Evidence map›Paper›PMID 35545892›Full record

ArticleCancer medicine2022

Spirometry at diagnosis and overall survival in non-small cell lung cancer patients.

Ting Zhai, Yi Li, Robert Brown, Michael Lanuti, Justin F Gainor, David C Christiani

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Survivorship Challenges and Supportive Care in Lung Cancer.Seminars in respiratory and critical care medicine · 2025
    Review
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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.

Ting ZhaiDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0002-8763-1387
Yi LiDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.
Robert BrownPulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Michael LanutiDivision of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Justin F GainorMassachusetts General Hospital Cancer Center and Department of Hematology & Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
David C ChristianiDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Harvard University · USUniversity of Michigan · US

Funding

The Boston Lung Cancer Survival CohortU01CA209414 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI David C Christiani · 2017 to 2026
$12.2M
New Statistical Methods for Modelling Cancer OutcomesR01CA249096 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yi Li · 2021 to 2026
$2.5M
NCI NIH HHS 5U01CA209414NCI NIH HHS R01CA249096
6 · The paper itself

Abstract

Pulmonary function can predict all-cause mortality, and chronic obstructive pulmonary disease (COPD) is associated with worse overall survival (OS) in non-small cell lung cancer (NSCLC) patients. Though pre-operative lung function is predictive of in-hospital mortality following lung cancer surgery, its predictive utility for long-term survival is unclear. The prognostic role of commonly used spirometry tests in survival of lung cancer also remains uncertain. This study evaluates the role of spirometry at lung cancer diagnosis in predicting OS of NSCLC patients. This was a retrospective study using data from the Boston Lung Cancer Study on newly diagnosed NSCLC patients with spirometry tests performed before cancer therapy (n = 2805). Spirometric test values, after being categorized using quartiles, were analyzed for association with OS using univariate and risk-adjusted multiple regression models. Further, we analyzed OS by the status of COPD determined by spirometry, and, among those with COPD, by its stage defined by the Global Initiative for Chronic Obstructive Lung Disease criteria. Both univariate and multiple regression models demonstrated that lower quartiles of actual and percent predicted forced expiratory volume in 1 second and forced vital capacity at lung cancer diagnosis were significantly associated with worse OS. Spirometry-determined COPD, and more advanced stage of COPD at lung cancer diagnosis were associated with worse lung cancer OS. The findings provide evidence that a good pulmonary function at diagnosis may help improve OS in NSCLC patients.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsPulmonary Disease, Chronic ObstructiveHumansLungRetrospective StudiesSpirometryepidemiologylung cancerprognostic factorsurvival

Identifiers

PMID35545892
PMCPMC9761088
OpenAlexW4280595518

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.