Objective To 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 Methods In 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. Results Statistical 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. Conclusion The 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.

中文翻译：

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Lenke 5 青少年特发性脊柱侧凸患者后侧脊柱侧弯手术后近端交界后凸畸形的预测与机器学习

目的通过机器学习建立Lenke 5 青少年特发性脊柱侧凸（AIS）患者接受长后路内固定融合手术的近端交界后凸（PJK）预后模型，并分析PJK的危险因素。材料与方法 回顾性招募2013年1月至2018年12月期间符合纳入标准的44例AIS患者（女性/男性：34/10；PJK/非PJK：34/10）来自华西医院。两名独立研究人员获得了 37 项临床和放射学特征。进行了 PJK 组和非 PJK 组之间的单变量分析。通过使用四种类型的机器学习算法以及两种过采样方法[合成少数技术 (SMOTE) 和随机过采样]，构建了 12 个模型。模型判别采用受试者工作特征曲线下面积（AUC），临床效用采用F1评分和准确度进行评价。通过 Cox 回归和机器学习同时分析风险因素。结果 PJK组与非PJK组的统计学差异如下：性别（p = 0.001）、术前因素[胸椎后凸畸形（p = 0.03）、T1倾斜角（T1S，p = 0.078）]、术后因素[T1S（ p = 0.097)，近端交界角 (p = 0.003)，上器械椎骨 (UIV) – UIV + 1 (p = 0.001)]。使用 SMOTE 的随机森林在独立测试数据集上获得了最佳预测性能，AUC = 0.944，准确度 = 0.909，F1 得分 = 0.667。Cox 模型显示，男性和较大的术前 T1S 是 PJK 的独立预后因素（优势比分别 = 10.701 和 57.074）。性别在表现最佳的模型的重要性排名中也位居第一。结论 采用SMOTE模型的随机森林对Lenke 5 AIS患者长时间内固定融合手术后发生PJK的个体风险具有重要的预测价值。此外，将 Cox 模型的结果与机器学习提取的特征排名相结合，比任何单独一个都更有价值，尤其是在风险因素的解释方面。结论 采用SMOTE模型的随机森林对Lenke 5 AIS患者长时间内固定融合手术后发生PJK的个体风险具有重要的预测价值。此外，将 Cox 模型的结果与机器学习提取的特征排名相结合，比任何单独一个都更有价值，尤其是在风险因素的解释方面。结论 采用SMOTE模型的随机森林对Lenke 5 AIS患者长时间内固定融合手术后发生PJK的个体风险具有重要的预测价值。此外，将 Cox 模型的结果与机器学习提取的特征排名相结合，比任何单独一个都更有价值，尤其是在风险因素的解释方面。