ArticleBMJ open2020
Cost-effectiveness analysis of PET/CT surveillance imaging to detect systemic recurrence in resected stage III melanoma: study protocol.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Comparison of CT, MRI, and F-18 FDG PET/CT for initial N-staging of oral squamous cell carcinoma: a cost-effectiveness analysis.European journal of nuclear medicine and molecular imaging · 2022Article
- The Impact of Surveillance Imaging Frequency on the Detection of Distant Disease for Patients with Resected Stage III Melanoma.Annals of surgical oncology · 2022Article
- Article
- Cost-Effectiveness of PET/CT Surveillance Schedules to Detect Distant Recurrence of Resected Stage III Melanoma.International journal of environmental research and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIn the new era of effective systemic therapies for advanced melanoma, early detection of lower volume recurrent disease using surveillance imaging can improve survival. However, intensive imaging follow-up strategies are likely to increase costs to health systems and may pose risks to patients. The objective of this study is to estimate from the Australian health system perspective the cost-effectiveness of four follow-up strategies in resected stage III melanoma over a 5-year period following surgical treatment with curative intent. METHODS AND ANALYSIS: A decision-analytic model will be built to estimate the costs and benefits of (1) 12 monthly, (2) 6 monthly, (3) 3-4 monthly positron emission tomography/CT imaging for 5 years, compared with (4) no imaging follow-up. The model will be populated with probabilities of disease recurrence, test performance measures using data from >1000 consecutive resected stage III melanoma patients from Melanoma Institute Australia diagnosed between 2000 and 2017. Healthcare resource use, including surveillance imaging, doctor's visits, subsequent tests and procedures to investigate suspicious findings, will be quantified from detailed patient records and valued using Australian reference pricing. Economic outcomes include cost per new distant melanoma recurrence detected and cost per diagnostic error avoided, for no imaging compared with the other strategies.Deterministic sensitivity analyses will examine the robustness of model results. ETHICS AND DISSEMINATION: This study was approved by the Sydney Local Health District, Sydney Local Health District Ethics Review Committee (RPAH Zone), AU/1/830638 and the Australian Institute of Health and Welfare (EO2019-1-454). The results of this study will be published in peer-reviewed medical and health economics journals and will inform melanoma management guidelines.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.