Awake spinal surgery: simplifying the learning curve with a patient selection algorithm

• Published in: Neurosurgical focus Vol. 51; no. 6; p. E2
• Main Authors: Letchuman, Vijay, Agarwal, Nitin, Mummaneni, Valli P., Wang, Michael Y., Shabani, Saman, Patel, Arati, Rivera, Joshua, Haddad, Alexander F., Le, Vivian, Chang, Joyce M., Chou, Dean, Gandhi, Seema, Mummaneni, Praveen V.
• Format: Journal Article
• Language: English
• Published: United States 01.12.2021
• Subjects: Aged; Algorithms; Female; Humans; Learning Curve; Middle Aged; Patient Selection; Spinal Fusion; Wakefulness; fusion; decompression; spine; awake; anesthesia
• ISSN: 1092-0684; 1092-0684
• DOI: 10.3171/2021.9.FOCUS21433

There is a learning curve for surgeons performing "awake" spinal surgery. No comprehensive guidelines have been proposed for the selection of ideal candidates for awake spinal fusion or decompression. The authors sought to formulate an algorithm to aid in patient selection for surgeons who are in the startup phase of awake spinal surgery. The authors developed an algorithm for selecting patients appropriate for awake spinal fusion or decompression using spinal anesthesia supplemented with mild sedation and local analgesia. The anesthetic protocol that was used has previously been reported in the literature. This algorithm was formulated based on a multidisciplinary team meeting and used in the first 15 patients who underwent awake lumbar surgery at a single institution. A total of 15 patients who underwent decompression or lumbar fusion using the awake protocol were reviewed. The mean patient age was 61 ± 12 years, with a median BMI of 25.3 (IQR 2.7) and a mean Charlson Comorbidity Index of 2.1 ± 1.7; 7 patients (47%) were female. Key patient inclusion criteria were no history of anxiety, 1 to 2 levels of lumbar pathology, moderate stenosis and/or grade I spondylolisthesis, and no prior lumbar surgery at the level where the needle is introduced for anesthesia. Key exclusion criteria included severe and critical central canal stenosis or patients who did not meet the inclusion criteria. Using the novel algorithm, 14 patients (93%) successfully underwent awake spinal surgery without conversion to general anesthesia. One patient (7%) was converted to general anesthesia due to insufficient analgesia from spinal anesthesia. Overall, 93% (n = 14) of the patients were assessed as American Society of Anesthesiologists class II, with 1 patient (7%) as class III. The mean operative time was 115 minutes (± 60 minutes) with a mean estimated blood loss of 46 ± 39 mL. The median hospital length of stay was 1.3 days (IQR 0.1 days). No patients developed postoperative complications and only 1 patient (7%) required reoperation. The mean Oswestry Disability Index score decreased following operative intervention by 5.1 ± 10.8. The authors propose an easy-to-use patient selection algorithm with the aim of assisting surgeons with patient selection for awake spinal surgery while considering BMI, patient anxiety, levels of surgery, and the extent of stenosis. The algorithm is specifically intended to assist surgeons who are in the learning curve of their first awake spinal surgery cases.