1659 Factors Associated with Use of Robot-Assistance for Hysterectomy in Treating Benign Disease

Abstract

Study Objective To evaluate what factors are associated with the use of robot assistance in benign gynecologic hysterectomies. Design We performed a cross-sectional analysis of the 2007 - 2014 Nationwide Inpatient Sample database. All women age ≥18 years who underwent hysterectomy were identified using ICD-9-CM procedure codes. A second ICD-9-CM code was used to identify cases with robot assistance. Patients who underwent treatment for cancer or have concomitant cesarean deliveries were excluded. Logistic regression was used to assess independent demographic, clinical or health system factors associated with undergoing a robotic-assisted procedure versus traditional laparoscopy or abdominal approaches. Setting N/A Patients or Participants N/A Interventions N/A Measurements and Main Results When compared to all routes of hysterectomy, patients age > 34 (OR 1.16), median household income above the first quartile (OR 1.13) and a diagnosis of obesity (OR 1.19), endometriosis (OR 1.15), uterine prolapse (OR 1.11), abnormal uterine bleeding (OR 1.22) and pelvic pain (OR 1.24) were more likely to undergo robot-assisted hysterectomy. Those undergoing treatment in urban (OR 3.36) and large hospitals (OR 1.47) were more likely to undergo robot-assisted hysterectomy. Medicaid insurance holders (OR 0.64) and Black (OR 0.74), Hispanic (OR 0.97) and Asian/Pacific Islander (OR 0.78) races were associated with lower likelihood of undergoing robot-assisted hysterectomy. Similar factors were found when examining predictors of robot-assisted hysterectomy among all laparoscopic hysterectomies, although patient race, income, and diagnosis were not associated with undergoing robot-assisted hysterectomy in this subgroup. Conclusion In addition to clinical diagnosis, many demographic and health system factors are associated with use of robot-assistance for hysterectomy. This may indicate decreased access to the robot among underserved patient populations coupled with the capacity of large, urban facilities to make capital investments in robotic equipment.