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.
Keywords: Delayed discharge; Diverse urban population; Gynecologic malignancy; Post-operative length of stay; Pre-hospitalization risk stratification; Robotic assisted surgery.
Published by Elsevier Inc.