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Non-modifiable factors predict discharge quality after robotic partial nephrectomy.

Matthew J Maurice1, Daniel Ramirez1, Önder Kara1,2

  • 1Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic, 9500 Euclid Ave, Q10-1, Cleveland, OH, 44195, USA.

International Urology and Nephrology
|September 28, 2016
PubMed
Summary

Predictors of poor discharge quality after robotic partial nephrectomy (RPN) include non-modifiable patient and disease factors like race, chronic kidney disease, and tumor complexity. Risk adjustment is crucial for RPN provider reimbursement.

Keywords:
Length of stayNephrectomyPatient dischargePatient readmissionRobotic surgical procedures

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Area of Science:

  • Urology
  • Surgical Outcomes
  • Health Services Research

Background:

  • Robotic partial nephrectomy (RPN) is a common procedure for kidney tumors.
  • Ensuring high-quality patient discharge is essential for successful surgical outcomes and healthcare efficiency.
  • Identifying factors associated with suboptimal discharge can inform quality improvement initiatives.

Purpose of the Study:

  • To determine patient, disease, and provider-related predictors of poor discharge quality following RPN.
  • To analyze the impact of various factors on length of stay and unplanned readmissions after RPN.

Main Methods:

  • A retrospective analysis of 791 patients undergoing RPN between 2011 and 2015.
  • Poor discharge quality was defined as a length of stay exceeding 3 days or an unplanned readmission.
  • Univariate and multivariable logistic regression analyses were used to identify significant predictors.

Main Results:

  • Over 27% of patients experienced poor discharge quality.
  • Significant predictors included older age, Black race, social insurance, higher ASA score, chronic kidney disease (CKD), increased tumor size, and higher tumor complexity.
  • On multivariable analysis, Black race, higher ASA, CKD, tumor size, and tumor complexity remained significant predictors.
  • CKD, Black race, and higher ASA were associated with the highest odds of poor discharge.

Conclusions:

  • Non-modifiable patient characteristics and disease factors are key predictors of poor discharge quality after RPN.
  • These findings highlight the need for risk adjustment based on patient and disease factors in healthcare reimbursement models for RPN.
  • Future strategies should focus on mitigating risks associated with these identified factors to improve RPN outcomes.