Evidence map›Paper›PMID 29489642›Full record

Observational studyMedicine2018

Impact of heavy smoking on the benefits from first-line EGFR-TKI therapy in patients with advanced lung adenocarcinoma.

Ping Zhang, Xin Nie, Zhixin Bie, Lin Li

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 19% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
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  5. Review
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  7. 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

4 authors at 1 institution in 1 country.

Ping ZhangDepartment of Oncology Department of Minimally Invasive Tumor Therapies Center, Beijing Hospital, National Center of Gerontology, Beijing, China.
Xin Nie
Zhixin Bie
Lin Li
Beijing Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Smoking is a risk factor for nonsmall cell lung carcinoma (NSCLC) and is associated with a lower response to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI). The purpose of this study is to examine the impact of the smoking status on the benefits from first-line EGFR-TKI in NSCLC patients with EGFR mutation.This was a retrospective study of 159 patients with advanced NSCLC treated at the Beijing Hospital between January 2011 and December 2016. The follow-up was censored on December 2017. EGFR mutation status, smoking (nonsmoker vs <30 packs/year (light smoker) vs ≥30 packs/year (heavy smoker)), treatment, treatment response, and progression-free survival (PFS) were collected from the charts.Median follow-up was 10.0 (1.0-36.6) months. Response rate was lower in heavy smokers compared with nonheavy smokers (19.0% vs 71.7%, P < .001). There was no difference in PFS between nonsmokers (median, 10.5 months) and light smoker (median, 11.0 months), and these 2 groups were pooled together. PFS was longer in nonheavy smokers compared with heavy-smokers (median, 10.7 vs 6.0 months, P < .001). Smoking ≥ 30 packs/year (HR = 2.48, 95% CI: 1.55-3.98, P < .001) was associated with PFS.In patients with advanced NSCLC, the benefits and PFS of EGFR-TKI were better for nonheavy smokers than for heavy smokers.

Indexed as

AdultAgedCarcinoma, Non-Small-Cell LungErbB ReceptorsFemaleHumansLung NeoplasmsMaleMiddle AgedMutationProtein Kinase InhibitorsRetrospective StudiesRisk FactorsSmokingErbB ReceptorsProtein Kinase Inhibitors

Identifiers

PMID29489642
PMCPMC5851738
OpenAlexW2794203499

What OpenQuestion holds

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LicenceCC BY-ND
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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.