Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology

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.