Sinan Khadhouri1, Kevin M Gallagher2, Kenneth R MacKenzie3, Taimur T Shah4, Chuanyu Gao5, Sacha Moore6, Eleanor F Zimmermann7, Eric Edison8, Matthew Jefferies9, Arjun Nambiar3, Thineskrishna Anbarasan10, Miles P Mannas11, Taeweon Lee11, Giancarlo Marra12, Juan Gómez Rivas13, Gautier Marcq14, Mark A Assmus15, Taha Uçar16, Francesco Claps17, Matteo Boltri17, Giuseppe La Montagna17, Tara Burnhope18, Nkwam Nkwam18, Tomas Austin19, Nicholas E Boxall20, Alison P Downey21, Troy A Sukhu22, Marta Antón-Juanilla23, Sonpreet Rai24, Yew-Fung Chin25, Madeline Moore18, Tamsin Drake26, James S A Green27, Beatriz Goulao28, Graeme MacLennan29, Matthew Nielsen22, John S McGrath30, Veeru Kasivisvanathan31. 1. Health Services Research Unit, University of Aberdeen, Aberdeen, UK; Aberdeen Royal Infirmary, Aberdeen, UK; British Urology Researchers in Surgical Training (BURST) Collaborative, UK. Electronic address: sinan.khadhouri@doctors.org.uk. 2. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Western General Hospital, Edinburgh, UK; Department of Clinical Surgery, University of Edinburgh, Edinburgh, UK. 3. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Freeman Hospital, Newcastle Upon Tyne, UK. 4. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Department of Surgery and Cancer, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK; Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK. 5. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Addenbrookes Hospital, Cambridge, UK. 6. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Wrexham Maelor Hospital, Wrexham, UK. 7. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Torbay and South Devon NHS Foundation Trust, Torbay, UK. 8. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Department of Urology, Whipps Cross Hospital, Barts Health NHS Trust, London, UK. 9. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Morriston Hospital, Swansea, UK; Swansea University, Swansea, UK. 10. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Western General Hospital, Edinburgh, UK. 11. Department of Urologic Sciences, University of British Columbia, Vancouver, Canada. 12. Department of Surgical Sciences, Città della Salute e della Scienza, Turin, Italy; University of Turin, Turin, Italy. 13. Department of Urology, La Paz University Hospital, Madrid, Spain. 14. Urology Department, Claude Huriez Hospital, CHU Lille, Lille, France; CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277 - CANTHER - Cancer Heterogeneity Plasticity and Resistance to Therapies, University Lille, Lille, France. 15. Division of Urology, Department of Surgery, University of Alberta, Edmonton, Alberta, Canada. 16. Department of Urology, Istanbul Medeniyet University, Istanbul, Turkey. 17. Urological Clinic, Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy. 18. University Hospitals of Derby and Burton NHS Foundation Trust, Derby, UK. 19. Department of Urology, Queen Alexandra Hospital, Portsmouth, UK. 20. Salford Royal NHS Foundation Trust, Salford, UK. 21. Doncaster Royal Infirmary, Doncaster, UK. 22. University of North Carolina Hospitals, Chapel Hill, NC, USA. 23. Department of Urology, Hospital Universitario Cruces, Barakaldo, Spain. 24. St James University Hospital, Leeds Teaching Hospital NHS Trust, Leeds, UK. 25. Aberdeen Royal Infirmary, Aberdeen, UK. 26. The Royal Bournemouth Hospital, Bournemouth, UK. 27. Department of Urology, Whipps Cross Hospital, Barts Health NHS Trust, London, UK; Healthcare and Population Research, Kings College, London, UK. 28. Health Services Research Unit, University of Aberdeen, Aberdeen, UK. 29. Centre for Healthcare Randomised Trials, University of Aberdeen, Aberdeen, UK. 30. University of Exeter Medical School, Exeter, UK; Royal Devon and Exeter NHS Foundation Trust, Exeter, UK. 31. British Urology Researchers in Surgical Training (BURST) Collaborative, UK; Division of Surgery and Interventional Science, University College London, London, UK; Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK.
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.
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.