Evidence map›Paper›PMID 34844922›Full record

SynthesisThe British journal of general practice : the journal of the Royal College of General Practitioners2022

Risk prediction models for symptomatic patients with bladder and kidney cancer: a systematic review.

Hannah Harrison, Juliet A Usher-Smith, Lanxin Li, Lydia Roberts, Zhiyuan Lin, Rachel E Thompson, Sabrina H Rossi, Grant D Stewart, Fiona M Walter, Simon Griffin and 1 more

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

Synthesis in The British journal of general practice : the journal of the Royal College of General Practitioners, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

7 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
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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 3 institutions in 2 countries.

Hannah HarrisonThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge.
Juliet A Usher-SmithThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge.
Lanxin LiUniversity of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge.
Lydia RobertsUniversity of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge.
Zhiyuan LinUniversity of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge.
Rachel E ThompsonUniversity of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge.
Sabrina H RossiDepartment of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge.
Grant D StewartDepartment of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge.
Fiona M WalterThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, and director, Wolfson Institute of Population Health, Queen Mary University of London, London.
Simon GriffinThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge.
Yin ZhouThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge.
University of Cambridge · GBCambridge School · PTAddenbrooke's Hospital · GB

Funding

Cancer Research UK 21464Cancer Research UK C55650/A21464Cancer Research UK C9685/A25177Cancer Research UK CC8640/A23385Medical Research Council MC_UU_00006/6Wellcome Trust 20391/Z/16/Z
6 · The paper itself

Abstract

backgroundTimely diagnosis of bladder and kidney cancer is key to improving clinical outcomes. Given the challenges of early diagnosis, models incorporating clinical symptoms and signs may be helpful to primary care clinicians when triaging at-risk patients.

aimTo identify and compare published models that use clinical signs and symptoms to predict the risk of undiagnosed prevalent bladder or kidney cancer. DESIGN AND

settingSystematic review.

methodA search identified primary research reporting or validating models predicting the risk of bladder or kidney cancer in MEDLINE and EMBASE. After screening identified studies for inclusion, data were extracted onto a standardised form. The risk models were classified using TRIPOD guidelines and evaluated using the PROBAST assessment tool.

resultsThe search identified 20 661 articles. Twenty studies (29 models) were identified through screening. All the models included haematuria (visible, non-visible, or unspecified), and seven included additional signs and symptoms (such as abdominal pain). The models combined clinical features with other factors (including demographic factors and urinary biomarkers) to predict the risk of undiagnosed prevalent cancer. Several models (

conclusionModels were identified that could be used in primary care to guide referrals, with potential to identify lower-risk patients with visible haematuria and to stratify individuals who present with non-visible haematuria. However, before application in general practice, external validations in appropriate populations are required.

Indexed as

Kidney NeoplasmsUrinary BladderBiasBiomarkersHematuriaHumansBiomarkersbladder cancerearly diagnosiskidney cancerrisk predictionsystematic review

Identifiers

PMID34844922
PMCPMC8714528
OpenAlexW3204875605

What OpenQuestion holds

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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.