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Objectives Developing screening and diagnosis methodologies based on novel biomarkers should allow for the detection of the lung cancer (LC) and possibly at an earlier stage and thereby increase the effectiveness of clinical interventions. Here, our primary objective was to evaluate the potential of spontaneous sputum as a source of non-invasive metabolomic biomarkers for LC status. Materials and methods Spontaneous sputum was collected and processed from 34 patients with suspected LC, alongside 33 healthy controls. Of the 34 patients, 23 were subsequently diagnosed with LC (LC + , 16 NSCLC, six SCLC, and one radiological diagnosis), at various stages of disease progression. The 67 samples were analysed using flow infusion electrospray ion mass spectrometry (FIE-MS) and gas-chromatography mass spectrometry (GC–MS). Results Principal component analysis identified negative mode FIE-MS as having the main separating power between samples from healthy and LC. Discriminatory metabolites were identified using ANOVA and Random Forest. Indications of potential diagnostic accuracy involved the use of receiver operating characteristic/area under the curve (ROC/AUC) analyses. This approach identified metabolites changes that were only observed with LC. Metabolites with AUC values of greater than 0.8 which distinguished between LC + /LC − binary classifications where identified and included Ganglioside GM1 which has previously been linked to LC. Conclusion This study indicates that metabolomics based on sputum can yield metabolites that can be used as a diagnostic and/or discriminator tool. These could aid clinical intervention and targeted diagnosis of LC within an ‘at risk’ LC − population group. The use of sputum as a non-invasive source of metabolite biomarkers may aid in the development of an at-risk population screening programme for lung cancer or enhanced clinical diagnostic pathways. [ABSTRACT FROM AUTHOR]

Summary: Background: Pulmonary rehabilitation (PR) is recommended for patients with respiratory disease who feel limited by breathlessness. Poor attendance wastes finite resources, increases waiting times and is probably associated with poorer clinical outcomes. We investigated what factors, identifiable from routine hospital data, predict poor attendance once enrolled in a pulmonary rehabilitation programme (PRP). Methods: Retrospective case note study of 239 patients (60% male) of mean (S.D.) age of 66.6 (8.7) years, mean FEV1 39.6 (14.6)% predicted, who attended a 6 (short) or 18 (long) week, 18 session, outpatient PRP. Attendance data was analysed using linear multiple regression analysis with the log transformed odds ratio of attendance as the dependant variable. Results: Overall median attendance was 16 out of 18 sessions. Being a current smoker (p<0.05), attending a long PRP (p<0.05), more previous hospital admissions (p<0.01), higher Medical Research Council (MRC) dyspnoea score (p<0.01) or enduring a long journey (p<0.001) were independent risk factors for low attendance. Lower body mass index (BMI) and distance from PR centre were of borderline importance (p<0.1) but age, gender, co-morbidity, respiratory diagnosis, FEV1 and St. Georges Respiratory Questionnaire Score at baseline did not predict later attendance (p>0.2). Conclusions: Attendance at PRPs is independently influenced by smoking status, the degree of breathlessness, frequency of hospital admissions, length of the programme and journey time. [Copyright &y& Elsevier]

Stopping smoking has proven benefits in nearly all illnesses but the impact and health economic benefits of stopping smoking after a diagnosis of lung cancer are less well defined. We assessed the cost-effectiveness of smoking cessation (SC) services for patients with newly diagnosed lung cancer against current usual care, where patients are unlikely to receive SC service referral. A health economic model was constructed in Excel. The modelled population comprised of patients with a new diagnosis of non–small cell lung cancer (NSCLC). Data from the LungCast data set (Clinical Trials Identifier NCT01192256) were used to estimate model inputs. A structured search of published literature identified inputs not represented in LungCast, including healthcare resource use and costs. Costs were estimated from a 2020/2021 UK National Health Service and Personal Social Services perspective. The model estimated the incremental quality-adjusted life-year (QALY) gained in patients with newly diagnosed NSCLC receiving targeted SC intervention than those receiving no intervention. Extensive one-way sensitivity analyses explored input and data set uncertainty. In the 5-year base case, the model estimated an incremental cost of £14 904 per QALY gained through SC intervention. Sensitivity analysis estimated an outcome range of between £9935 and £32 246 per QALY gained. The model was most sensitive to the estimates of relative quit rates and expected healthcare resource use. This exploratory analysis indicates that SC intervention for smokers with patients with newly diagnosed NSCLC should be a cost-effective use of UK National Health Service resources. Additional research with focused costing is needed to confirm this positioning. • This study is the first to explore the potential cost-effectiveness of targeted SC intervention in currently smoking patients with newly diagnosed non–small cell lung cancer (a high-risk population proven to benefit from quitting smoking). • The LungCast trial (Clinical Trials Identifier NCT01192256) is a real-world data set exploring the prevalence and survival impact of smoking in a cohort of patients with newly diagnosed lung cancer with > 2400 patients tracked for up to 2 years (or until death). • This exploratory analysis can support commissioners in assessing the potential value of investing in dedicated SC services at the point of diagnosis of non–small cell lung cancer. [ABSTRACT FROM AUTHOR]