To identify risk predictive models for bladder-specific cancer mortality in patients undergoing radical cystectomy and assess their clinical utility and risk of bias.
Systematic review (CRD42021224626:PROSPERO) in Medline and EMBASE (from their creation until 31/10/2021), were screened to include articles focused on the development and internal validation of a predictive model of specific-cancer mortality in patients undergoing radical cystectomy. CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST) were applied.
Nineteen observational studies were included. The main predictors were sociodemographic variables, such as age (18 studies, 94.7%) and sex (17, 89.5% studies), tumor characteristics (TNM stage (18 studies, 94.7%), histological subtype/grade (15 studies, 78.9%), lymphovascular invasion (10 studies, 52.6%) and treatment with chemotherapy (13 studies, 68.4%). C-index values was presented in 14 studies. The overall risk of bias assessed using PROBAST led to 100% studies being classified as high risk (the analysis domain was rated to be at high risk of bias in all the studies) and 52.6% showed low applicability. Only 5 studies (26.3%) included an external validation and 2 (10.5%) included a prospective study design.
Using clinical predictors to assess the risk of bladder-specific cancer mortality is a feasibility alternative. However, the studies showed a high risk of bias and their applicability is uncertain. Studies should improve the conducting and reporting, and subsequent external validation studies should be developed.
European journal of clinical investigation. 2022 May 31 [Epub ahead of print]
Pau Sarrió Sanz, Laura Martinez Cayuelas, Blanca Lumberas, Laura Sánchez Caballero, Antonio Palazón Bru, Vicente Francisco Gil Guillén, Luis Gómez Pérez
Urology Services, University Hospital of San Juan de Alicante, Alicante, Spain., Department of Public Health, History of Science and Gynecology, Miguel Hernández University, and CIBER en Epidemiología y Salud Pública, Spain., Department of Clinical Medicine, Miguel Hernández University, Alicante, Spain.