Elango Vijaykumar,1,* James Gatenby,2,* Alexander Evans,3 William Henley,3 Neva Eleangovan,2 Amy Couper,3,4 Margee Kerr,3,4 Cono Ariti,2 John Busby,3 Luis Alves,5,6 Mona Bafadhel,7 Mohit Bhutani,8 Victoria Carter,2 Rongchang Chen,9 James Chalmers,10 Michael G Crooks,11,12 Graham Devereux,13 Andrew P Dickens,2 Daryl Freeman,14 Chris P Gale,15– 17 John Martin Gibson,18– 20 David Halpin,21 Katherine Hickman,22,23 John R Hurst,24 Jonathan Marshall,25 Fernando J Martinez,26 Marc Miravitlles,27 Hana Mullerova,28 Tamsin Morris,29 Shigeo Muro,30 Jonatan Natman,31 Jennifer K Quint,32 Anita Sharma,33 Dave Singh,34 Mukesh Singh,35,36 Daiana Stolz,37 Rachel Pullen,3 Della Varghese,38 Tom M Wilkinson,39,40 Tonya Winders,41 David Price3,4,421Modality Partnership, Horley, Surrey, UK; 2Optimum Patient Care, Cambridge, UK; 3Observational and Pragmatic Research Institute, Singapore, Singapore; 4Optimum Patient Care Global, Cambridge, UK; 5EPI Unit, Institute of Public Health, University of Porto, Porto, Portugal; 6Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, Portugal; 7King’s Centre for Lung Health, School of Immunology and Microbial Science, Faculty of Life Sciences and Medicine, King’s College London, London, UK; 8Division of Pulmonary Medicine, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada; 9Key Laboratory of Respiratory Disease of Shenzhen, Shenzhen Institute of Respiratory Disease, Shenzhen People’s Hospital (Second Affiliated Hospital of Jinan University, First Affiliated Hospital of South University of Science and Technology of China), Shenzhen, Guangdong, People’s Republic of China; 10Scottish Centre for Respiratory Research, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK; 11Hull University Teaching Hospitals NHS Trust, Hull, UK; 12Hull York Medical School, University of Hull, Hull, UK; 13Clinical Sciences, Liverpool School of Tropical Medicine, University of Liverpool, Liverpool, UK; 14East of England Community Health & Care NHS Trust, Norwich, UK; 15Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK; 16Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; 17Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK; 18NorthWest EHealth, Salford, UK; 19Department of Diabetes and Endocrinology, Salford Royal Hospital, Salford, UK; 20Division of Diabetes, Endocrinology and Gastroenterology, University of Manchester, Manchester, UK; 21University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK; 22NRAP, Care Quality Improvement Department (CQID), RCP, London, UK; 23Low Moor Medical Practice, Bradford, UK; 24UCL Respiratory, University College London, London, UK; 25Global Medical Affairs, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK; 26New York-Presbyterian Weill Cornell Medical Center, New York, NY, USA; 27Pneumology Dept, Hospital Universitari Vall d’Hebron, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Barcelona Hospital Campus, CIBER de Enfermedades Respiratorias (CIBERES), Barcelona, Spain; 28Respiratory & Immunology, Medical and Payer Evidence Strategy, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK; 29Medical and Scientific Affairs, AstraZeneca, London, UK; 30Department of Respiratory Medicine, Nara Medical University, Nara, Japan; 31Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Gothenburg, Sweden; 32School of Public Health, Imperial College London, London, UK; 33Platinum Medical Centre, Chermside, QLD, Australia; 34Division of Infection, Immunity & Respiratory Medicine, University of Manchester, Manchester University NHS Foundation Trust, Manchester, UK; 35Horse Fair Practice Group, Keele University Medical School Teaching Practice, Rugeley, UK; 36Staffordshire & Stoke-on-Trent ICB, Stafford, UK; 37Clinic of Respiratory Medicine and Faculty of Medicine, University of Freiburg, Freiburg, Germany; 38Evidence Strategy, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, MD, USA; 39NIHR Southampton BRC Southampton, Southampton, UK; 40Faculty of Medicine, University of Southampton, Southampton, UK; 41Global Allergy & Airways Patient Platform, Vienna, Austria; 42Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK*These authors contributed equally to this workCorrespondence: David Price, Observational and Pragmatic