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Title: The use of optimal treatment for DLBCL is improving in all age groups and is a key factor in overall survival, but non-clinical factors influence treatment.
Epworth Authors: Prince, Miles
Other Authors: Doo, Nicole
White, Victoria
Martin, Kara
Bassett, Julie
Harrison, Simon
Jefford, Michael
Winship, Ingrid
Millar, Jeremy
Milne, Roger
Seymour, John
Giles, Graham
Keywords: Cancer Survival
Diffuse Large B Cell Lymphoma (DLBCL)
Epidemiologic Studies
Patterns of Care
Non-Hodgkin Lymphoma
Victorian Cancer Registry
Suboptimal Treatment
Optimal Treatment
Multivariable Analysis
Demographic Variation
Univariable/multivariable Logistic Regression models
Therapeutic Modalities
Non-Clinical Factors
Socioeconomic Status (SES)
Area of Remoteness Index of Australia
Epworth Centre for Immunotherapies and Snowdome Laboratories
Molecular Oncology and Cancer Immunology
Cancer Services Clinical Institute, Epworth HealthCare, Victoria, Australia
Issue Date: Jul-2019
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
Citation: Cancers, 11(7), 928.
Abstract: Introduction: Diffuse large B cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma for which a cure is usually the therapeutic goal of optimal treatment. Using a large population-based cohort we sought to examine the factors associated with optimal DLBCL treatment and survival. Methods: DLBCL cases were identified through the population-based Victorian Cancer Registry, capturing new diagnoses for two time periods: 2008-2009 and 2012-2013. Treatment was pre-emptively classified as 'optimal' or 'suboptimal', according to compliance with current treatment guidelines. Univariable and multivariable logistic regression models were fitted to determine factors associated with treatment and survival. Results: Altogether, 1442 DLBCL cases were included. Based on multivariable analysis, delivery of optimal treatment was less likely for those aged ≥80 years (p < 0.001), women (p = 0.012), those with medical comorbidity (p < 0.001), those treated in a non-metropolitan hospital (p = 0.02) and those who were ex-smokers (p = 0.02). Delivery of optimal treatment increased between 2008-2009 and the 2012-2013 (from 60% to 79%, p < 0.001). Delivery of optimal treatment was independently associated with a lower risk of death (hazard ratio (HR) = 0.60 (95% confidence interval (CI) 0.45-0.81), p = 0.001). Conclusion: Delivery of optimal treatment for DLBCL is associated with hospital location and category, highlighting possible demographic variation in treatment patterns. Together with an increase in the proportion of patients receiving optimal treatment in the more recent time period, this suggests that treatment decisions in DLBCL may be subject to non-clinical influences, which may have implications when evaluating equity of treatment access. The positive association with survival emphasizes the importance of delivering optimal treatment in DLBCL.
DOI: 10.3390/cancers11070928
PubMed URL:
ISSN: 2072-6694
Journal Title: Cancers (Basel)
Type: Journal Article
Affiliated Organisations: Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
Concord Repatriation General Hospital, Sydney Medical School, University of Sydney, Sydney, NSW 2139, Australia
Concord Clinical School, University of Sydney, Concord, NSW 2139, Australia
School of Psychology, Faculty of Health, Deakin University, Geelong, VIC 3220, Australia
Centre for Behavioural Research in Cancer, Cancer Council Victoria, Melbourne, VIC 3004, Australia
Department of Haematology, Peter MacCallum Cancer Centre & Royal Melbourne Hospital, Melbourne, VIC 3000, Australia
Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC 3010, Australia
Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC 3050, Australia
Department of Medicine, The University of Melbourne, Parkville, VIC 3010, Australia
Alfred Health Radiation Oncology, Alfred and LaTrobe Regional Hospital, Melbourne, VIC 3004, Australia
School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia
Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia
Type of Clinical Study or Trial: Cohort Study
Appears in Collections:Cancer Services

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