Evidence map›Paper›PMID 35317320›Full record

ArticleWorld journal of gastrointestinal oncology2022

Chemotherapy predictors and a time-dependent chemotherapy effect in metastatic esophageal cancer.

Lauren Midthun, Sungjin Kim, Andrew Hendifar, Arsen Osipov, Samuel J Klempner, Joseph Chao, May Cho, Michelle Guan, Veronica R Placencio-Hickok, Alexandra Gangi and 6 more

Open access · diamondAbstract read
In one paragraph

Article in World journal of gastrointestinal oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

16 authors at 1 institution in 1 country.

Lauren MidthunDepartment of Medicine, Division of Hematology and Oncology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Sungjin KimBiostatistics and Bioinformatics Research Center, Cedars Sinai Medical Center, Los Angeles, CA 90048, United States.
Andrew HendifarSamuel Oschin Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Arsen OsipovDepartment of Medicine, Division of Hematology and Oncology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Samuel J KlempnerDepartment of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Brigham and Women's Hospital/Harvard Medical School, Boston, MA 02114, United States.
Joseph ChaoDepartment of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, United States.
May ChoDivision of Hematology and Oncology, Department of Medicine, University of California, Irvine, CA 92697, United States.
Michelle GuanDepartment of Medicine, Division of Hematology and Oncology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Veronica R Placencio-HickokDepartment of Medicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, United States.
Alexandra GangiDepartment of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Miguel BurchDepartment of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
De-Chen LinDepartment of Medicine, Division of Hematology and Oncology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Kevin WatersDepartment of Pathology and Laboratory Medicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, United States.
Katelyn AtkinsDepartment of Radiation Oncology, Cedars Sinai Medical Center, Los Angeles, CA 90048, United States.
Mitchell KamravaDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Jun GongDepartment of Medicine, Division of Hematology and Oncology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States. jun.gong@cshs.org.
Cedars-Sinai Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChemotherapy has long been shown to confer a survival benefit in patients with metastatic esophageal cancer. However, not all patients with metastatic disease receive chemotherapy.

aimTo evaluate a large cancer database of metastatic esophageal cancer cases to identify predictors of receipt to chemotherapy and survival.

methodsWe interrogated the National Cancer Database (NCDB) between 2004-2015 and included patients with M1 disease who had received or did not receive chemotherapy. A logistic regression model was used to examine the associations between chemotherapy and potential confounders and a Cox proportional hazards model was employed to examine the effect of chemotherapy on overall survival (OS). Propensity score analyses were further performed to balance measurable confounders between patients treated with and without chemotherapy.

resultsA total of 29182 patients met criteria for inclusion in this analysis, with 21911 (75%) receiving chemotherapy and 7271 (25%) not receiving chemotherapy. The median follow-up was 69.45 mo. The median OS for patients receiving chemotherapy was 9.53 mo (9.33-9.72)

conclusionIn this large, retrospective NCDB analysis, we identified several socioeconomic and clinicopathologic predictors for receiving chemotherapy and OS in patients with metastatic esophageal cancer. The benefit of chemotherapy on OS is time-dependent and favors early initiation. Focused outreach in lower income and underinsured patients is critical as receipt of chemotherapy is associated with improved OS.

Indexed as

ChemotherapyEsophageal cancerMetastaticPredictorsSurvival

Identifiers

PMID35317320
PMCPMC8919005
OpenAlexW4210912079

What OpenQuestion holds

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LicenceCC BY-NC
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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.