Article

The relationship between waiting time for radiotherapy and clinical outcomes: a systematic review of the literature.

Queen's Cancer Research Institute, Queen's University, Kingston, Ontario, Canada.
Radiotherapy and Oncology (Impact Factor: 4.86). 05/2008; 87(1):3-16. DOI: 10.1016/j.radonc.2007.11.016
Source: PubMed

ABSTRACT To synthesize the direct clinical evidence relating waiting times (WTs) for radiotherapy (RT) to the outcomes of RT.
We did a systematic review of the literature between 1975 and 2005 to identify clinical studies describing the relationship between WTs and outcomes of RT. Only high quality (HQ) studies that had adequately controlled for confounding factors were included in the primary analysis. WTs that had originally been reported as a categorical variable were converted to a continuous variable based on the distribution of WTs in each category. Meta-analyses were done using a fixed-effect model.
The systematic review identified 44 relevant studies. Meta-analyses of 20 HQ studies of local control demonstrated a significant increase in the risk of local failure with increasing WT, RRlocal recurrence/month =1.14, 95% Confidence Intervals (CI): 1.09-1.21. For post-operative RT for breast cancer; RRlocal recurrence/month =1.11, 95%CI: 1.04-1.19. For post-operative RT for head and neck cancer, RRlocal recurrenc/month =1.28, 95%CI: 1.08-1.52. For definitive RT for head and neck cancer, RRlocal recurrence/month =1.15, 95%CI: 1.02-1.29. There was little evidence of any association between WTs and the risk of distant metastasis. Meta-analyses of the 6 HQ studies of breast cancer showed RRmetastasis/month =1.04, 95%CI: 0.98-1.09. Meta-analyses of 4 HQ studies of breast cancer showed no significant decrease in survival with increasing WT, RRdeath/month =1.06, 95%CI: 0.97-1.16, but there was a marginally significant decrease in survival in 4 HQ studies of head and neck cancer, RRdeath/month =1.16, 95%CI: 1.02-1.32.
The risk of local recurrence increases with increasing WTs for RT. The increase in local recurrence rate may translate into decreased survival in some clinical situations. WTs for RT should be as short as reasonably achievable.

0 Bookmarks
  • [Show abstract] [Hide abstract]
    ABSTRACT: The measurement of population benefits is important for priority setting, economic evaluation and quality improvement. It also informs advocacy. In this article, the use of demand models to estimate the achievable benefit of cancer therapy is reviewed. Achievable benefit refers to the treatment benefit achievable under optimal conditions. The population benefit of radiotherapy has been used as an example. Demand models provide a means of estimating the optimal proportion of patients with treatment indications when guidelines are followed. They may be used to estimate achievable benefit. The choice of end point should reflect the range of benefits associated with the treatment of interest. In some cases, further model development is needed if a pre-existing demand model is used. The benefit of treatment for each indication is estimated using a systematic review process. The highest level of evidence is used to define the benefit for each indication. In cases where multiple sources of the same level and quality of evidence exist, a meta-analysis is carried out. Population-based effectiveness data sources are considered, but three major challenges to their use are: (i) generalisability of the observed outcomes, (ii) data resolution and (iii) confounding and bias. The population benefit determined from this process describes the population proportion achieving a benefit due to the use of guideline-based treatment, compared with no use of that treatment. Sensitivity analysis provides a means for modelling the effect of model uncertainties. The predominant uncertainty is most often due to uncertainty in indication proportion. Preference-sensitive treatment decisions are a common example. The described approach to estimating the achievable benefit of cancer therapy is robust to model uncertainties, rapidly adaptable and is transparent. However, estimates rely on the quality of model data sources and may be affected by model assumptions. Models should be developed for a broader range of modalities of cancer therapy and relevant end points. Copyright © 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.
    Clinical Oncology 11/2014; 27(2). · 2.83 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Countries, states, and island nations often need forward planning of their radiotherapy services driven by different motives. Countries without radiotherapy services sponsor patients to receive radiotherapy abroad. They often engage professionals for a feasibility study in order to establish whether it would be more cost-beneficial to establish a radiotherapy facility. Countries where radiotherapy services have developed without any central planning, find themselves in situations where many of the available centers are private and thus inaccessible for a majority of patients with limited resources. Government may decide to plan ahead when a significant exodus of cancer patients travel to another country for treatment, thus exposing the failure of the country to provide this medical service for its citizens. In developed countries, the trigger has been the existence of highly visible waiting lists for radiotherapy revealing a shortage of radiotherapy equipment. This paper suggests that there should be a systematic and comprehensive process of long-term planning of radiotherapy services at the national level, taking into account the regulatory infrastructure for radiation protection, planning of centers, equipment, staff, education programs, quality assurance, and sustainability aspects. Realistic budgetary and cost considerations must also be part of the project proposal or business plan.
    Frontiers in Oncology 11/2014; 4:315.
  • Source
    Oral Oncology 12/2014; · 3.03 Impact Factor

Full-text (2 Sources)

Download
Available from
Oct 2, 2014