TY - JOUR
T1 - Developing a Diagnostic Multivariable Prediction Model for Urinary Tract Cancer in Patients Referred with Haematuria
T2 - Results from the IDENTIFY Collaborative Study
AU - IDENTIFY Study group
AU - Khadhouri, Sinan
AU - Gallagher, Kevin M.
AU - MacKenzie, Kenneth R.
AU - Shah, Taimur T.
AU - Gao, Chuanyu
AU - Moore, Sacha
AU - Zimmermann, Eleanor F.
AU - Edison, Eric
AU - Jefferies, Matthew
AU - Nambiar, Arjun
AU - Anbarasan, Thineskrishna
AU - Mannas, Miles P.
AU - Lee, Taeweon
AU - Marra, Giancarlo
AU - Gómez Rivas, Juan
AU - Marcq, Gautier
AU - Assmus, Mark A.
AU - Uçar, Taha
AU - Claps, Francesco
AU - Boltri, Matteo
AU - La Montagna, Giuseppe
AU - Burnhope, Tara
AU - Nkwam, Nkwam
AU - Austin, Tomas
AU - Boxall, Nicholas E.
AU - Downey, Alison P.
AU - Sukhu, Troy A.
AU - Antón-Juanilla, Marta
AU - Rai, Sonpreet
AU - Chin, Yew Fung
AU - Moore, Madeline
AU - Drake, Tamsin
AU - Green, James S.A.
AU - Goulao, Beatriz
AU - MacLennan, Graeme
AU - Nielsen, Matthew
AU - McGrath, John S.
AU - Kasivisvanathan, Veeru
AU - Chaudry, Aasem
AU - Sharma, Abhishek
AU - Bennett, Adam
AU - Ahmad, Adnan
AU - Abroaf, Ahmed
AU - Suliman, Ahmed Musa
AU - Lloyd, Aimee
AU - McKay, Alastair
AU - Wong, Albert
AU - Silva, Alberto
AU - Schneider, Alexandre
AU - Meeks, Joshua
N1 - Funding Information:
Funding/Support and role of the sponsor: Grants from Action Bladder Cancer UK, The Urology Foundation, The Rosetrees Trust were used for costs of statistical analysis and dissemination of results at international meetings and conferences. There were no endorsements from pharmaceutical companies or agencies to write this article. Veeru Kasivisvanathan is an Academic Clinical Lecturer funded by the United Kingdom National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, NIHR or the Department of Health and Social Care.
Funding Information:
Acknowledgments: We would like to thank all the BURST research collaborators for taking part in this study, Max Peters for his support and advice regarding the methods and Jonathan Deeks for his support from the Test Evaluation Research Group. Though unrelated to this study, the BURST Research Collaborative would like to acknowledge funding from the BJU International, the British Association of Urological Surgeons, Ferring Pharmaceuticals Ltd, and Dominvs Group.
Publisher Copyright:
© 2022 The Authors
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - Bladder cancer
KW - Haematuria
KW - Prostate cancer
KW - Renal cancer
KW - Risk Calculator
KW - Risk factors
KW - Urinary tract cancer
KW - Urothelial cancer
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UR - http://www.scopus.com/inward/citedby.url?scp=85133294506&partnerID=8YFLogxK
U2 - 10.1016/j.euf.2022.06.001
DO - 10.1016/j.euf.2022.06.001
M3 - Article
C2 - 35760722
AN - SCOPUS:85133294506
SN - 2405-4569
VL - 8
SP - 1673
EP - 1682
JO - European Urology Focus
JF - European Urology Focus
IS - 6
ER -
