Predictive Modeling for Blood Transfusion After Adult Spinal Deformity Surgery: A Tree-Based Machine Learning Approach

Conclusion. This investigation produced tree-based machine-learning models of blood transfusion risk after ASD surgery. The random forest model offered very good predictive capability as measured by AUC. Our single classification tree model offered superior ease of implementation, but a lower AUC as compared to the random forest approach, although this difference was not statistically significant at the size of our validation cohort. Clinicians may choose to implement either of these models to predict blood transfusion among their patients. Furthermore, policy makers may use these models on a population-based level to assess predicted transfusion rates after ASD surgery. Level of Evidence: 3
Source: Spine - Category: Orthopaedics Tags: Deformity Source Type: research