Systematic review and evaluation of predictive modeling algorithms in spinal surgeries.

Abstract In order to better educate patients, predictive models have been implemented to stratify surgical risk, thereby instituting greater uniformity across surgical practices and prioritizing the safety and outcomes of patients. The purpose of this study is to conduct a systematic review summarizing the major predictive models used to evaluate patients as candidates for spinal surgery. A search was conducted for articles related to predictive modeling in spinal surgeries using PubMed, MEDLINE, and Scopus databases. Papers with area under the receiver operating curve (AUROC) scores reported were included in the analysis. Models not relevant to spinal procedures were excluded. Comparison between models was only attainable for those that reported AUROCs for individual procedures. Based on a combination of AUROC scores and demonstrated applicability to spinal procedures, the models by Scheer et al. (0.89), Ratliff et al. (0.70), the Seattle Spine Score (0.712), Risk Assessment Tool (0.67–0.7), and the Spine Sage calculator (0.81–0.85) were determined to be ideal for predictive modeling in spinal surgeries and were subsequently broken down into their individual inputs and outputs to determine what elements a theoretical model should assimilate. Alongside the model by Scheer et al., the Spine Sage calculator, Seattle Spine Score, Risk Assessment Tool, and a model by Ratliff et al. showed the most promise for patients undergoing spinal procedures. Using the first model as a springboard, new spinal predictive models can be optimized through use of larger prospective databases, with longer follow-up times, and greater inclusion of reliable high impact variables.