Evidence map›Paper›PMID 36645151›Full record

Observational studyCancer medicine2023

Treatment patterns and survival of low and intermediate-risk prostate cancer in end-stage kidney disease: A retrospective population cohort study.

Nagaraju Sarabu, Weichuan Dong, Al W Ray, Austin Fernstrum, Megan Prunty, Lee E Ponsky, Jonathan E Shoag, Vahakn B Shahinian, Krista L Lentine, Siran M Koroukian

Open access · goldAbstract readObservational Study
In one paragraph

Observational study 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, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 23% 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, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Observational
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

10 authors at 4 institutions in 1 country.

Nagaraju SarabuDivision of Nephrology, University Hospitals Cleveland Medical Center, Ohio, Cleveland, United States.ORCID 0000-0001-6084-8677
Weichuan DongPopulation and Quantitative Health Sciences, Population Cancer Analytics Shared Resource, and the Case Comprehensive Cancer Center, Case Western Reserve University, Ohio, Cleveland, United States.ORCID 0000-0002-3981-658X
Al W RayDepartment of Urology, University Hospitals Cleveland Medical Center, Ohio, Cleveland, United States.
Austin FernstrumDepartment of Urology, University Hospitals Cleveland Medical Center, Ohio, Cleveland, United States.
Megan PruntyDepartment of Urology, University Hospitals Cleveland Medical Center, Ohio, Cleveland, United States.ORCID 0000-0002-9150-6706
Lee E PonskyDepartment of Urology, University Hospitals Cleveland Medical Center, Ohio, Cleveland, United States.
Jonathan E ShoagDepartment of Urology, University Hospitals Cleveland Medical Center, Ohio, Cleveland, United States.
Vahakn B ShahinianDivision of Nephrology, Department of Medicine, University of Michigan, Michigan, Ann Arbor, United States.
Krista L LentineCenter for Abdominal Transplantation, Saint Louis University School of Medicine, Missouri, St. Louis, United States.
Siran M KoroukianPopulation and Quantitative Health Sciences, Population Cancer Analytics Shared Resource, and the Case Comprehensive Cancer Center, Case Western Reserve University, Ohio, Cleveland, United States.ORCID 0000-0002-8106-9581
University Hospitals of Cleveland · USCase Western Reserve University · USSaint Louis University · USUniversity of Michigan · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn accordance with guidelines, observation with or without active surveillance for low-risk prostate cancer increased in recent years in the general population. We compared treatment patterns and mortality for low- and intermediate-risk prostate cancer and mortality rates among end-stage kidney disease (ESKD) and non-ESKD patients.

methodsThis is a retrospective population-based observational cohort study of Surveillance, Epidemiology, and End Results-Medicare data of men aged 66 years and older with localized prostate cancer (2004-2015). ESKD status was determined using Medicare billing codes. Multivariable logistic regression models and Cox-proportional hazards models were used to study definitive treatment patterns and mortality, respectively.

resultsFor low-risk prostate cancer, dialysis patients (N = 83) had lower but not statistically significant odds (OR, 0.74; 95% CI: 0.48-1.16) of receiving definitive treatment than non-ESKD patients (N = 24,935). For those with intermediate-risk prostate cancer, dialysis patients (N = 254) had lower odds to receive definitive treatment (OR, 0.54; 95% CI: 0.42-0.72) than non-ESKD patients (N = 60,883). From 2004-2010 to 2011-2015, for patients with low-risk prostate cancer, while the receipt of definitive treatment for non-ESKD patients trended down from 72% to 48%, it trended up for dialysis patients from 55% to 65%. Kidney transplant patients (N = 33 for low-risk and N = 91 for intermediate-risk) had lower rates of definitive treatment for low-risk and similar rates of treatment for intermediate-risk prostate cancer compared to non-ESKD patients.

conclusionsThe disparity in definitive treatment rates for low-risk prostate cancer among dialysis patients exists despite their high mortality, compared to the general population.

Indexed as

Kidney Failure, ChronicProstatic NeoplasmsAgedCohort StudiesHumansMaleMedicareRetrospective StudiesUnited Statesdisparityend-stage kidney diseasekidney transplantprostate cancershared decision makingtreatment

Identifiers

PMID36645151
PMCPMC10134264
OpenAlexW4316469612

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.