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Risk factors associated with delayed discharge following robotic assisted surgery for gynecologic malignancy
Joan R Tymon-Rosario1, Devin T Miller2, Akiva P Novetsky3
1Montefiore Medical Center, Division of Gynecologic Oncology, Department of Obstetrics & Gynecology and Women's Health, Bronx, NY, United States of America; Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine, New Haven, CT, United States of America.
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.
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