Pre-Operative Planning in Complex Deformities and Use of Patient-Specific UNiD^™ Instrumentation

Abstract

Study Design Review of current literature and authors experience. Objective Pre-operative planning is an integral part of complex spine surgery. With the advent of computer-assisted planning, multiple surgical plans can be evaluated utilizing alignment parameters, and the best plan for individual patients selected. However, the ability to evaluate and measure surgical correction goals intraoperatively are still limited. The use of patient-specific UNiD rods, created based on pre-operative plans, provided an initial tool for implementation of pre-operative plans in the operative setting. Methods A literature review for the use of patient-specific UNiD rods in thoracolumbar spine complex surgery was performed. The articles were selected and reviewed for the initial experience/outcomes of these techniques. Further, the initial experience of the authors at The University of Colorado is described. Results The use of UNiD patient-specific rods, in combination with pre-operative planning has been shown to provide a higher rate of patients with spinopelvic alignment parameters within currently accepted ranges. This includes improvement of sagittal vertical axis (SVA) < 50 mm and pelvic incidence (PI)–lumbar lordosis (LL) = ± 10°. Multiple authors have shown improvement in pelvic tilt to age adjusted values but note continued difficulties in obtaining correction goals. Conclusions The use of pre-operative planning software and UNiD patient-specific rods has been shown to improve surgeon’s ability to achieve spinopelvic alignment parameters, specifically SVA and PI-LL, along with other possible benefits. Further research is needed regarding long-term value of the technology.

Personalized Medicine in Orthopaedic Surgery: The Case of Spine Surgery

“…More recently, surgical planning tools have begun integrating techniques to measure spinopelvic parameters from radiographs and to generate plans facilitated by machine learning algorithms. 3 UNiD Adaptive Spine Intelligence (Medtronic) is a surgical planning tool that uses dedicated engineers to measure spinopelvic alignment parameters, predicts reciprocal alignment changes that will happen in the untouched segments of the spine, and enables the surgeon to order rods for implantation contoured to the patient's desired postoperative alignment (Figure 1). This system continuously collects preoperative and postoperative data to improve its predictive capability using machine learning algorithms.…”

“…Prebent contoured rods based on preoperative planning parameters have demonstrated their efficacy in obtaining preplanned spinal alignment. 3 These prebent rods have demonstrated secondary advantages including increased ultimate load and stiffness mechanical properties reducing the risk of rod breakage 23 and a better overall maintenance of curve correction in a study of adolescent idiopathic patients. 24 Another technology which shows great promise in intraoperative treatments of deformity is 3D printing (Figure 2).…”

“…2 Furthermore, new technological advances have allowed clinicians to have patient-specific surgical plans using custom-made individualized implants. 3 This review article will summarize the recent literature describing the use of personalized care in orthopaedics, using the orthopaedic subspecialty of spine surgery as a case example. We will also review how personalized medicine can affect processes and outcomes in all phases of care including the preoperative evaluation, the surgical procedure itself, and postoperative management.…”

Clinical Validation of a Novel Musculoskeletal Modeling Framework to Predict Postoperative Sagittal Alignment

“…Lafage et al 3 combined preoperative and postoperative radiographs and adjusted the estimated postoperative PT in virtual alignment models. Recent studies have used machine learning techniques to estimate postoperative PT and thoracic kyphosis (TK) in long fusion cases 15–17. The limitations of regression and geometrical models include negligence of RC, lack of generalizability to different patient cohorts, and explanation of the underlying causes of postural changes regulated by patient and surgical attributes 8,9.…”

“…Recent studies have used machine learning techniques to estimate postoperative PT and thoracic kyphosis (TK) in long fusion cases. [15][16][17] The limitations of regression and geometrical models include negligence of RC, lack of generalizability to different patient cohorts, and explanation of the underlying causes of postural changes regulated by patient and surgical attributes. 8,9 Machine learning models share some of these inherent limitations.…”