Research Institute, 22 Sin Ming Lane, #06-76, Midview City, Singapore, 573969, Singapore, Tel +65 3105 1489, Email dprice@opri.sgBackground: Integrated COPD management has improved outcomes for patients with diagnosed COPD. The CONQUEST Quality Improvement Program aims to improve the diagnosis and management for patients with potential and diagnosed modifiable high-risk (MHR) COPD, defined as those with a history of exacerbations and opportunities for treatment optimization.Methods: Utilizing the CONQUEST registry and Optimum Patient Care (OPC) Research Database, this observational cohort analysis assessed changes in quality indicator attainment over 12 months among potentially undiagnosed, newly diagnosed and already diagnosed patients with MHR COPD in exposed and matched usual care (UC) general practices. In exposed practices, newly diagnosed and already diagnosed patients were restricted to those receiving OPC-led CONQUEST clinics. Outcomes were compared using difference-in-difference generalized linear models with overlap propensity weighting.Findings: From 168 UK practices, we identified 113,574 potentially undiagnosed (59,706 exposed, 53,868 UC), 2787 newly diagnosed (1448 exposed of which 252 attended OPC clinics, 1339 UC) and 38,054 already diagnosed (21,670 exposed of which 392 attended OPC clinics, 16,384 UC). Potentially undiagnosed patients in exposed practices were more likely to subsequently receive a spirometry-confirmed COPD diagnosis (1.0% vs 0.6%; adjusted ratios-of-risk-ratios [ARRR]:1.55, 95% CI: 1.19– 2.01) compared to UC. Initiation or step-up to guideline-appropriate inhaled therapy was more likely among OPC-supported patients in exposed practices for newly diagnosed (96.4% vs 55.7%; ARRR: 1.71, 95% CI: 1.41– 2.06) and already diagnosed (91.6% vs 39.8%; ARRR: 1.94, 95% CI: 1.77– 2.1212) versus unsupported UC.Interpretation: CONQUEST is associated with measurable improvements in quality indicator attainment aligned with guideline-recommended identification and management of COPD, including higher rates of spirometry-confirmed COPD diagnosis and treatment optimization. Future randomized trials will evaluate if this translates into improved outcomes including reduced exacerbations and hospitalizations.Keywords: COPD, observational study, optimum patient care research database, quality improvement, primary care, CONQUEST
Carl Marincowitz,1 Richard Jacques,1 Katherine Zwerger,1 Graham Martin,2 Karen Spilsbury,3 Jennifer Kirsty Burton,4 Fawn Harrad-Hyde,5 David Price,6 Emily Lam,7 Hilary M Garrett,7 Suzanne Mason11School of Medicine and Population Health, The University of Sheffield, Sheffield, UK; 2THIS Institute, University of Cambridge, Cambridge, UK; 3Faculty of Medicine and Health, University of Leeds, Leeds, UK; 4School of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow, UK; 5Health Sciences, University of Leicester, Leicester, UK; 6Optimum Patient Care UK, Cambridge, UK; 7Patient Public Contributor, Health Data Research UK, London, UKCorrespondence: Carl Marincowitz, Email c.marincowitz@sheffield.ac.ukPurpose: There over 250,000 care home residents in England and they account for a disproportionate number of hospital admissions. Since 2020, structured medication reviews for care home residents have been mandated in primary care contracts. We used a nationally representative cohort of care home residents to identify predictors of anticholinergic burden and sedative load and measure regional variation in prescribing.Patients and Methods: Using primary care data from a third of practices in England we derived four 6-month cohorts of over 40,000 care home residents linked to the national regulator’s register of care homes between 01/01/2023 and 31/12/2024. We estimated three-level logistic regression models including resident, postcode district and commissioner (ICB) level factors which predicted high anticholinergic burden and sedative load. We estimated funnel-plots to measure case-mix risk adjusted variation in prescribing at postcode district and commissioner geographical levels.Results: Significant predictors of anticholinergic burden and sedative load included gender, age, frailty, and comorbidity. We demonstrated some case-mix adjusted variation at ICB and postcode district level (anticholinergic burden 2.3-fold ICB and 3.5-fold variation at district level; sedative load 1.7-fold ICB and 2.7-fold variation at District level).Conclusion: This is one of the largest studies of care home residents in England. We have identified factors associated with risk of residents having a high anticholinergic burden or sedative load which could be used to help target personalised interventions.Keywords: anticholinergic burden, sedative load, care homes
