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Impact of Non-modifiable Patient Factors on Perioperative Outcome Following Radical Cystectomy with Enhanced Recovery Protocol

Abstract: PD36-02
Sources of Funding: none

Introduction

Enhanced recovery after surgery (ERAS) protocols in the setting of radical cystectomy have consistently improved perioperative outcomes. Though known to impact individual surgical and post-operative course, the influence of non-modifiable patient factors on perioperative outcomes has not been well-quantified. We sought to determine how various non-modifiable patient factors impact perioperative outcomes following radical cystectomy with ERAS.

Methods

We retrospectively reviewed our IRB approved prospectively-maintained bladder cancer database. Patients who underwent open radical cystectomy for urothelial carcinoma with ERAS protocol were identified. Non-modifiable patient factors including age, race, BMI, charlson comorbidity index (CCI), and ASA score were examined. Univariate and multivariate analysis was completed to determine impact of these factors on length of hospital stay, 30 and 90 day complications and readmission.

Results

A total of 289 patients were identified who underwent open radical cystectomy with ERAS by three urologic oncologists between 5/2012 and 3/2016. Patient characteristics are described in Table 1. On multivariable analysis, age, race, CCI, and perioperative transfusion significantly impacted length of stay (Table 2). 30-day complication and readmission occurrences were impacted similarly by both preoperative hemoglobin level and perioperative transfusion though only perioperative transfusion remained significant on multivariable analysis (p=0.008 and p=0.005). No significant impact was found on 90 day complication or readmission.

Conclusions

Patient age, race, and CCI significantly impact length of hospital stay following radical cystectomy with enhanced recovery protocol. Perioperative transfusion is significantly associated with 30-day complication and readmission. Fixed patient factors should be accounted for in risk-adjustment and reimbursement models.

Funding

none

Authors
Daniel Zainfeld
Jie Cai
Gus Miranda
Anne Schuckman
Siamak Daneshmand
Hooman Djaladat
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