Evidence map›Paper›PMID 38450056›Full record

ArticleJTO clinical and research reports2024

The Benefits and Harms of Lung Cancer Screening in Individuals With Comorbidities.

Minal S Kale, Keith Sigel, Arushi Arora, Bart S Ferket, Juan Wisnivesky, Chung Yin Kong

Open access · goldAbstract read
In one paragraph

Article in JTO clinical and research reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

10 citing papers in PubMed, 9 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Impact of Comorbidities on the Mortality Benefits of Lung Cancer Screening: A Post-Hoc Analysis of the PLCO and NLST Trials.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2025
    Article
  9. Article
  10. 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

6 authors at 2 institutions in 1 country.

Minal S KaleDepartment of Medicine, Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Keith SigelDepartment of Medicine, Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Arushi AroraDepartment of Medicine, Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Bart S FerketInstitute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
Juan WisniveskyDepartment of Medicine, Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Chung Yin KongDepartment of Medicine, Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Icahn School of Medicine at Mount Sinai · USMount Sinai Health System · US

Funding

Comparative Modeling of Lung Cancer Prevention, Early Detection and Treatment InterventionsU01CA253858 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2020 to 2025
$8.4M
Investigating the Roles of Patient Beliefs, Stigma, and Physician Implicit Bias on Disparities in Lung Cancer ScreeningR01MD014890 · NIMHD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KALE, MINAL S, SMITH, CARDINALE B · 2021 to 2025
$4.1M
NCI NIH HHS U01 CA253858NIMHD NIH HHS R01 MD014890
6 · The paper itself

Abstract

Introduction: Individuals with a history of smoking and a high risk of lung cancer often have a high prevalence of smoking-related comorbidities. The presence of these comorbidities might alter the benefit-to-harm ratio of lung cancer screening by influencing the risk of complications, quality of life, and competing risks of death. Nevertheless, individuals with chronic diseases are underrepresented in screening clinical trials. In this study, we use microsimulation modeling to determine the impact of chronic diseases on lung cancer benefits and harms. Methods: We extended a validated lung cancer screening microsimulation model that comprehensively recapitulates an individual's lung cancer development, progression, detection, follow-up, treatment, and survival. We parameterized the model to reflect the impact of chronic diseases on complications from invasive testing, quality of life, and mortality in individuals in five-year age categories between the ages of 50 and 80 years. Outcomes included life-years (LY) gained per 100,000 in patients with chronic obstructive pulmonary disease, diabetes mellitus, heart disease, and history of stroke compared with screening-eligible individuals without comorbidities. Results: Among individuals between the ages of 50 and 54 years, we found that the presence of a comorbidity altered the LY gained from screening per 100,000 individuals depending on the comorbidity: 4296 LY with no comorbidities; 3462 LY, 3260 LY, 3031 LY, and 3257 LY with chronic obstructive pulmonary disease, heart disease, diabetes mellitus, and stroke, respectively. We observed greater reductions in LY gained in individuals with two comorbidities; we observed similar patterns for individuals between the ages of 55 and 59 years, 60 and 64 years, 65 and 69 years, 70 and 74 years, and 75 and 80 years. Conclusions: Comorbidities reduce LY gained from screening per 100,000 compared with no comorbidities, and our results can be used by clinicians when discussing the benefits and harms of screening in their patients with comorbidities.

Indexed as

ComorbidityDecision analytic modelLung cancer screeningMicrosimulation modeling

Identifiers

PMID38450056
PMCPMC10915410
OpenAlexW4390942336

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.