Evidence map›Paper›PMID 36357601›Full record

ArticleWorld journal of urology2022

Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer.

Rowan David, Mrunal Hiwase, Arman A Kahokehr, Jason Lee, David I Watson, John Leung, Michael E O'Callaghan

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of urology, 2022. 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
1.4field-weighted citation impact, top 18% 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, 7 citations in OpenAlex.

  1. Pooled it
  2. Enhancing Radiation Oncology Education Through Artificial Intelligence: A Review of Applications, Limitations, and Future Directions.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025
    Review
  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

7 authors at 1 institution in 1 country.

Rowan DavidCollege of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia. rowanvictordavid@gmail.com.ORCID http://orcid.org/0000-0003-4483-1204
Mrunal HiwaseCollege of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia.
Arman A KahokehrCollege of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia.ORCID http://orcid.org/0000-0003-2618-3266
Jason LeeCollege of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia.
David I WatsonCollege of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia.ORCID http://orcid.org/0000-0002-7683-2693
John LeungCollege of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia.ORCID http://orcid.org/0000-0003-1834-9131
Michael E O'CallaghanCollege of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia.ORCID http://orcid.org/0000-0001-5038-5859
Flinders University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe risk of treatment-related toxicity is important for patients with localised prostate cancer to consider when deciding between treatment options. We developed a model to predict hospitalisation for radiation-induced genitourinary toxicity based on patient characteristics.

methodsThe prospective South Australian Prostate Cancer Clinical Outcomes registry was used to identify men with localised prostate cancer who underwent curative intent external beam radiotherapy (EBRT) between 1998 and 2019. Multivariable Cox proportional regression was performed. Model discrimination, calibration, internal validation and utility were assessed using C-statistics and area under ROC, calibration plots, bootstrapping, and decision curve analysis, respectively.

resultsThere were 3,243 patients treated with EBRT included, of which 644 (20%) patients had a treated-related admission. In multivariable analysis, diabetes (HR 1.35, 95% CI 1.13-1.60, p < 0.001), smoking (HR 1.78, 95% CI 1.40-2.12, p < 0.001), and bladder outlet obstruction (BOO) without transurethral resection of prostate (TURP) (HR 7.49, 95% CI 6.18-9.08 p < 0.001) followed by BOO with TURP (HR 4.96, 95% CI 4.10-5.99 p < 0.001) were strong independent predictors of hospitalisation (censor-adjusted c-statistic = 0.80). The model was well-calibrated (AUC = 0.76). The global proportional hazards were met. In internal validation through bootstrapping, the model was reasonably discriminate at five (AUC 0.75) years after radiotherapy.

conclusionsThis is the first study to develop a predictive model for genitourinary toxicity requiring hospitalisation amongst men with prostate cancer treated with EBRT. Patients with localised prostate cancer and concurrent BOO may benefit from TURP before EBRT.

Indexed as

BrachytherapyProstatic NeoplasmsRadiation InjuriesTransurethral Resection of ProstateUrinary Bladder Neck ObstructionAustraliaHospitalsHumansMaleProspective StudiesDecision curve analysisExternal beam radiotherapyGenitourinary complicationsGenitourinary toxicityHospital admissionHospitalisationMachine learningProstate cancerRadiation therapyRadiotherapy

Identifiers

PMID36357601
PMCPMC9712379
OpenAlexW4308768654

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.