Developing a Diagnostic Multivariable Prediction Model for Urinary Tract Cancer in Patients Referred with Haematuria: Results from the IDENTIFY Collaborative Study
Sinan Khadhouri, Kevin Gallagher, Kenneth R. MacKenzie, Taimur T. Shah, Chuanyu Gao, Sacha Moore, Eleanor Zimmermann, Eric Edison, Matthew Jefferies, Arjun Nambiar, Thineskrishna Anbarasan, Miles Mannas, Taeweon Lee, Giancarlo Marra, Juan Gómez Rivas, Gautier Marcq, Mark Assmus, Taha Uçar, Francesco Claps, M. Boltri, Giuseppe La Montagna, Tara Burnhope, Nkwam Nkwam, Tomas Austin, Nicholas E. Boxall, Alison Downey, Troy A. Sukhu, M. Antón-Juanilla, Sonpreet Rai, Yew-Fung Chin, Madeline Moore, Tamsin Drake, James Green, Beatriz Goulão, Graeme MacLennan, Matthew E. Nielsen, John McGrath, Veeru Kasivisvanathan, Aasem Chaudry, A. S. Sharma, Adam Bennett, Adnan Ahmad, Ahmed Abroaf, Ahmed Musa Suliman, Aimee Lloyd, Alastair McKay, Albert J. Wong, José Alberto Silva, Alexandre Schneider, Alison MacKay, Allen Knight, Alkiviadis Grigorakis, Amar Bdesha, Amy Nagle, Ana Cebola, Ananda Kumar Dhanasekaran, Andraž Kondža, André Barcelos, Andrea Benedetto Galosi, Andrea Ebur, Andrea Minervini, Andrew Russell, Andrew Webb, Ángel García de Jalón, Ankit A. Desai, Anna Czech, Anna Mainwaring, Anthony Adimonye, Arighno Das, Arnaldo Figueiredo, Arnauld Villers, Artur Lemiński, Arvinda Chippagiri, Asim Ahmed Lal, Asıf Yıldırım, Athanasios Marios Voulgaris, Audrey Uzan, Aye Moh Moh Oo, Ayman Younis, Bachar Zelhof, Bashir Mukhtar, Benjamin Ayres, Ben Challacombe, Benedict T. Sherwood, Benjamin T. Ristau, Billy Lai, Brechtje Nellensteijn, Brielle Schreiter, Carlo Trombetta, Catherine Dowling, Catherine Hobbs, Cayo Augusto Estigarribia Benítez, C. Lebâcle, Cherrie Wing Yin Ho, Chi‐Fai Ng, Chloe Mount, Chon Meng Lam, Chris Blick, C. T. A. Brown, Christopher Gallegos
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.