ArticleBladder cancer (Amsterdam, Netherlands)2022
Comorbidity Scores and Machine Learning Methods Can Improve Risk Assessment in Radical Cystectomy for Bladder Cancer.
Article in Bladder cancer (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- Postoperative outcomes of bariatric surgery performed by trainees: a retrospective single-centre study from a Swiss regional hospital.Langenbeck's archives of surgery · 2026Article
- Impact of Surgical Wait Time on Survival After Radical Cystectomy for Muscle-Invasive Bladder Cancer: A Retrospective Cohort Study.Cancer management and research · 2026Article
- Bladder Cancer and Artificial Intelligence: Emerging Applications.The Urologic clinics of North America · 2024Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPre-operative risk assessment in radical cystectomy (RC) is an ongoing challenge especially in elderly patients.
objectiveTo evaluate the ability of comorbidity indices and their combination with clinical parameters in machine learning models to predict mortality and morbidity after RC.
methodsIn 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.
resultsThe aCCI, ASA and GCI showed significant results for the prediction of complications (χ
conclusionsThe 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.
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