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Predicting length of stay after robotic partial nephrectomy

  • Urology - Original Paper
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Abstract

Introduction

To investigate factors predictive of length of stay (LOS) after robotic partial nephrectomy (RPN) in an effort to identify patients suitable for RPN with overnight stay at outpatient surgical facilities.

Materials and methods

Retrospective chart review of patients who underwent RPN at Memorial Sloan Kettering Cancer Center from January 2007 to July 2012 was conducted. Univariate and multivariate analyses were performed to identify the main predictors of LOS. The discrimination of the multivariate model was measured using the area under the curve (AUC); tenfold cross-validation was performed to correct for over-fit.

Results

One hundred and eighty-six patients were included in the analysis; 84 (45 %) had LOS of ≤1 day (median LOS 2 day; interquartile range 1–2). On univariate analysis, preoperative variables associated with LOS > 1 included larger tumors (P < 0.0001), lower estimated glomerular filtration rate (P = 0.003), older age (P = 0.006), female gender (P = 0.035), and higher comorbidity score (P = 0.015); operative variables associated with LOS > 1 day included greater estimated blood loss (P < 0.0001) and longer operative (P < 0.0001) and ischemia (P < 0.0001) times. The AUC of the preoperative model was 0.61 (95 % CI 0.52–0.69) after tenfold cross-validation.

Conclusions

LOS after RPN is influenced by age, gender, medical comorbidities, and tumor size. However, when analyzed retrospectively, these factors had limited ability to predict LOS after RPN with sufficient accuracy to develop a prediction tool.

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Authors and Affiliations

  1. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA

    Wassim M. Bazzi, Melanie Bernstein, Raul Parra & Jonathan A. Coleman

  2. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Daniel D. Sjoberg & Angelica A. C. Grasso

Authors
  1. Wassim M. Bazzi
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  2. Daniel D. Sjoberg
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  3. Angelica A. C. Grasso
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  4. Melanie Bernstein
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  5. Raul Parra
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  6. Jonathan A. Coleman
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Corresponding author

Correspondence to Jonathan A. Coleman.

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Funding

This investigation was supported by the Sidney Kimmel Center for Prostate and Urologic Cancers.

Conflict of interest

All authors declare no conflict of interests.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Bazzi, W.M., Sjoberg, D.D., Grasso, A.A.C. et al. Predicting length of stay after robotic partial nephrectomy. Int Urol Nephrol 47, 1321–1325 (2015). https://doi.org/10.1007/s11255-015-1044-7

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  • DOI: https://doi.org/10.1007/s11255-015-1044-7

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