Riku Klén1,2, Antti P Salminen3, Mehrad Mahmoudian1,4, Kari T Syvänen3, Laura L Elo1, Peter J Boström3. 1. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland. 2. Turku PET Centre, University of Turku, Turku, Finland. 3. Department of Urology, Turku University Hospital and University of Turku, Turku, Finland. 4. Department of Future Technologies, University of Turku, Turku, Finland.
Abstract
Purpose: To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy and to assess potential risk factors for post-operative and surgery related mortality.Materials and methods: Material consists of 1099 consecutive radical cystectomy (RC) patients operated at 16 different hospitals in Finland 2005-2014. Machine learning methodology was utilized. For model building and testing, the data was randomly divided into training data (n = 733, 66.7%) and independent testing data (n = 366, 33.3%). To predict the risk of early death after RC from baseline variables, a binary classifier was constructed using logistic regression with lasso regularization. Finally, a user-friendly risk table was constructed for practical use. Results: The model resulted in an area under the receiver operating characteristic curve (AUROC) of 0.73 (95% CI = 0.59-0.87). The strongest risk factors were: American Society of Anesthesiologists physical status classification (ASA), congestive heart failure (CHF), age adjusted Charlson comorbidity index (ACCI) and chronic pulmonary disease. Conclusion: This study with a novel methodological approach adds CHF and chronic pulmonary disease to previously known independent prognostic risk factors for early death after RC. Importantly, the risk prediction tool uses purely pre-operative data and can be used before surgery.
Purpose: To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy and to assess potential risk factors for post-operative and surgery related mortality.Materials and methods: Material consists of 1099 consecutive radical cystectomy (RC) patients operated at 16 different hospitals in Finland 2005-2014. Machine learning methodology was utilized. For model building and testing, the data was randomly divided into training data (n = 733, 66.7%) and independent testing data (n = 366, 33.3%). To predict the risk of early death after RC from baseline variables, a binary classifier was constructed using logistic regression with lasso regularization. Finally, a user-friendly risk table was constructed for practical use. Results: The model resulted in an area under the receiver operating characteristic curve (AUROC) of 0.73 (95% CI = 0.59-0.87). The strongest risk factors were: American Society of Anesthesiologists physical status classification (ASA), congestive heart failure (CHF), age adjusted Charlson comorbidity index (ACCI) and chronic pulmonary disease. Conclusion: This study with a novel methodological approach adds CHF and chronic pulmonary disease to previously known independent prognostic risk factors for early death after RC. Importantly, the risk prediction tool uses purely pre-operative data and can be used before surgery.
Authors: Riku Klén; Disha Purohit; Ricardo Gómez-Huelgas; José Manuel Casas-Rojo; Juan Miguel Antón-Santos; Jesús Millán Núñez-Cortés; Carlos Lumbreras; José Manuel Ramos-Rincón; Noelia García Barrio; Miguel Pedrera-Jiménez; Antonio Lalueza Blanco; María Dolores Martin-Escalante; Francisco Rivas-Ruiz; Maria Ángeles Onieva-García; Pablo Young; Juan Ignacio Ramirez; Estela Edith Titto Omonte; Rosmery Gross Artega; Magdy Teresa Canales Beltrán; Pascual Ruben Valdez; Florencia Pugliese; Rosa Castagna; Ivan A Huespe; Bruno Boietti; Javier A Pollan; Nico Funke; Benjamin Leiding; David Gómez-Varela Journal: Elife Date: 2022-05-17 Impact factor: 8.713