BACKGROUND:Liquid biopsy has emerged as a promising, minimally invasive tool to detect cancer biomarkers. Extracellular vesicles (EVs) are secreted into biofluids to transport cargo such as DNA and are a potential biosource for liquid biopsy biomarkers. To determine the optimal use of liquid biopsy for diagnosing mutations in lung adenocarcinoma (LUAD), we compared the KRAS mutation status in DNA isolated from four different plasma fractions, including EVs. METHODS:Plasma was collected from 58 participants diagnosed with LUAD (early-stage (I, II), n = 30; late-stage (IIIB, IV), n = 28) with known KRAS mutation (KRASmt) or wild-type KRAS (KRASwt). Three distinct plasma-derived fractions were prepared by sequential differential ultracentrifugation aiming to isolate EVs (pellets 1, 2, 3 (P1-P3)). These were tested together with the corresponding plasma supernatant (SUP) for the presence of KRAS G12/G13 mutations by droplet digital PCR (ddPCR). RESULTS:In early-stage KRASmt LUAD, mutations were detected by ddPCR in only 1 of 15 processed plasma supernatant samples and 1 of 15 P3 samples, but not in any P1 or P2 pellets. In late-stage KRASmt LUAD, mutations were detected in 13/14 of SUP samples, but only in a small fraction of the pellet preparations: P1 (0/14), P2 (2/14) and P3 (2/13) samples. CONCLUSION:We conclude that ddPCR testing of plasma supernatant achieved high overall agreement with tumour KRASmt status. There was varying abundance of KRAS detected in the plasma fractions.

Small extracellular vesicles (sEVs) offer a promising, non-invasive method for cancer detection. Despite global research efforts, successful translation of sEV-based diagnostics remains limited. In this study, we identify a 4-protein sEV biomarker panel (thrombospondin-1, nidogen-1, pentraxin-3, and versican) based on proteomic profiles obtained from an isogenic cancercell line model. The panel's performance is validated across 22 cancer cell lines and 764 retrospective plasma/serum samples spanning multiple cancer types, yielding robust performance (area under the curve [AUC]: 0.91-1.00). To facilitate clinical application, we develop a multiplex sEV device that integrates nanoshearing-based microfluidics and surface-enhanced Raman scattering (SERS) for simultaneous detection of the 4-protein panel. Using this device on a prospective cohort of 68 patients, we accurately differentiate between benign lung changes and early-stage lung cancer. These findings underscore the potential of sEVs as diagnostic markers for cancer screening. Furthermore, the multiplex microfluidic device's scalability, simplicity, and cost-effectiveness indicate feasibility for large-scale population screening.

BACKGROUND:Mobile health (mHealth) is a novel model of care that may overcome barriers to pulmonary rehabilitation (PR) access. This study determined if mHealth PR was equivalent to centre-based PR (CB-PR) in improving exercise capacity and health status in people with chronic obstructive pulmonary disease (COPD). METHOD:Single-blinded, multicentre, randomised controlled equivalence trial using an intention-to-treat analysis. Participants completed 8 weeks of either mHealth PR, using the mobile PR (m-PR) application and supported by telephone calls, or CB-PR. Co-primary outcomes, measured at baseline and end-intervention, were change in 6 minute walk distance (6MWD) and COPD assessment test (CAT) score, with an equivalence margin of 30 m and 2 points, respectively. RESULTS:90 participants were randomised (mean (SD), m-PR n = 44: age 75 (7) years; forced expiratory volume in one second (FEV1) 58 (15) % predicted; CB-PR n = 46: age 75 (6) years; FEV1 55 (14) % predicted) with 38 m-PR participants and 42 CB-PR participants completing at least one primary outcome. At end-intervention, there was no between-group difference in 6MWD (mean difference (MD) 13 m, 95% CI -6 to 31), indicating equivalence of m-PR to CB-PR. There was a significant between-group difference in CAT score (MD -4.9 points, 95% CI -7.2 to -2.6), with both limits of the CI exceeding the equivalence margin, indicating superiority of m-PR. CONCLUSION:An mHealth PR programme resulted in equivalent improvements in exercise capacity and superior improvements in health status when compared with CB-PR in people with COPD. mHealth PR could be effective as a management option for people with COPD with adequate digital literacy. TRIAL REGISTRATION NUMBER:ACTRN12619001253190.

