Muscle invasive bladder cancer and radical cystectomy: a risk predictive model

• Type of Material: Article
• Authors: Mohamad A Tfaily, Emory University; Hani Tamim, American University of Beirut Medical Center; Albert E Hajj, American University of Beirut Medical CenterAmerican University of Beirut Medical Center; Deborah Mukherji, American University of Beirut Medical Center
• Language: English
• Date: 2022-10-18
• Publisher: Cancer Intelligence
• Publication Version: Final Published Version
• Copyright Statement: © the authors; licensee ecancermedicalscience.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3332/ecancer.2022.1456
• Title of Journal or Parent Work: Ecancermedicalscience
• Volume: 16
• Grant/Funding Information: Research reported in this publication was supported by the Fogarty International Center and Office of Dietary Supplements of the National Institutes of Health under Award Number D43 TW009118. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
• Keywords: trimodal therapy; risk calculator; radical cystectomy; muscle invasive bladder cancer
• Research Categories: Health Sciences, Medicine and Surgery; Health Sciences, Oncology

Abstract

Background Radical cystectomy (RC) for muscle invasive bladder cancer (MIBC) remains the historical gold standard for treatment despite significant perioperative morbidity and subsequent quality of life concerns. Trimodal therapy (TMT) is gaining acceptance as an alternative bladder preserving approach. We aim to identify patients for whom TMT may be the optimal approach by constructing risk calculators of morbidity and mortality associated with RC. Methods Using the American College of Surgeons National Surgical Quality Improvement Program database, we selected patients diagnosed with MIBC undergoing RC, with a total of 10,642 patients identified. The primary outcome was mortality and secondary outcome was morbidity within 30 days of the procedure. We conducted multivariate logistic regression to obtain the best fit model for each outcome on 70% of the sample. Validation of the models was then performed on the remaining 30% of the sample. Model performance was assessed using discrimination and calibration abilities and a risk calculator was constructed for pre-operative counselling. Results Of the full cohort, 199 patients (1.9%) died and 2,328 patients (21.9%) experienced morbidity. Variables selected for the model predicting mortality included age, frailty, the American Society of Anesthesiologists status and preoperative creatinine. For the mortality model, the area under the curve was 72% with a Hosmer–Lemeshow statistic of 0.722. For the morbidity model, the area under the curve was 60% with a Hosmer–Lemeshow statistic of 0.287. Variables significant in the model included continent diversion, smoking and frailty. Conclusion We have constructed statistically significant and clinically relevant models using readily available health indicators to be used in multi-disciplinary discussion to provide high-risk patients with individualised risks of morbidity and mortality from RC, allowing for counselling for alternative treatments such as TMT.