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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 Chemotherapy 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 R-CHOP 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. |
URI: | http://hdl.handle.net/11434/2042 |
DOI: | 10.3390/cancers11070928 |
PubMed URL: | https://pubmed.ncbi.nlm.nih.gov/31269764 |
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 MOCI |
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