Background and objective:Recent observational data suggest that cardioselective β-blockers like bisoprolol are safe and beneficial for patients with COPD. However, the acute effects of bisoprolol on lung and cardiovascular function in these patients is unclear, a gap that this study aimed to address. Methods:This was a subanalysis of pre-randomisation screening visit data from the ongoing Preventing Adverse Cardiac Events (PACE) in COPD randomised controlled trial. If all other eligibility criteria were met, participants were orally administered an unblinded 1.25 mg tablet of bisoprolol. Post-bronchodilator spirometry, heart rate and blood pressure were monitored at 0, 30 (cardiovascular parameters only), 60 and 120 min. For this subanalysis, respiratory intolerance was defined as a decrease in forced expiratory volume in 1 s (FEV1) (L) ≥200 mL and ≥12% from the 0-min FEV1 (L) value; and cardiovascular intolerance was defined as systolic blood pressure (SBP) falling below 100 mmHg at 1 or 2 h. Results:Of 359 consented participants, 292 conducted the test-dose procedure. 13 (4.5%) were respiratory intolerant and six (2.1%) were cardiovascular intolerant at 1 or 2 h. No participant was intolerant for both. There was no significant difference in FEV1 (L) or SBP at baseline At 120 min the intolerant group's mean FEV1 had significantly decreased to 1.05 L (95% CI 0.86-1.25 L; p<0.0001); the tolerant group experienced no change (1.10, 1.05-1.14 L; p=0.33). Conclusion:The administration of 1.25 mg bisoprolol was acutely well tolerated in >95% of COPD patients.

BACKGROUND:Lung cancer is the leading cause of cancer-related deaths worldwide, largely due to late-stage diagnosis. Lung cancer screening by computed tomography (CT) reduces mortality by detecting cancers earlier in high-risk individuals, prompting Australia to launch the National Lung Cancer Screening Program (NLCSP) in 2025. Understanding the differences between screen-detected and clinically diagnosed lung cancers is essential for evaluating the impact of screening. AIMS:To compare demographic, clinical and treatment characteristics of screen-detected versus clinically diagnosed lung cancer cases in Australia and assess evidence of earlier stage shift and treatment differences. METHODS:Screen-detected cases were sourced from the Queensland Lung Cancer Screening Study (QLCSS) and International Lung Screening Trial (ILST), including cancers identified at baseline CT and up to 5 years of follow-up. Clinically diagnosed cases from the corresponding periods were identified through Queensland Oncology Online. Data included demographics, smoking history, comorbidities, cancer stage and treatments. Wilcoxon rank-sum, Fisher exact and chi-squared tests (P < 0.05) were used to compare the groups. Sensitivity analyses included 1:2 case-control and NLCSP-eligible subgroup analysis. RESULTS:Screen-detected cases were more likely to be diagnosed at stage I (63% vs 28%, Padj < 0.01) and underwent surgery (Padj < 0.05), whereas clinically detected cases were mostly stage IV (36% vs 14%) and received more radiotherapy (Padj < 0.001). These differences persisted in sensitivity and subgroup analyses. CONCLUSIONS:Screen-detected lung cancers show a stage shift towards earlier diagnosis and higher surgical treatment rates, supporting the potential effectiveness of the NLCSP.

OBJECTIVES:This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To assess the benefits and harms of smoking cessation interventions in adults undergoing lung cancer screening with low-dose computed tomography (LDCT) of the chest.

Background:Cytological examination is of suboptimal sensitivity but high specificity for the diagnosis of malignant pleural effusions (MPEs). Pleural fluid extracellular vesicles (PFEVs) are enriched with disease-specific microRNAs (miRNAs) which may improve the diagnostic yield for MPE. Our previous study demonstrated the feasibility of isolating miRNAs from PFEVs and profiling PFEV miRNAs by Nanostring nCounter® Human v3 miRNA expression assay. Here, we interrogated in a small cohort to evaluate the diagnostic potential of PFEV miRNAs to differentiate between benign pleural effusion and MPE. Methods:Extracellular vesicles (EVs) from pleural fluids were isolated by two sequential ultracentrifugation steps. PFEVs were extracted and characterised by western blotting analysis, particle analysis by tunable resistive pulse sensing (TRPS) technology, and transmission electron microscopy (TEM). Total RNAs (including miRNAs) were extracted from PFEVs and profiled by the Nanostring nCounter® 827 probe miRNA expression assay. Differential expression analysis of the miRNA expression assays on PFEV samples was performed using the Bioconductor DESeq2 package. Results:EVs from pleural fluids were evident by staining of positive EV-associated protein markers, particle size distribution within the expected parameters, and the cup-shaped morphology by TEM. Employing Nanostring nCounter® Human v3 miRNA expression assay, this proof-of-principle study demonstrated PFEV miRNAs were differentially expressed between benign effusions and malignant effusions [malignant pleural mesothelioma (MPM) or lung adenocarcinoma metastatic to pleura (metLUAD)]. The expression of six miRNAs (hsa-miR-1246, hsa-miR-136-5p, hsa-miR-141-3p, hsa-miR-145-5p, hsa-miR-200c-3p, and hsa-miR-9-5p) significantly differed between benign and malignant effusions, or between MPM and metLUAD, at adjusted P<0.05 and log2fold change ≥1.0. Conclusions:The miRNAs identified from this study could be interrogated further for their utility as a single biomarker candidate or to be tested simultaneously in a panel to complement pleural effusion diagnostics. PFEV miRNAs represent a novel bioresource with potential to aid in the diagnosis of pleural effusions. Larger prospective studies are needed to confirm their diagnostic utility.

