Risk factors associated with delayed discharge following robotic assisted surgery for gynecologic malignancy

Joan R. Tymon-Rosario, Devin T. Miller, Akiva Novetsky, Gary L. Goldberg, Nicole S. Nevadunsky, Sharmila K. Makhija, Dennis Y. Kuo, Anne R. Van Arsdale

Research output: Contribution to journalArticlepeer-review


Background: The risk factors for extended length of stay (LOS) have not been examined in a cohort of patients with complex social and medical barriers who undergo robotic assisted (RA) surgery for gynecologic malignancies. We sought to identify those patients with a LOS > 24 h after robotic surgery and the risk factors associated with delayed discharge. Then we aimed to develop a predictive model for clinical care and identify modifiable pre-operative risk factors. Methods: After IRB approval, data was abstracted from medical records of all patients with a gynecologic malignancy who underwent a RA laparoscopic surgery from 2010 to 2015. Univariable and multivariable logistic regression was performed to identify independent risk factors associated with delayed discharge defined as LOS > 24 h. A multi-variable logistic regression model was performed using a stepwise backward selection for the final prediction model. All testing was two-sided and a p-value < 0.05 was considered statistically significant. Results: Of the 406 eligible and evaluable patients, 194 (48%) had a LOS > 24 h. Age ≥ 60 years, a higher usage of narcotic medication, a longer surgical time, and a larger estimated blood loss were all associated with LOS > 24 h (p < 0.05). Many of these women had a social work consultation and went home with home care services despite no surgical or post-operative complications. Our prediction model has the potential to correctly classified 75% of the patients discharged within 24 h. Conclusions: The development of a pre-hospitalization risk stratification and anticipating the possible need for home care services pre-operatively shows promise as a strategy to decrease LOS in patients classified as high-risk. These findings warrant prospective validation through the use of this prediction model in our institution.

Original languageEnglish (US)
Pages (from-to)723-728
Number of pages6
JournalGynecologic Oncology
Issue number3
StatePublished - Jun 2020


  • Delayed discharge
  • Diverse urban population
  • Gynecologic malignancy
  • Post-operative length of stay
  • Pre-hospitalization risk stratification
  • Robotic assisted surgery

ASJC Scopus subject areas

  • Oncology
  • Obstetrics and Gynecology


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