Comparison of Midline versus Bilateral Paramedian Incisions for Robot-Assisted Spine Surgery

Introduction: Both midline and bilateral paramedian incisions are commonly used during spine surgery. Literature on the impact of incision type on surgical outcomes is conflicting, with studies implicating both midline and bilateral incisions with increased rates of intraoperative complications. In this study, we systematically compare between the two incision types among robotic spine surgeries. Furthermore, we establish a deep learning-based prediction model to identify risk factors for incidental durotomy among this cohort.

Methods: All patients undergoing robotic-assisted spine surgery from 2018 to 2022 were retrospectively identified and categorized into midline and bilateral incision groups. Univariate analyses were used to compare demographics, operative details, the primary outcome (durotomy), and other complications. Risk factors for incidental durotomy were identified using multiple machine learning survival models, which were evaluated through Receiver Operating Characteristic (ROC) analysis with the Area Under the Curve (AUC) calculated.

Results: Of the 500 robotic-assisted spine cases identified, 302 (60%) were performed with bilateral incisions while 198 (40%) used a midline incision. Incidental durotomy was higher among patients with midline incisions (13% vs 5%, p=0.001), as was mortality (p=0.002), revision surgery (p < 0.001), blood loss (p < 0.001), and surgical duration (p < 0.001). Bilateral incisions were preferred for thoracic procedures and TLIFs, while midline was common for lumbar cases (p < 0.001). Midline patients also had poorer preoperative status, with a higher proportion scoring 50-70 on the Karnofsky Performance Status (p=0.024), 2+ on the CCI index (p < 0.001) and 3 for ASA status (p < 0.001). Postoperative complications, like myocardial infarction, acute kidney injury, pulmonary edema and septic shock are more common in the midline cohort. Similarly, rates of 30-day readmission (p=0.018), wound complications (p=0.018) and long-term reoperation (p=0.001) were higher in this group. For risk factors of durotomy, the Random Forest Classifier model was the best-performing (AUC=0.709), with a minimal depth analysis identifying BMI, age at surgery, race and midline approach as the top 4 factors significantly associated with elevated risk of durotomy.

Conclusion : Midline incisions in robotic spine surgery show higher risks of durotomy and complications, underscoring the need for careful surgical planning. Machine learning models could enhance patient-specific risk assessments, guiding safer incision approach selection.