Non-modifiable factors predict discharge quality after robotic partial nephrectomy

Abstract

To identify predictors of poor discharge quality after robotic partial nephrectomy (RPN) at a large academic center. We queried our institutional RPN database for consecutive patients treated between 2011 and 2015. The primary outcome was poor discharge quality, defined as length of stay > 3 days and/or unplanned readmission. The association between patient, disease, and provider factors and overall discharge quality was assessed using univariate and multivariable analyses. Of 791 cases, 219 (27.7 %) had poor discharge quality. On univariate analysis, factors associated with poor discharge quality were older age (p < .01), black race (p = .01), social insurance (p < .01), higher ASA score (p < .01), chronic kidney disease (p < .01), increased tumor size (p < .01), and higher tumor complexity (p = .01). Surgeon case volume did not predict discharge quality (p = .63). After adjustment for covariates on multivariable analysis, race (p = .01), ASA (p = .02), CKD (p < .01), tumor size (p = .02), and tumor complexity (p = .03) still predicted poor discharge quality. In particular, the odds of poor discharge quality were highest in the setting of CKD (OR 2.62, 95 % CI 1.72-4.01), black race (OR 2.17, 95 % CI 1.32-3.57), and higher ASA (OR 1.49, 95 % CI 1.07-2.08). Non-modifiable patient and disease factors predict poor discharge quality after RPN. Risk adjustment for these factors will be important for determining future reimbursement for RPN providers.