Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning
Chang-Hyun Lee,
Dae-Jean Jo,
Jae Keun Oh,
Seung-Jae Hyun,
Jin Hoon Park,
Kyung Hyun Kim,
Jun Seok Bae,
Bong Ju Moon,
Chang-Kyu Lee,
Myoung Hoon Shin,
Hyun Jun Jang,
Moon-Soo Han,
Chi Heon Kim,
Chun Kee Chung,
Seung-Myung Moon,
Affiliations
Chang-Hyun Lee
Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
Dae-Jean Jo
Department of Neurosurgery, Kyung Hee University Hospital at Gangdong, Seoul, Korea
Jae Keun Oh
Department of Neurosurgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
Seung-Jae Hyun
Department of Neurosurgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
Jin Hoon Park
Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
Kyung Hyun Kim
Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
Jun Seok Bae
Wooridul Spine Hospital, Seoul, Korea
Bong Ju Moon
Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
Chang-Kyu Lee
Department of Neurosurgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
Myoung Hoon Shin
Department of Neurosurgery, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon, Korea
Hyun Jun Jang
Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
Moon-Soo Han
Department of Neurosurgery, Chonnam National University Research Institute of Medical Sciences, Chonnam National University Hospital & Medical School, Gwangju, Korea
Chi Heon Kim
Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
Chun Kee Chung
Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
Seung-Myung Moon
Department of Neurosurgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
Objective Although adult spinal deformity (ASD) surgery aims to restore and maintain alignment, 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° at the final follow-up, or an increase in PJA of ≥ 10° compared to the preoperative values. Multivariable analysis was performed to identify independent variables. Subsequently, 5 machine learning models were created to predict 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 characteristics 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.
