Evidence map›Paper›PMID 32631384›Full record

ArticleBMC pulmonary medicine2020

Conventional Computed Tomographic Calcium Scoring vs full chest CTCS for lung cancer screening: a cost-effectiveness analysis.

Boxiang Jiang, Philip A Linden, Amit Gupta, Craig Jarrett, Stephanie G Worrell, Vanessa P Ho, Yaron Perry, Christopher W Towe, University Hospitals’ Research in Surgical Outcomes and Effectiveness (UH-RISES)

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC pulmonary medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 3 pooled it
0.6field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 3 syntheses or guidelines pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. 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

9 authors at 2 institutions in 1 country.

Boxiang JiangDepartment of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Philip A LindenDepartment of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Amit GuptaDepartment of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Craig JarrettDepartment of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Stephanie G WorrellDepartment of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Vanessa P HoDeparment of Trauma and Acute Care Surgery, MetroHealth Hospital, Cleveland, OH, USA.
Yaron PerryDepartment of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Christopher W ToweDepartment of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA. christopher.towe@uhhospitals.org.ORCID http://orcid.org/0000-0002-3850-8040
University Hospitals’ Research in Surgical Outcomes and Effectiveness (UH-RISES)
University Hospitals of Cleveland · USMetroHealth · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConventional CTCS images the mid/lower chest for coronary artery disease (CAD). Because many CAD patients are also at risk for lung malignancy, CTCS often discovers incidental pulmonary nodules (IPN). CTCS excludes the upper chest, where malignancy is common. Full-chest CTCS (FCT) may be a cost-effective screening tool for IPN.

methodsA decision tree was created to compare a FCT to CTCS in a hypothetical patient cohort with suspected CAD. (Figure) The design compares the effects of missed cancers on CTCS with the cost of working up non-malignant nodules on FCT. The model was informed by results of the National Lung Screening Trial and literature review, including the rate of malignancy among patients receiving CTCS and the rate of malignancy in upper vs lower portions of the lung. The analysis outcomes are Quality-Adjusted Life Year (QALY) and incremental cost-effectiveness ratio (ICER), which is generally considered beneficial when <$50,000/QALY.

resultsLiterature review suggests that rate of IPNs in the upper portion of the lung varied from 47 to 76%. Our model assumed that IPNs occur in upper and lower portions of the lung with equal frequency. The model also assumes an equal malignancy potential in upper lung IPNs despite data that malignancy occurs 61-66% in upper lung fields. In the base case analysis, a FCT will lead to an increase of 0.03 QALYs comparing to conventional CTCS (14.54 vs 14.51 QALY, respectively), which translates into an QALY increase of 16 days. The associated incremental cost for FCT is $278 ($1027 vs $748, FCT vs CTCS respectively. The incremental cost-effectiveness ratio (ICER) is $10,289/QALY, suggesting significant benefit. Sensitivity analysis shows this benefit increases proportional to the rate of malignancy in upper lung fields.

conclusionConventional CTCS may be a missed opportunity to screen for upper lung field cancers in high risk patients. The ICER of FCT is better than screening for breast cancer screening (mammograms $80 k/QALY) and colon cancer (colonoscopy $6 k/QALY). Prospective studies are appropriate to define protocols for FCT.

Indexed as

Age FactorsCoronary Artery DiseaseCost-Benefit AnalysisEarly Detection of CancerFemaleHumansIncidental FindingsLung NeoplasmsMaleMiddle AgedMonte Carlo MethodQuality-Adjusted Life YearsRisk AssessmentTomography, X-Ray ComputedVascular CalcificationConventional computed tomographic calcium scoringCoronary artery diseaseCost-effectiveness analysisFull chest calcium scoring scanLung cancer screening

Identifiers

PMID32631384
PMCPMC7336401
OpenAlexW3038924777

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.