Evidence map›Paper›PMID 33284845›Full record

ArticlePloS one2020

Predicting the 10-year risk of death from other causes in men with localized prostate cancer using patient-reported factors: Development of a tool.

Daniel M Frendl, Gordon FitzGerald, Mara M Epstein, Jeroan J Allison, Mitchell H Sokoloff, John E Ware

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

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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

10 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Prostate cancer among Saudis: a registry review.Annals of medicine and surgery (2012) · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Daniel M FrendlDepartment of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.ORCID 0000-0002-3571-6610
Gordon FitzGeraldDepartment of Surgery Center for Outcomes Research, University of Massachusetts Medical School, Worcester, MA, United States of America.
Mara M EpsteinDepartment of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States of America.
Jeroan J AllisonDepartment of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States of America.
Mitchell H SokoloffDepartment of Urology, University of Massachusetts Medical School, Worcester, MA, United States of America.
John E WareDepartment of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States of America.

Funding

Functional Health Predictors of Other Cause Mortality Risk in Prostate CancerF30AG047772 · NIA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI FRENDL, DANIEL MARK · 2014 to 2015
$54k
NIA NIH HHS F30 AG047772
6 · The paper itself

Abstract

objectiveTo develop a tool for estimating the 10-year risk of death from other causes in men with localized prostate cancer. SUBJECTS AND

methodsWe identified 2,425 patients from the Surveillance Epidemiology and End Results-Medicare Health Outcomes Survey database, age <80, newly diagnosed with clinical stage T1-T3a prostate cancer from 1/1/1998-12/31/2009, with follow-up through 2/28/2013. We developed a Fine and Gray competing-risks model for 10-year other cause mortality considering age, patient-reported comorbid medical conditions, component scores and items of the SF-36 Health Survey, activities of daily living, and sociodemographic characteristics. Model discrimination and calibration were compared to predictions from Social Security life table mortality risk estimates.

resultsOver a median follow-up of 7.7 years, 76 men died of prostate-specific causes and 465 died of other causes. The strongest predictors of 10-year other cause mortality risk included increasing age at diagnosis, higher approximated Charlson Comorbidity Index score, worse patient-reported general health (fair or poor vs. excellent-good), smoking at diagnosis, and marital status (all other vs. married) (all p<0.05). Model discrimination improved over Social Security life tables (c-index of 0.70 vs. 0.59, respectively). Predictions were more accurate than predictions from the Social Security life tables, which overestimated risk in our population.

conclusionsWe provide a tool for estimating the 10-year risk of dying from other causes when making decisions about treating prostate cancer using pre-treatment patient-reported characteristics.

Indexed as

Cause of DeathModels, StatisticalAgedAged, 80 and overAge FactorsComorbidityFollow-Up StudiesHealth SurveysHumansLife TablesMaleMarital StatusNeoplasm StagingProstatic NeoplasmsRisk AssessmentRisk Factors

Identifiers

PMID33284845
PMCPMC7721137

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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.