Rationale: People with COPD often experience multimorbidity, which further contributes to morbidity and mortality. Sub-optimal self-management of COPD leads to increased exacerbations and hospital admissions. Most prior self-management interventions for COPD have focused on single disease and biomedical outcomes. We aimed to test a novel self-management support program for COPD in the context of multimorbidity in Australian primary care, and hypothesized that at 12 months’ follow-up, patients in the intervention group will have improved COPD-related quality of life, COPD knowledge, patient activation and overall health-related quality of life compared to patients in the control group. Methods: The APCOM Trial is a pragmatic cluster randomized controlled clinical trial currently being conducted in 29 general practices across Australia. Participating general practices and their patients were randomized 1:1 to control and intervention groups. Patients were eligible if they were: i) aged 40 years and above, ii) had a spirometry-confirmed COPD diagnosis, iii) had visited their general practice in the past year, and iv) had at least one co-existing comorbidity. Patients in the intervention group receive a tailored, self-management program delivered by trained practice nurses via personalized health coaching sessions, in collaboration with their GPs and other relevant healthcare providers. The intervention, based on the Health Belief Model, comprises COPD knowledge, inhaler device technique, pulmonary rehabilitation referral if indicated, strategies to cope with COPD exacerbations and comorbidities, and smoking cessation if applicable. Outcome measures include the COPD Assessment Test (CAT) (range 0-40), COPD exacerbations, COPD Knowledge Questionnaire (range 0-13), Patient Activation Measure (PAM) (range 0-100), smoking status, COPD medication, attendance of pulmonary rehabilitation, health-related quality of life, Test of Adherence to Inhalers (intervention group only) and inhaler device technique (intervention group only). Results: The trial is currently at the stage of intervention delivery and 6 months’ data collection. Of 222 people who completed baseline data, 54% were female. Their mean age was 72 years (SD±9.68), and 35% lived alone. Patients had between 1-16 comorbidities. Their average CAT score was 19.57 (SD±7.50). Average scores of COPD knowledge and PAM were 7.55 (SD±2.35) and 58.32 (SD±13.06), respectively. The patients were prescribed 0-6 medications for their COPD; only 5% reported prior attendance at pulmonary rehabilitation. Conclusion: The baseline findings indicate a medium impact of COPD on patients’ health status, moderate level of COPD knowledge, and lack of confidence and skills to modify their health behaviour. Therefore, personalised self-management support is likely to be beneficial to the participants.

Background:Clinical decision support systems (CDSS) have the potential to play a crucial role in enhancing health care quality by providing evidence-based information to clinicians at the point of care. Despite their increasing popularity, there is a lack of comprehensive research exploring their design characterization and trends. This limits our understanding and ability to optimize their functionality, usability, and adoption in health care settings. Objective:This systematic review examined the design characteristics of CDSS from a user-centered perspective, focusing on user-centered design (UCD), user experience (UX), and usability, to identify related design challenges and provide insights into the implications for future design of CDSS. Methods:This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations and used a grounded theory analytical approach to guide the conduct, data analysis, and synthesis. A search of 4 major electronic databases (PubMed, Web of Science, Scopus, and IEEE Xplore) was conducted for papers published between 2013 and 2023, using predefined design-focused keywords (design, UX, implementation, evaluation, usability, and architecture). Papers were included if they focused on a designed CDSS for a health condition and discussed the design and UX aspects (eg, design approach, architecture, or integration). Papers were excluded if they solely covered technical implementation or architecture (eg, machine learning methods) or were editorials, reviews, books, conference abstracts, or study protocols. Results:Out of 1905 initially identified papers, 40 passed screening and eligibility checks for a full review and analysis. Analysis of the studies revealed that UCD is the most widely adopted approach for designing CDSS, with all design processes incorporating functional or usability evaluation mechanisms. The CDSS reported were mainly clinician-facing and mostly stand-alone systems, with their design lacking consideration for integration with existing clinical information systems and workflows. Through a UCD lens, four key categories of challenges relevant to CDSS design were identified: (1) usability and UX, (2) validity and reliability, (3) data quality and assurance, and (4) design and integration complexities. Notably, a subset of studies incorporating Explainable artificial intelligence highlighted its emerging role in addressing key challenges related to validity and reliability by fostering explainability, transparency, and trust in CDSS recommendations, while also supporting collaborative validation with users. Conclusions:While CDSS show promise in enhancing health care delivery, identified challenges have implications for their future design, efficacy, and utilization. Adopting pragmatic UCD design approaches that actively involve users is essential for enhancing usability and addressing identified UX challenges. Integrating with clinical systems is crucial for interoperability and presents opportunities for AI-enabled CDSS that rely on large patient data. Incorporating emerging technologies such as Explainable Artificial Intelligence can boost trust and acceptance. Enabling functionality for CDSS to support both clinicians and patients can create opportunities for effective use in virtual care.