In:
Bladder Cancer, IOS Press, Vol. 6, No. 2 ( 2020-06-11), p. 151-159
Abstract:
BACKGROUND AND OBJECTIVE: While radical cystectomy (RC) is the gold-standard treatment for patients with muscle-invasive bladder cancer, it is associated with a significant rate of complications. We aim to develop a prediction model to assess the risk of complications in the postoperative period using routinely collected data in the course of preoperative evaluation in patients undergoing RC for bladder cancer. METHODS: We retrospectively reviewed 508 patients who underwent RC for bladder cancer from January 2008 to October 2016. Potential preoperative risk predictors were collected. Postoperative complications were graded using the Clavien-Dindo scale. Prediction models were developed using variables with the highest predictive value for postoperative complications. The accuracy of themodels was assessed using the area under the receiver operating characteristic curve (AUROC) and cross-validation. RESULTS: Variables achieved the highest prediction accuracy for major postoperative complications in the 31 to 90-day postoperative period. In this period, 14 variables were predictive of major postoperative complications. The three most predictive individual variables were BMI, neoadjuvant chemotherapy, and creatinine with AUROC/odds ratios of 0.643/1.09, 0.609/2.43, and 0.597/1.22, respectively. This postoperative period also had the best performing prediction model for major complications, which utilized four variables to achieve an AUROC of 0.727. CONCLUSION: Routinely collected preoperative variables may be useful for determining patient risk for short-term postoperative complications after RC. Prediction models can help identify patients who may benefit from patient education, counseling and development of risk reduction strategies. Interactions between individual variables should be evaluated to further improve accuracy of the prediction models.
Type of Medium:
Online Resource
ISSN:
2352-3727
,
2352-3735
Language:
Unknown
Publisher:
IOS Press
Publication Date:
2020
detail.hit.zdb_id:
2827070-8
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