Robotic-assisted vs. fluoroscopy-assisted MIS-TLIF: improved screw accuracy and reduced early opioid use with comparable long-term outcomes - Report - MDSpire
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Robotic-assisted vs. fluoroscopy-assisted MIS-TLIF: improved screw accuracy and reduced early opioid use with comparable long-term outcomes
Clinical Report: Comparative Analysis of Robotic-Assisted and Fluoroscopy-Assisted MIS-TLIF
Overview
This study compares robotic-assisted (RA) and fluoroscopy-assisted (FA) techniques in minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF). The RA approach showed improved screw placement accuracy and reduced early opioid consumption.
Background
Minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is a common surgical intervention for degenerative lumbar disease. The adoption of robotic-assisted techniques aims to enhance surgical precision and reduce opioid consumption.
Data Highlights
Outcome
RA Group
FA Group
P-value
Screw Accuracy (Grade A)
91%
57%
<0.001
Inpatient MME (Postoperative Days 0-1)
Lower
Higher
0.002
Total LOS MME
Lower
Higher
0.0015
Long-term Opioid Use (3, 6, 12 months)
Similar
Similar
p > 0.05
Key Findings
The RA group had a significantly higher proportion of grade A screws compared to the FA group (91% vs. 57%, p < 0.001).
Inpatient opioid consumption was significantly lower in the RA group on postoperative days 0-1 (p = 0.002).
Total opioid consumption during length of stay was also lower in the RA group (p = 0.0015).
Long-term opioid use at 3, 6, and 12 months showed no significant differences between the two groups (p > 0.05).
Clinical Implications
The findings suggest that while robotic-assisted techniques improve screw placement accuracy and reduce early postoperative opioid consumption, they do not lead to differences in long-term opioid use or patient-reported outcomes. This information may guide surgical decision-making and opioid management strategies.
Conclusion
Robotic-assisted MIS-TLIF offers advantages in screw accuracy and early opioid consumption.
by Daniel W. Griepp, Bryce Sarcar, Hepzibha Alexander, Rabia Ahmed, Andrew Beggs, Armando Bunjaj, Jeffrey P. Turnbull, Joshua Caskey, Shivum Desai, Heather Heitkotter, Daniel A. Carr