Evidence map›Paper›PMID 29787590›Full record

ArticlePloS one2018

Cost-effectiveness of an autoantibody test (EarlyCDT-Lung) as an aid to early diagnosis of lung cancer in patients with incidentally detected pulmonary nodules.

John Edelsberg, Derek Weycker, Mark Atwood, Geoffrey Hamilton-Fairley, James R Jett

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 3 pooled it
4.4field-weighted citation impact, top 5% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 citations in OpenAlex.

  1. Pooled it
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  4. Trial
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  6. Article
  7. Review
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  11. Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.American journal of respiratory and critical care medicine · 2021
    Article
  12. Article
  13. Review
  14. Review
  15. Molecular biomarkers in early stage lung cancer.Translational lung cancer research · 2021
    Review
  16. The National Cancer Institute Early Detection Research Network: Two Decades of Progress.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    Review
  17. Biomarkers for Lung Cancer Screening and Detection.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    Article
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

John EdelsbergPolicy Analysis Inc. (PAI), Brookline, MA, United States of America.
Derek WeyckerPolicy Analysis Inc. (PAI), Brookline, MA, United States of America.ORCID 0000-0002-5405-2215
Mark AtwoodPolicy Analysis Inc. (PAI), Brookline, MA, United States of America.
Geoffrey Hamilton-FairleyOncimmune Ltd, Nottingham, United Kingdom.
James R JettOncimmune LLC, Napa, CA, United States of America.
Policy Analysis (United States) · USAstrimmune (United Kingdom) · GBOncoImmune (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePatients who have incidentally detected pulmonary nodules and an estimated intermediate risk (5-60%) of lung cancer frequently are followed via computed tomography (CT) surveillance to detect nodule growth, despite guidelines for a more aggressive diagnostic strategy. We examined the cost-effectiveness of an autoantibody test (AABT)-Early Cancer Detection Test-Lung (EarlyCDT-LungTM)-as an aid to early diagnosis of lung cancer among such patients.

methodsWe developed a decision-analytic model to evaluate use of the AABT versus CT surveillance alone. In the model, patients with a positive AABT-because they are at substantially enhanced risk of lung cancer-are assumed to go directly to biopsy, resulting in diagnosis of lung cancer in earlier stages than under current guidelines (a beneficial stage shift). Patients with a negative AABT, and those scheduled for CT surveillance alone, are assumed to have periodic CT screenings to detect rapid growth and thus to have their lung cancers diagnosed-on average-at more advanced stages.

resultsAmong 1,000 patients who have incidentally detected nodules 8-30 mm, have an intermediate-risk of lung cancer, and are evaluated by CT surveillance alone, 95 (9.5%) are assumed to have lung cancer (local, 73.6%; regional, 22.0%; distant, 4.4%). With use of the AABT set at a sensitivity/specificity of 41%/93% (stage shift = 10.8%), although expected costs would be higher by $949,442 ($949 per person), life years would be higher by 53 (0.05 per person), resulting in a cost per life-year gained of $18,029 and a cost per quality-adjusted life year (QALY) gained of $24,330. With use of the AABT set at a sensitivity/specificity of 28%/98% (stage shift = 7.4%), corresponding cost-effectiveness ratios would be $18,454 and $24,833.

conclusionsUnder our base-case assumptions, and reasonable variations thereof, using AABT as an aid in the early diagnosis of lung cancer in patients with incidentally detected pulmonary nodules who are estimated to be at intermediate risk of lung cancer and are scheduled for CT surveillance alone is likely to be a cost-effective use of healthcare resources.

Indexed as

Cost-Benefit AnalysisIncidental FindingsAgedAutoantibodiesDisease ProgressionEarly Detection of CancerFalse Positive ReactionsFemaleHealth Care CostsHumansLife ExpectancyLung NeoplasmsMaleTomography, X-Ray ComputedAutoantibodies

Identifiers

PMID29787590
PMCPMC5963796
OpenAlexW2803795774

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.