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

Sinan Khadhouri* (Corresponding Author), Kevin M. Gallagher, Taimur T. Shah, Chuanyu Gao, Sacha Moore, Eleanor F. Zimmermann, Eric Edison, Matthew Jefferies, Arjun Nambiar, Thineskrishna Anbarasan, Miles P. Mannas, Taeweon Lee, Giancarlo Marra, Juan Gómez Rivas, Gautier Marcq, Mark A. Assmus, Taha Uçar, Francesco Claps, Matteo Boltri, Giuseppe La MontagnaTara Burnhope, Nkwam Nkwam, Tomas Austin, Nicholas E. Boxall, Alison P. Downey, Troy A. Sukhu, Marta Antón-Juanilla, Sonpreet Rai, Yew-Fung Chin, Madeline Moore, Tamsin Drake, James S.A. Green, Beatriz Goulao, Graeme MacLennan, Matthew Nielsen, John S. McGrath, Veeru Kasivisvanathan, Aasem Chaudry, Abhishek Sharma, Adam Bennett, Alison MacKay, Daniel Gordon, Jennifer Jones, Kevin Brown, Peter K Osei-Bonsu, Peter Smith, Ricky Ellis, Rosalyn Hawkins, Shahid Khan, The IDENTIFY Study Group, Kenneth MacKenzie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)1673-1682
Number of pages10
JournalEuropean Urology Focus
Volume8
Issue number6
Early online date25 Jun 2022
DOIs
Publication statusPublished - 1 Nov 2022

Bibliographical note

open access via Elsevier agreement
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.

Keywords

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

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