Developing a Diagnostic Multivariable Prediction Model for Urinary Tract Cancer in Patients Referred with Haematuria: Results from the IDENTIFY Collaborative Study.

Humans Male Urologic Neoplasms / complications Urinary Bladder Neoplasms / complications Kidney Neoplasms

Bladder cancer Haematuria Prostate cancer Renal cancer Risk Calculator Risk factors Urinary tract cancer Urothelial cancer

Journal

European urology focus

ISSN: 2405-4569

Titre abrégé: Eur Urol Focus

Pays: Netherlands

ID NLM: 101665661

Informations de publication

Date de publication:
11 2022

Historique:

received: 23 03 2022

revised: 05 05 2022

accepted: 04 06 2022

pubmed: 28 6 2022

medline: 15 12 2022

entrez: 27 6 2022

Statut: ppublish

Résumé

Patient factors associated with urinary tract cancer can be used to risk stratify patients referred with haematuria, prioritising those with a higher risk of cancer for prompt investigation. To develop a prediction model for urinary tract cancer in patients referred with haematuria. A prospective observational study was conducted in 10 282 patients from 110 hospitals across 26 countries, aged ≥16 yr and referred to secondary care with haematuria. Patients with a known or previous urological malignancy were excluded. The primary outcomes were the presence or absence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC], and renal cancer). Mixed-effect multivariable logistic regression was performed with site and country as random effects and clinically important patient-level candidate predictors, chosen a priori, as fixed effects. Predictors were selected primarily using clinical reasoning, in addition to backward stepwise selection. Calibration and discrimination were calculated, and bootstrap validation was performed to calculate optimism. The unadjusted prevalence was 17.2% (n = 1763) for bladder cancer, 1.20% (n = 123) for UTUC, and 1.00% (n = 103) for renal cancer. The final model included predictors of increased risk (visible haematuria, age, smoking history, male sex, and family history) and reduced risk (previous haematuria investigations, urinary tract infection, dysuria/suprapubic pain, anticoagulation, catheter use, and previous pelvic radiotherapy). The area under the receiver operating characteristic curve of the final model was 0.86 (95% confidence interval 0.85-0.87). The model is limited to patients without previous urological malignancy. This cancer prediction model is the first to consider established and novel urinary tract cancer diagnostic markers. It can be used in secondary care for risk stratifying patients and aid the clinician's decision-making process in prioritising patients for investigation. We have developed a tool that uses a person's characteristics to determine the risk of cancer if that person develops blood in the urine (haematuria). This can be used to help prioritise patients for further investigation.

Sections du résumé

BACKGROUND

Patient factors associated with urinary tract cancer can be used to risk stratify patients referred with haematuria, prioritising those with a higher risk of cancer for prompt investigation.

OBJECTIVE

To develop a prediction model for urinary tract cancer in patients referred with haematuria.

DESIGN, SETTING, AND PARTICIPANTS

A prospective observational study was conducted in 10 282 patients from 110 hospitals across 26 countries, aged ≥16 yr and referred to secondary care with haematuria. Patients with a known or previous urological malignancy were excluded.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcomes were the presence or absence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC], and renal cancer). Mixed-effect multivariable logistic regression was performed with site and country as random effects and clinically important patient-level candidate predictors, chosen a priori, as fixed effects. Predictors were selected primarily using clinical reasoning, in addition to backward stepwise selection. Calibration and discrimination were calculated, and bootstrap validation was performed to calculate optimism.

RESULTS AND LIMITATIONS

The unadjusted prevalence was 17.2% (n = 1763) for bladder cancer, 1.20% (n = 123) for UTUC, and 1.00% (n = 103) for renal cancer. The final model included predictors of increased risk (visible haematuria, age, smoking history, male sex, and family history) and reduced risk (previous haematuria investigations, urinary tract infection, dysuria/suprapubic pain, anticoagulation, catheter use, and previous pelvic radiotherapy). The area under the receiver operating characteristic curve of the final model was 0.86 (95% confidence interval 0.85-0.87). The model is limited to patients without previous urological malignancy.

CONCLUSIONS

This cancer prediction model is the first to consider established and novel urinary tract cancer diagnostic markers. It can be used in secondary care for risk stratifying patients and aid the clinician's decision-making process in prioritising patients for investigation.

PATIENT SUMMARY

We have developed a tool that uses a person's characteristics to determine the risk of cancer if that person develops blood in the urine (haematuria). This can be used to help prioritise patients for further investigation.

Identifiants

DOI: 10.1016/j.euf.2022.06.001 PMID: 35760722

pubmed: 35760722

pii: S2405-4569(22)00129-8

doi: 10.1016/j.euf.2022.06.001

pii:

doi:

Types de publication

Observational Study Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

Pagination

1673-1682

Subventions

Organisme : Medical Research Council

ID : MR/S00680X/1

Pays : United Kingdom

Organisme : Department of Health

Pays : United Kingdom

Investigateurs

Aasem Chaudry (A)

Abhishek Sharma (A)

Adam Bennett (A)

Adnan Ahmad (A)

Ahmed Abroaf (A)

Ahmed Musa Suliman (AM)

Aimee Lloyd (A)

Alastair McKay (A)

Albert Wong (A)

Alberto Silva (A)

Alexandre Schneider (A)

Alison MacKay (A)

Allen Knight (A)

Alkiviadis Grigorakis (A)

Amar Bdesha (A)

Amy Nagle (A)

Ana Cebola (A)

Ananda Kumar Dhanasekaran (AK)

Andraž Kondža (A)

André Barcelos (A)

Andrea Benedetto Galosi (AB)

Andrea Ebur (A)

Andrea Minervini (A)

Andrew Russell (A)

Andrew Webb (A)

Ángel García de Jalón (ÁG)

Ankit Desai (A)

Anna Katarzyna Czech (AK)

Anna Mainwaring (A)

Anthony Adimonye (A)

Arighno Das (A)

Arnaldo Figueiredo (A)

Arnauld Villers (A)

Artur Leminski (A)

Arvinda Chippagiri (A)

Asim Ahmed Lal (AA)

Asıf Yıldırım (A)

Athanasios Marios Voulgaris (AM)

Audrey Uzan (A)

Aye Moh Moh Oo (AMM)

Ayman Younis (A)

Bachar Zelhof (B)

Bashir Mukhtar (B)

Ben Ayres (B)

Ben Challacombe (B)

Benedict Sherwood (B)

Benjamin Ristau (B)

Billy Lai (B)

Brechtje Nellensteijn (B)

Brielle Schreiter (B)

Carlo Trombetta (C)

Catherine Dowling (C)

Catherine Hobbs (C)

Cayo Augusto Estigarribia Benitez (CAE)

Cédric Lebacle (C)

Cherrie Wing Yin Ho (CWY)

Chi-Fai Ng (CF)

Chloe Mount (C)

Chon Meng Lam (CM)

Chris Blick (C)

Christian Brown (C)

Christopher Gallegos (C)

Claire Higgs (C)

Clíodhna Browne (C)

Conor McCann (C)

Cristina Plaza Alonso (C)

Daniel Beder (D)

Daniel Cohen (D)

Daniel Gordon (D)

Daniel Wilby (D)

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Elizabeth Day (E)

Elsayed Desouky (E)

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Emrah Yuruk (E)

Enes Kilic (E)

Eoin Dinneen (E)

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