PP16 Machine Learning In The Treatment Of Spinal Deformities: Early Life-cycle Economic Analysis In Australia

Introduction Patients with spinal deformity require implanted spinal rods to be specifically shaped to their anatomy. Spinal rods which are manually shaped in the operating theatre are prone to fracture and malpositioning. This emphasizes the need for preoperative planning, intraoperative imaging, and accurate shaping of the implant. The application of machine learning (ML) has enabled precision in shaping patient-specific rods, with the potential to reduce adverse events. Our objective is to assess the economic value of this technology, early in its lifecycle, in the context of Health Technology Assessment (HTA) in Australia. Methods A budget impact analysis was performed to quantify the economic value of patient-specific spinal rods from an Australian payor perspective. Clinical outcomes were sourced from literature review, and cost inputs were obtained from Medicare, Private Health Data Bureau and Hospital Casemix Protocol Data databases. Results Preliminary analysis indicates that a reduction in the rate of revision surgery due to decreased instrument failure results in cost-savings to the healthcare system, despite a higher outlay for the patient-specific rods. Adolescents who may have remained sagittally malaligned after the implantation of manually bent rods are expected to derive the greatest benefit from this ML application. The key uncertainty in this analysis is the limited real-world data of this emerging technology. ML is an iterative process of continuous improvement, identifying correlations within the data collected. As additional surgical data are integrated into predictive models, we anticipate ML technology will enhance decision-making support in surgical strategy and enable better implant precision, resulting in further decreased operating time, reduced mechanical complications, and increased healthcare savings. Conclusions ML technology is enabling precision in patient-specific implants, which is expected to drive healthcare cost-savings due to a reduction in instrument failure. Fewer replacement surgeries are an important patient-relevant outcome, especially for adolescents with spinal deformity. This preliminary analysis demonstrates the economic value of ML enabled patient-specific rods to Australian payors, early in its lifecycle.