Evidence map›Paper›PMID 35674139›Full record

ArticleCancer medicine2023

Survival of cancer survivors with a new pancreatic cancer diagnosis.

Sandi L Pruitt, Anna Tavakkoli, Hong Zhu, Daniel F Heitjan, David E Gerber, Amit G Singal, Ethan A Halm, Muhammad Shaalan Beg, Bhumika Maddineni, Ankit J Kansagra and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

11 authors at 5 institutions in 2 countries.

Sandi L PruittDepartment of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID 0000-0002-1007-9176
Anna TavakkoliDepartment of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID 0000-0001-9921-9085
Hong ZhuDepartment of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Daniel F HeitjanDepartment of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
David E GerberDepartment of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Amit G SingalDepartment of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID 0000-0002-1172-3971
Ethan A HalmRutgers Biological Health Sciences (RBHS), Rutgers University, New Brunswick, New Jersey, USA.
Muhammad Shaalan BegHarold C. Simmons Comprehensive Cancer Center, Dallas, Texas, USA.
Bhumika MaddineniDepartment of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Ankit J KansagraHarold C. Simmons Comprehensive Cancer Center, Dallas, Texas, USA.
Caitlin C MurphySchool of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Texas Oncology · USRutgers, The State University of New Jersey · USSouthern Methodist University · USThe University of Texas Health Science Center at Houston · USThe University of Texas Southwestern Medical Center · US

Funding

Revisiting Prior Cancer as an Exclusion Criterion for Cancer Clinical TrialsR01CA229834 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI PRUITT, SANDI LEIGH · 2018 to 2023
$2.9M
Development of New Cancer Treatments and InvestigatorsK24CA201543 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI GERBER, DAVID ERIC · 2016 to 2020
$737k
NCI NIH HHS K24CA201543NCI NIH HHS R01 CA229834NCI NIH HHS R01CA229834
6 · The paper itself

Abstract

backgroundPersons newly diagnosed with pancreas cancer and who have survived a previous cancer are often excluded from clinical trials, despite limited evidence about their prognosis. We examined the association between previous cancer and overall survival.

methodsThis US population-based cohort study included older adults (aged ≥66 years) diagnosed with pancreas cancer between 2005 and 2015 in the linked Surveillance, Epidemiology, and End Results-Medicare data. We used Cox proportional hazards models to estimate stage-specific effects of previous cancer on overall survival, adjusting for sociodemographic, treatment, and tumor characteristics.

resultsOf 32,783 patients, 18.7% were previously diagnosed with another cancer. The most common previous cancers included prostate (29.0%), breast (18.9%), or colorectal (9.7%) cancer. More than half of previous cancers (53.9%) were diagnosed 5 or more years prior to pancreas cancer diagnosis or at an in situ or localized stage (47.8%). The proportions of patients surviving 1, 3, and 5 years after pancreas cancer were nearly identical for those with and without previous cancer. Median survival in months was as follows for those with and without previous cancer respectively: 7 versus 8 (Stage 0/I), 10 versus 10 (Stage II), 7 versus 7 (Stage III), and 3 versus 2 (Stage IV). Cox models indicated that patients with previous cancer had very similar or statistically equivalent survival to those with no previous cancer.

conclusionsGiven nearly equivalent survival compared to those without previous cancer, cancer survivors newly diagnosed with pancreas cancer should be considered for inclusion in pancreas cancer clinical trials.

Indexed as

Cancer SurvivorsPancreatic NeoplasmsAgedCohort StudiesHumansMaleMedicareNeoplasm StagingProportional Hazards ModelsSEER ProgramUnited Statesclinical trialsmultiple malignanciespancreas cancersurvival

Identifiers

PMID35674139
PMCPMC9844592
OpenAlexW4281711826

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.