Demographic Trends in Robotic-assisted Spinal Fusion from 2011 to 2021

Introduction: The adoption of robotic-assisted technology in spinal fusion surgery has expanded significantly in recent years. Trends in robotic adoption across different demographic groups remain underexplored. This study examines trends in robotic spinal fusion adoption across age, gender, and racial groups from 2011 to 2021 to provide insights into demographic variations in adoption.

Methods: We analyzed data from the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP NIS) from 2011 to 2021 to observe trends in robotic-assisted spinal fusion. Patients were categorized by age groups (0-17, 18-29, 30-49, 50-64, 65+), gender, and race (White, Black, Hispanic, Asian or Pacific Islander, Native American, Other). Adoption rates and relative distributions were assessed within each demographic group and compared to total spinal fusion distributions.

Results: A total of 34,896 patients who underwent robotic-assisted spinal fusion were identified. Robotic adoption increased across all demographic groups, with the most notable growth occurring after 2017. By 2021, adoption rates were highest among older age groups (30-49, 50-64, and 65+), reflecting the age distribution of overall spinal fusion procedures. Gender trends revealed slightly higher rates of robotic adoption among female patients compared to males, particularly in recent years. Racial trends indicated increasing adoption across all groups, with Hispanic and White patients showing the highest adoption rates by 2021, while Native American and Black patients exhibited lower but steadily increasing rates.

Conclusion : Our analysis highlights growth in robotic-assisted spinal fusion adoption across age, gender, and racial groups, with demographic patterns similar to those seen in overall spinal fusion procedures. While adoption rates have increased across all groups, differences amongst groups point to potential variations in access. These findings warrant further research into factors driving differences in adoption rates and to identify any potential barrier to access for underrepresented groups.