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Predictive capacity of four comorbidity indices estimating perioperative mortality after radical cystectomy for urothelial carcinoma of the bladder

Study Type – Prognosis (case series) Level of Evidence 4 What's known on the subject? and What does the study add? The degree of comorbidity significantly affects the course of patients with bladder cancer undergoing radical cystectomy (RC). To our knowledge this is the first study comparing fo...

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Bibliographic Details
Published in:BJU international 2012-09, Vol.110 (6b), p.E222-E227
Main Authors: Mayr, Roman, May, Matthias, Martini, Thomas, Lodde, Michele, Pycha, Armin, Comploj, Evi, Wieland, Wolf F., Denzinger, Stefan, Otto, Wolfgang, Burger, Maximilian, Fritsche, Hans‐Martin
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Language:English
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Summary:Study Type – Prognosis (case series) Level of Evidence 4 What's known on the subject? and What does the study add? The degree of comorbidity significantly affects the course of patients with bladder cancer undergoing radical cystectomy (RC). To our knowledge this is the first study comparing four different comorbidity indices in patients undergoing RC for urothelial carcinoma to assess the best clinical predictors for 90‐day perioperative mortality. We concluded that the ASA score should be the method of choice, as it showed a predictive ability superior to that of ECOG and CCI, and is much easier to generate than the ACE‐27. OBJECTIVE •  To evaluate which of the following among the Adult Comorbidity Evaluation‐27 (ACE‐27), the Charlson Comorbidity Index (CCI), the Eastern Cooperative Oncology Group performance status (ECOG) and the American Society of Anesthesiologists (ASA) comorbidity scores correlate best with perioperative mortality after radical cystectomy (RC) for urothelial carcinoma (UC) of the bladder. PATIENTS AND METHODS •  A study was carried out on 555 unselected consecutive patients without neoadjuvant chemotherapy who underwent RC for UC of the bladder from 2000 to 2010 at one of two institutions. •  Patients' medical records were reviewed retrospectively. •  We established a defined binary linear progression model based on clinical variables to predict perioperative mortality
ISSN:1464-4096
1464-410X
DOI:10.1111/j.1464-410X.2012.10938.x