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

• Published in: Gynecologic oncology Vol. 157; no. 3; pp. 723 - 728
• Main Authors: Tymon-Rosario, Joan R., Miller, Devin T., Novetsky, Akiva P., Goldberg, Gary L., Nevadunsky, Nicole S., Makhija, Sharmila K., Kuo, Dennis Y., Van Arsdale, Anne R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-06-2020
• Subjects: Delayed discharge; Diverse urban population; Female; Genital Neoplasms, Female - complications; Genital Neoplasms, Female - surgery; Gynecologic malignancy; Humans; Middle Aged; Patient Discharge; Post-operative length of stay; Postoperative Complications - etiology; Pre-hospitalization risk stratification; Prospective Studies; Risk Factors; Robotic assisted surgery; Robotic Surgical Procedures - adverse effects; Post-operative length of stay; Robotic assisted surgery; Gynecologic malignancy; Delayed discharge; Diverse urban population; Pre-hospitalization risk stratification
• ISSN: 0090-8258; 1095-6859; 1095-6859
• DOI: 10.1016/j.ygyno.2020.03.019

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.
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.
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.
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.
•Almost half of our patients are discharged >24 h after their procedure despite a low rate of surgical complications.•Age > 60, narcotic usage, longer surgical time, and larger estimated blood loss were associated with delayed discharge.•A standardized pain management regimen that places non-narcotic analgesia at the forefront may decrease postoperative stay.•Research is needed to determine if anticipation of home care services preoperatively can decrease postoperative stay.•Prospective evaluation of our population specific model is warranted.