Evidence map›Paper›PMID 32562611›Full record

Observational studyChest2020

Standardized Reporting and Management of Suspicious Findings on Chest CT Imaging Is Associated With Improved Lung Cancer Diagnosis in an Observational Study.

Thomas H Urbania, Jennifer R Dusendang, Lisa J Herrinton, Stacey Alexeeff, Douglas A Corley, Sora Ely, Ashish Patel, Todd Osinski, Lori C Sakoda

Open access · greenAbstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 24% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Thomas H UrbaniaDepartment of Radiology, Kaiser Permanente Northern California, Oakland, CA.
Jennifer R DusendangDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Lisa J HerrintonDivision of Research, Kaiser Permanente Northern California, Oakland, CA. Electronic address: lisa.herrinton@kp.org.
Stacey AlexeeffDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Douglas A CorleyDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Sora ElyDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Ashish PatelDepartment of Thoracic Surgery, Kaiser Permanente Northern California, Oakland, CA.
Todd OsinskiDepartment of Radiology, Kaiser Permanente Northern California, Oakland, CA.
Lori C SakodaDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Kaiser Permanente · US

Funding

Evaluating a Risk Prediction Model for Lung CancerK07CA188142 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI SAKODA, LORI · 2015 to 2019
$920k
NCI NIH HHS K07 CA188142
6 · The paper itself

Abstract

backgroundFollow-up of chest CT scan findings suspicious for lung cancer may be delayed because of inadequate documentation. Standardized reporting and follow-up may reduce time to diagnosis and care for lung cancer. STUDY DESIGN AND

methodsWe implemented a reporting system that standardizes tagging of chest CT scan reports by classifying pulmonary findings. The system also automates referral of patients with findings suspicious for lung cancer to a multidisciplinary care team for rapid review and follow-up. The system was designed to reduce the time to diagnosis, particularly for early-stage lung cancer. We evaluated the effectiveness of this system, using a quasi-experimental stepped wedge cluster design, examining 99,148 patients who underwent diagnostic (nonscreening) chest CT imaging from 2015 to 2017 and who had not received a chest CT scan in the preceding 24 months. We evaluated the association of the intervention with the incidence of diagnosis and surgical treatment of early-stage (I, II) and late-stage (III, IV) lung cancer within 120 days of chest CT imaging.

resultsForty percent of patients received the intervention. Among 2,856 patients (2.9%) who received diagnoses of lung cancer, 28% had early-stage disease. In multivariable analyses, the intervention was associated with 24% greater odds of early-stage diagnosis (OR, 1.24; 95% CI, 1.09-1.41) and no change in the odds of late-stage diagnosis (OR, 1.04; 95% CI, 0.95-1.14). The intervention was not associated with the rate of surgical treatment within 120 days.

interpretationIn this large quasi-experimental community-based observational study, implementation of a system that combines standardized tagging of chest CT scan reports with clinical navigation was effective for increasing the diagnosis of early-stage lung cancer.

Indexed as

AdolescentAdultAgedAged, 80 and overDelayed DiagnosisFemaleHumansIncidenceLung NeoplasmsMaleMiddle AgedRadiography, ThoracicRetrospective StudiesTomography, X-Ray ComputedUnited StatesYoung AdultCT imagingdiagnostic accuracylung cancer

Identifiers

PMID32562611
PMCPMC7783862
OpenAlexW3036276936

What OpenQuestion holds

Textmetadata
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.