Full text

Turn on search term navigation

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

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
Author
Nicole Wong Doo 1   VIAFID ORCID Logo  ; White, Victoria M 2 ; Martin, Kara 3 ; Bassett, Julie K 3 ; H Miles Prince 4   VIAFID ORCID Logo  ; Harrison, Simon J 4 ; Jefford, Michael 5 ; Winship, Ingrid 6 ; Millar, Jeremy L 7   VIAFID ORCID Logo  ; Milne, Roger L 8 ; Seymour, John F 4   VIAFID ORCID Logo  ; Giles, Graham G 8 

 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 
 Cancer Epidemiology Division, 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 
 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 
 Cancer Epidemiology Division, Cancer Council Victoria, 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 
First page
928
Publication year
2019
Publication date
2019
Publisher
MDPI AG
e-ISSN
20726694
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2547490077
Copyright
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.