Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient.

Li Peng,Xiaoyan Yang,Lan Lan,Yonghong Gu,Bowen Hu,Rui Yang,Guangming Zhang,Peng Xiu,Xi Yang,Yueming Song,Xiaobo Zhou

doi:10.3389/fbioe.2020.559387

Abstract

ObjectiveTo build a model for proximal junctional kyphosis (PJK) prognostication in Lenke 5 adolescent idiopathic scoliosis (AIS) patients undergoing long posterior instrumentation and fusion surgery by machine learning and analyze the risk factors for PJK.Materials and MethodsIn total, 44 AIS patients (female/male: 34/10; PJK/non-PJK: 34/10) who met the inclusion criteria between January 2013 and December 2018 were retrospectively recruited from West China Hospital. Thirty-seven clinical and radiological features were acquired by two independent investigators. Univariate analyses between PJK and non-PJK groups were carried out. Twelve models were built by using four types of machine learning algorithms in conjunction with two oversampling methods [the synthetic minority technique (SMOTE) and random oversampling]. Area under the receiver operating characteristic curve (AUC) was used for model discrimination, and the clinical utility was evaluated by using F1 score and accuracy. The risk factors were simultaneously analyzed by a Cox regression and machine learning.ResultsStatistical differences between PJK and non-PJK groups were as follows: gender (p = 0.001), preoperative factors [thoracic kyphosis (p = 0.03), T1 slope angle (T1S, p = 0.078)], and postoperative factors [T1S (p = 0.097), proximal junctional angle (p = 0.003), upper instrumented vertebra (UIV) – UIV + 1 (p = 0.001)]. Random forest using SMOTE achieved the best prediction performance with AUC = 0.944, accuracy = 0.909, and F1 score = 0.667 on independent testing dataset. Cox model revealed that male gender and larger preoperative T1S were independent prognostic factors of PJK (odds ratio = 10.701 and 57.074, respectively). Gender was also at the first place in the importance ranking of the model with best performance.ConclusionThe random forest using SMOTE model has the great value for predicting the individual risk of developing PJK after long instrumentation and fusion surgery in Lenke 5 AIS patients. Moreover, the combination of the outcomes of a Cox model and the feature ranking extracted by machine learning is more valuable than any one alone, especially in the interpretation of risk factors.

Highlights

For adolescent idiopathic scoliosis (AIS) patients, orthopedic operations are employed to reconstruct the coronal and sagittal alignment in an attempt to maintain the stability of the spine (Mimura et al, 2017)
The clinical utility of the model was evaluated with F1 score, FIGURE 2 | Graphic representations of special angles of an adolescent idiopathic scoliosis patient with Proximal junctional kyphosis (PJK) postoperatively
The average accuracies of machine learning models without oversampling for predicting PJK occurrence in the train and test sets were 0.728 and 0.783, whereas, models trained with random oversampling (ROS) were 0.80 and 0.73, and models with synthetic minority technique (SMOTE) were 0.82 and 0.78, respectively

Summary

Introduction

For adolescent idiopathic scoliosis (AIS) patients, orthopedic operations are employed to reconstruct the coronal and sagittal alignment in an attempt to maintain the stability of the spine (Mimura et al, 2017). Proximal junctional kyphosis (PJK), a multifactorial proximal adjacent segment disease following fusion treatment, has drawn the attention of many spine surgeons (Watanabe et al, 2010; Kim et al, 2013). It affects around 28% of the adolescent idiopathic scoliosis (AIS) population, with regional pain and poor life quality in some severe cases (Kim et al, 2007; O’Shaughnessy et al, 2012; Passias et al, 2018; Sebaaly et al, 2018). The most commonly adopted definition of PJK is accepted in this study: the Cobb angle between the upper instrumented vertebra (UIV) and the two supra-adjacent vertebrae is superior to 10◦ and at least 10◦ greater than its preoperative value (Glattes et al, 2005)

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Conclusion