John L Gore,1 Allison Thompson,2 Sneha S Kelkar,3 Viktor Chirikov,3 Shweta Kamat,3 Wenying Quan,3 Kimberly Donnowitz,4 Christopher Young,4 David Nimke,4 Christina Marie Quicquaro,4 Candice L Willmon,5 Priti Pednekar41Department of Urology, University of Washington School of Medicine, Seattle, WA, USA; 2HTA Value & Evidence, Medical Affairs, Pfizer Inc., Bothell, WA, USA; 3Open Health Evidence and Access, Open Health Group, New York, NY, USA; 4Medical Value & Evidence, Medical Affairs, Astellas Pharma Global Development, Northbrook, IL, USA; 5Oncology Global, Medical Affairs, Pfizer Inc., Bothell, WA, USACorrespondence: Priti Pednekar, Medical Value & Evidence, Medical Affairs, Astellas Pharma Global Development, 2375 Waterview Drive, Northbrook, IL, 60062-6111, USA, Tel +1 267-438-3588, Email priti.pednekar@astellas.comIntroduction: This study reported characteristics and real-world treatment patterns of patients with muscle-invasive bladder cancer after radical cystectomy with or without neoadjuvant therapy in the United States.Patients and Methods: This cohort study extracted data from the Inovalon Insights Payer-Sourced© claims dataset (January 1, 2020–December 31, 2021). Eligible patients (≥ 18 years) diagnosed with bladder cancer who had undergone radical cystectomy with or without neoadjuvant therapy were followed from index date (first day of radical cystectomy or neoadjuvant therapy) until study end, diagnosis of metastasis, or the end of enrollment in their health plan. Neoadjuvant therapy was defined as systemic therapy received ≤ 6 months before radical cystectomy.Results: A total of 332 patients were included (male, 73.8%; median age, 64.0 years). Mean age-adjusted Charlson Comorbidity Index score was 6.3. 41.3% of patients underwent radical cystectomy without neoadjuvant therapy; 58.7% of patients received neoadjuvant therapy. The most frequent type of neoadjuvant chemotherapy was chemotherapy with cisplatin (46.1%): most commonly cisplatin with gemcitabine (22.9%) or cisplatin with doxorubicin, methotrexate, and vinblastine (16.9%). Adjuvant therapy was received by 11.7% of patients; with immunotherapy (6.0%) being the most common treatment. After radical cystectomy or adjuvant therapy, 63 patients received subsequent therapy, most commonly immunotherapy (41.3%). The highest intensity of healthcare resource utilization during the follow-up period was for outpatient visits (mean per-patient per-month, 6.4).Conclusion: Many patients underwent radical cystectomy without neoadjuvant or adjuvant therapy, suggesting an unmet need for alternative therapeutic regimens. Given the limitations of this retrospective claims-based design, including reliance on a claims-based muscle-invasive bladder cancer identification algorithm subject to coding errors, absence of clinical context, potential COVID-19 disruptions to care patterns, and a study window predating the approval of several key agents, these findings should be interpreted with caution. Further evaluation of contemporary treatment patterns is needed.Keywords: radical cystectomy, neoadjuvant therapy, adjuvant therapy
