Factors Impacting Use of Robotic Surgery for Treatment of Endometrial Cancer in the United States

• Published in: Annals of surgical oncology 2016-10, Vol.23 (11), p.3744-3748
• Main Authors: Blake, Erin A., Sheeder, Jeanelle, Behbakht, Kian, Guntupalli, Saketh R., Guy, Michael S.
• Format: Article
• Language: English
• Subjects: Age Factors; Aged; Comorbidity; Elective Surgical Procedures - statistics & numerical data; Endometrial Neoplasms - complications; Endometrial Neoplasms - surgery; European Continental Ancestry Group - statistics & numerical data; Female; Gynecologic Oncology; Health Facility Size - statistics & numerical data; Hospitals, Private - statistics & numerical data; Hospitals, Teaching; Humans; Income; Insurance, Health - statistics & numerical data; Medicine; Medicine & Public Health; Middle Aged; Obesity - complications; Oncology; Robotic Surgical Procedures - utilization; Rural Population - statistics & numerical data; Surgery; Surgical Oncology; United States; Urban Population - statistics & numerical data
• ISSN: 1068-9265; 1534-4681
• DOI: 10.1245/s10434-016-5252-x

Objective This study was designed to examine the impact of patient socioeconomic, clinical, and hospital characteristics on the utilization of robotics in the surgical staging of endometrial cancer. Methods Patients surgically treated for endometrial cancer at facilities that offered robotic and open approaches were identified from the National Inpatient Sample Database from 2008 to 2012. The groups were compared for socioeconomic, clinical, and hospital differences. Medical comorbidity scores were calculated using the Charlson comorbidity index. T tests and χ 2 were used to compare groups. Multivariable analyses were used to determine factors that were independently associated with a robotic approach. Results A total of 18,284 patients were included (robotic, n  = 7169; laparotomy, n  = 11,115). Significant differences were noted in all patient clinical and socioeconomic characteristics and all hospital characteristics. Multivariable analyses identified factors that independently predicted patients undergoing robotic surgery. These patients were older [adjusted odds ratio (aOR) 1.008; 95 % confidence interval (CI) 1.004–1.011], white (aOR 1.38; 95 % CI 1.27–1.50), and privately insured (aOR 1.16; 95 % CI 1.07–1.26). Clinically, these women were more likely to be obese (aOR 1.20; 95 % CI 1.11–1.30) and to be undergoing an elective case (aOR 1.25; 95 % CI 1.11–1.40). Hospitals were more likely to be under private control (aOR 1.55, 95 % CI 1.39–1.71) but less likely to be located in the south (aOR 0.87; 0.81–0.93), quantified as large or medium (aOR 0.57; 95 %CI 0.50–0.67), or teaching hospitals (aOR 0.68; 95 % CI 0.63–0.74). Conclusions Socioeconomic status and hospital characteristics are factors that independently predict robotic utilization in the United States. These racial, socioeconomic, and geographic disparities warrant further study regarding the utilization of this important technology.