Spinal Deformity Complexity Checklist for Minimally Invasive Surgery: Expert Consensus from the Minimally Invasive International Spine Study Group.

Background: We developed a spinal deformity complexity checklist (SDCC) to assess the difficulty in performing a circumferential minimally invasive surgery (MIS) for adult spinal deformity.
Methods: A modified Delphi method of panel experts was used to construct an SDCC checklist of radiographic and patient-related characteristics that could affect the complexity of surgery via MIS approaches. Ten surgeons with expertise in MIS deformity surgery were queried to develop and refine the SDCC with 3 radiographic categories (x-ray, magnetic resonance imaging, computed tomography) and 1 patient-related category. Within each category, characteristics affecting MIS complexity were identified by initial roundtable discussion. Second-round discussion determined which characteristics substantially impacted complexity the most.
Results: Thirteen characteristics within the x-ray category were determined. Spinopelvic characteristics, endpoints of instrumentation, and prior hardware/fusion were associated with increased complexity. Vertebral body rotation-as reflected by the Nash-Moe grade-added significant complexity. Psoas anatomy and spinal stenosis added the most complexity for the 5 magnetic resonance imaging characteristics. There were 3 characteristics in the CT category with pre-exisiting fusion, being the variable most highly selected. Of the 5 patient-related characteristics, osteoporosis and BMI were found to most affect complexity.
Conclusions: The SDCC is a comprehensive list of pertinent radiographic and patient-related characteristics affecting complexity level for MIS deformity surgery. The value of the SDCC is that it allows rapid assessment of key factors when determining whether MIS surgery can be performed effectively and safely. Patients with scores of 4 in any characteristic should be considered challenging to treat with MIS; open surgery may be a better alternative.
(Copyright © 2023 Elsevier Inc. All rights reserved.)