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

ABSTRACT:

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 investig

SUBMITTER: Wessels F 

PROVIDER: S-EPMC11181714 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature