagnosing and treating patients with COPD and illustrates some downstream challenges in acting on spirometry collected during an LHC.

Rationale: Heterogeneity of sepsis limits discovery and targeting of treatments. Clustering approaches in critical illness have identified patient groups who may respond differently to therapies. These include in acute respiratory distress syndrome (ARDS) two inflammatory sub-phenotypes, using latent class analysis (LCA), and in sepsis two Sepsis Response Signatures (SRS), based on transcriptome profiling. It is unknown if inflammatory sub-phenotypes such as those identified in ARDS are present in sepsis and how sub-phenotypes defined with different techniques compare. Objectives: To identify inflammatory sub-phenotypes in sepsis using LCA and assess if these show differential treatment responses. These sub-phenotypes were compared to hierarchical clusters based on inflammatory mediators and to SRS sub-phenotypes. Methods: LCA was applied to clinical and biomarker data from two septic shock randomized trials. VANISH compared norepinephrine to vasopressin and hydrocortisone to placebo and LeoPARDS compared levosimendan to placebo. Hierarchical cluster analysis (HCA) was applied to 65, 21 and 11 inflammatory mediators measured in patients from the GAinS (n=124), VANISH (n=155) and LeoPARDS (n=484) studies. Measurements and Main Results: LCA and HCA identified a sub-phenotype of patients with high cytokine levels and worse organ dysfunction and survival, with no interaction between LCA classes and trial treatment responses. Comparison of inflammatory and transcriptomic sub-phenotypes revealed some similarities but without sufficient overlap that they are interchangeable. Conclusions: A sub-phenotype with high levels of inflammation and increased disease severity is consistently identifiable in sepsis, with similarities to that described in ARDS. There was limited overlap with the transcriptomic sub-phenotypes.

IntroductionChronic Obstructive Pulmonary Disease (COPD) is underdiagnosed, and measurement of spirometry alongside low-dose computed tomography (LDCT) screening for lung cancer is one strategy to increase earlier diagnosis of this disease.MethodsEver-smokers at high risk of lung cancer were invited to the Yorkshire Lung Screening Trial for a Lung Health Check (LHC) comprising LDCT screening, pre-bronchodilator spirometry and smoking cessation service. In this cross-sectional study we present data on participant demographics, respiratory symptoms, lung function, emphysema on imaging and both self-reported and primary care diagnoses of COPD. Multivariable logistic regression analysis identified factors associated with possible underdiagnosis and misdiagnosis of COPD in this population, with airflow obstruction (AO) defined as FEV1/FVC ratio <0.70.ResultsOf 3,920 LHC attendees undergoing spirometry, 17% had undiagnosed AO with respiratory symptoms, representing potentially undiagnosed COPD. Compared to those with a primary care COPD code, this population had milder symptoms, better lung function, and were more likely to be current smokers (p≤0.001 for all comparisons). Of 836 attendees with a primary care COPD code who underwent spirometry, 19% did not have AO, potentially representing misdiagnosed COPD, although symptom burden was high.DiscussionSpirometry offered alongside LDCT screening can potentially identify cases of undiagnosed and misdiagnosed COPD. Future research should assess the downstream impact of these findings to determine if any meaningful changes to treatment and outcomes occurs, and also to assess the impact on co-delivering spirometry on other parameters of LDCT screening performance such as participation and adherence. Additionally, work is needed to better understand the aetiology of respiratory symptoms in those with misdiagnosed COPD, to ensure this highly symptomatic group receive evidence-based interventions.