Evidence map›Paper›PMID 35348055›Full record

ArticleThe Permanente journal2021

An Intervention to Tag Findings Suspicious for Lung Cancer on Chest Computed Tomography Has Good Sensitivity and Number Needed to Diagnose.

Jennifer R Dusendang, Lori C Sakoda, Thomas H Urbania, Sora Ely, Todd Osinski, Ashish Patel, Lisa J Herrinton

Open access · greenAbstract read
In one paragraph

Article in The Permanente journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 69% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

7 authors at 1 institution in 1 country.

Jennifer R DusendangDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Lori C SakodaDivision of Research, Kaiser Permanente Northern California, Oakland, CA.
Thomas H UrbaniaDepartment of Radiology, Kaiser Permanente Northern California, Oakland, CA.
Sora ElyDepartment of Chest Surgery, Kaiser Permanente Northern California, Oakland, CA.
Todd OsinskiDepartment of Radiology, Kaiser Permanente Northern California, Oakland, CA.
Ashish PatelDepartment of Chest Surgery, Kaiser Permanente Northern California, Oakland, CA.
Lisa J HerrintonDivision 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

backgroundIn 2015, Kaiser Permanente Northern California implemented an intervention to improve follow-up for pulmonary findings on diagnostic chest computed tomography (CT). The intervention includes tagging CT reports with the prefix "#PUL" followed by a character (0-6 or X) to track specific findings. #PUL5, indicating "suspicious for malignancy," triggers automatic referral for multidisciplinary care review.

methodsAmong patients who obtained an index chest CT exam from August 2015 to July 2017 without an exam in the previous 2 years, we computed the frequency of lung cancer diagnosis within 120 days of CT in relation to each #PUL tag. For #PUL5, we computed sensitivity, specificity, positive and negative predictive values, and number needed to diagnose. We also performed a chart review to assess why some patients diagnosed with lung cancer were not tagged #PUL5.

resultsOf the 39,409 patients with a tagged CT report, 1105 (2.8%) had a new primary lung cancer diagnosis within 120 days. Among the 2255 patients tagged #PUL5, 821 were diagnosed with lung cancer, with a sensitivity of 74% (95% confidence interval, 72%-77%). The positive predictive value was 36% (35%-38%), number needed to diagnosis was 2.7 (2.6-2.9), and specificity and negative predictive values were > 95%. Chart review identified opportunities to improve system defaults and clarify concepts.

conclusionThe intervention performed well but needed improvement. Automating CT reports was simple and generalizable, and enabled reduction of care gaps and system improvement.

Indexed as

Lung NeoplasmsHumansLungSensitivity and SpecificityThoraxTomography, X-Ray Computed

Identifiers

PMID35348055
PMCPMC8817905
OpenAlexW4210316328

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.