Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning

Abstract

Objective: Although adult spinal deformity (ASD) surgery aims to restore and maintain align-ment, proximal junctional kyphosis (PJK) may occur. While existing scoring systems predict PJK, they predominantly offer a generalized 3-tier risk classification, limiting their utility for nuanced treatment decisions. This study seeks to establish a personalized risk calculator for PJK, aiming to enhance treatment planning precision. Methods: Patient data for ASD were sourced from the Korean spinal deformity database. PJK was defined a proximal junctional angle (PJA) of >= 20 degrees at the final follow-up, or an increase in PJA of >= 10 degrees compared to the preoperative values. Multivariable analysis was performed to identify independent variables. Subsequently, 5 machine learning models were created to pre-dict individualized PJK risk post-ASD surgery. The most efficacious model was deployed as an online and interactive calculator. Results: From a pool of 201 patients, 49 (24. 4%) exhibited PJK during the follow-up period. Through multivariable analysis, postoperative PJA, body mass index, and deformity type emerged as independent predictors for PJK. When testing machine learning models using study results and previously reported variables as hyperparameters, the random forest model exhibited the highest accuracy, reaching 83%, with an area under the receiver operating char-acteristics curve of 0.76. This model has been launched as a freely accessible tool at: (https:// snuspine.shinyapps.io/PJKafterASD/). Conclusion: An online calculator, founded on the random forest model, has been developed to gauge the risk of PJK following ASD surgery. This may be a useful clinical tool for surgeons, allowing them to better predict PJK probabilities and refine subsequent therapeutic strategies.