Comorbidity Scores and Machine Learning Methods Can Improve Risk Assessment in Radical Cystectomy for Bladder Cancer.

BACKGROUND

Pre-operative risk assessment in radical cystectomy (RC) is an ongoing challenge especially in elderly patients.

OBJECTIVE

To evaluate the ability of comorbidity indices and their combination with clinical parameters in machine learning models to predict mortality and morbidity after RC.

METHODS

In 392 patients who underwent open RC, complication and mortality rates were reported. The predictive values of the age-adjusted Charlson Comorbidity index (aCCI), the Elixhauser Index (EI), the Physical Status Classification System (ASA) and Gagne's combined comorbidity Index (GCI) were evaluated using regression analyses. Various machine learning models (Gaussian naïve bayes, logistic regression, neural net, decision tree, random forest) were additionally investigated.

RESULTS

The aCCI, ASA and GCI showed significant results for the prediction of complications (χ

CONCLUSIONS

The ASA and aCCI show good prediction of mortality after RC but fail predicting complications accurately. Here, the combination of comorbidity indices and clinical parameters in machine learning models seems promising.

Informations de copyright

© 2022 – The authors. Published by IOS Press.

Déclaration de conflit d'intérêts

Frderik Wessels has no conflict of interest to report. Isabelle Bußhoff has no conflict of interest to report. Sophia Adam has no conflict of interest to report. Karl-Friedrich Kowalewski has no conflict of interest to report. Manuel Neuberger has no conflict of interest to report. Philipp Nuhn has no conflict of interest to report. Maurice Stephan Michel is financially supported by the German Cancer Aid (Deutsche Krebshilfe, project ID 73000189). The sponsor had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data, preparation, review or approval of the manuscript, and decision to submit the manuscript for publication. Maximilian C. Kriegmair is financially supported by the German Cancer Aid (Deutsche Krebshilfe, project ID 73000189). The sponsor had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data, preparation, review or approval of the manuscript, and decision to submit the manuscript for publication. Availability of data and materialData is available only internally due to its sensitive nature and its protection under the European General Data Protection Regulation. Sharing of data with external researchers might only be possible upon reasonable request and after obtaining ethics approval.