Erschienen in:
26.09.2016 | Urology - Original Paper
Non-modifiable factors predict discharge quality after robotic partial nephrectomy
verfasst von:
Matthew J. Maurice, Daniel Ramirez, Önder Kara, Ryan J. Nelson, Peter A. Caputo, Ercan Malkoç, Jihad H. Kaouk
Erschienen in:
International Urology and Nephrology
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Ausgabe 1/2017
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Abstract
Purpose
To identify predictors of poor discharge quality after robotic partial nephrectomy (RPN) at a large academic center.
Methods
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.
Results
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).
Conclusions
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.