Athena Gogali,1 Alexandros Ginis,2 Maria Bertoli,2 Polyanthi Papanastasiou,2 Konstantinos Kostikas11Respiratory Medicine Department, University of Ioannina Medical School, Ioannina, Greece; 2Medical Department, ELPEN Pharmaceutical Co. Inc, Pikermi, Attica, GreeceCorrespondence: Konstantinos Kostikas, Respiratory Medicine Department, University of Ioannina Medical School, Stavrou Niarchou Avenue, Ioannina, 45500, Greece, Email ktkostikas@gmail.comPurpose: Patient adherence and satisfaction are critical for effective asthma and chronic obstructive pulmonary disease (COPD) management, as adherence strongly influences symptoms and prognosis. Patient-reported outcomes, including treatment satisfaction and quality of life, provide valuable insights into the effectiveness of therapeutic interventions from the perspective of individual patients. This study evaluated patient satisfaction with the Elpenhaler device and its impact on quality of life in routine clinical practice.Patients and methods: Individual participant data were pooled from seven real-life studies of fixed-dose combinations delivered via the Elpenhaler device (asthma, budesonide/formoterol: BOREAS, SKIRON, COMPLETE, NOTOS; COPD, salmeterol/fluticasone: AEOLOS, AHEAD, RETRIEVE). Patient satisfaction was assessed with the Feeling of Satisfaction with Inhaler (FSI-10) questionnaire in the four studies in which it was administered, and quality of life in asthma with the mini Asthma Quality of Life Questionnaire (mAQLQ) at baseline, 3 and 6 months. The minimum clinically important difference (MCID) was defined as a change of at least 0.5 units in the mAQLQ total score.Results: In 4333 patients with evaluable FSI-10 score, overall satisfaction was high (mean total score 44.1, standard deviation 5.4), with 94.5% of patients expressing confidence in correct usage. Multivariable analysis showed that age ≥ 65 years, male sex and lower education level were independently associated with lower satisfaction, whereas never smoking predicted higher satisfaction. In 4212 patients with asthma, mAQLQ total score improved from 4.52 at baseline to 6.06 at 3 months and 6.27 at 6 months; the MCID was achieved by 83.4% and 87.8% of evaluable patients, respectively. Never smoking and lower education level predicted MCID achievement.Conclusion: Treatment delivered via the Elpenhaler is associated with high patient satisfaction and with significant and sustained improvements in quality of life, supporting its effectiveness in real-world respiratory care. The identification of factors associated with satisfaction and quality of life improvement may help in personalising treatment and optimising care.Keywords: elpenhaler, patient satisfaction, quality of life, QoL, asthma, COPD, patient-reported outcomes, dry powder inhaler
Background:Multidisciplinary lifestyle care in routine practice occurs alongside medication changes and irregular testing. We described outcomes during a one-year high-contact specialty-clinic program, separating average post-baseline differences from approximately 12-month change. Materials and Methods:This retrospective single-center study included 170 adults with hypertension, dyslipidemia, or diabetes. The primary estimand was the adjusted average difference between baseline and any available post-baseline measurement. Separate linear mixed models included a participant random intercept and fixed terms for follow-up status, age at measurement, sex, and time-varying antidiabetic, antihypertensive, and lipid-lowering medication classes. A complete-pair analysis used the measurement nearest 12 months within a 10-14-month window. Outcome-specific missingness was reported. A Bonferroni benchmark of P<0.0028 was applied across 18 mixed-model outcomes; paired P values were adjusted across all 18 prespecified outcomes. Results:Mean age was 63.0 years (SD 12.6), and 92 participants (54.1%) were women. Body-composition data were nearly complete, whereas physical-function data were available for a selected subset. Differences meeting the multiplicity benchmark were body weight (-2.11 kg, 95% CI -2.68 to -1.54), body fat percentage (-2.04 percentage points, -2.53 to -1.56), waist circumference (-2.72 cm, -3.43 to -2.01), body mass index (-0.90 kg/m2, -1.13 to -0.68), 1-min walk (+7.50 steps, 4.51 to 10.49), chair stand (+2.27 repetitions, 1.47 to 3.07), one-leg stand (+7.19 s, 3.37 to 11.00), and low-density lipoprotein cholesterol (-6.44 mg/dL, -10.49 to -2.39). The 10-14-month complete-pair analysis was directionally consistent. Conclusion:During this single-arm program, adiposity measures changed modestly and lower-extremity performance showed larger standardized differences. No comparator, outcome-specific missingness, unknown attendance, and medication-measurement limitations preclude causal attribution.
