Heo Kevin Y, Rajan Prashant V, Khawaja Sameer, Barber Lauren A, Yoon Sangwook Tim
Department of Orthopaedic Surgery, Emory University School of Medicine, Atlanta, GA, USA.
J Spine Surg. 2024 Sep 23;10(3):362-371. doi: 10.21037/jss-24-15. Epub 2024 Aug 23.
Acute kidney injury (AKI) after spinal fusion is a significant morbidity that can lead to poor post-surgical outcomes. Identifying AKI risk factors and developing a risk model can raise surgeons' awareness and allow them to take actions to mitigate the risks. The objective of the current study is to develop machine learning (ML) models to assess patient risk factors predisposing to AKI after posterior spinal instrumented fusion.
Data was collected from the IBM MarketScan Database (2009-2021) for patients >18 years old who underwent spinal fusion with posterior instrumentation (3-6 levels). AKI incidence (defined by the International Classification of Diseases codes) was recorded 90-day post-surgery. Risk factors for AKI were investigated and compared through several ML models including logistic regression, linear support vector machine (LSVM), random forest, extreme gradient boosting (XGBoost), and neural networks.
Among the 141,697 patients who underwent fusion with posterior instrumentation (3-6 levels), the overall rate of 90-day AKI was 2.96%. We discovered that the logistic regression model and LSVM demonstrated the best predictions with area under the curve (AUC) values of 0.75. The most important AKI prediction features included chronic renal disease, hypertension, diabetes mellitus ± complications, older age (>50 years old), and congestive heart failure. Patients who did not have these five key risk factors had a 90-day AKI rate of 0.29%. Patients who had an increasing number of key risk factors subsequently had higher risks of postoperative AKI.
The analysis of the data with different ML models identified 5 key variables that are most closely associated with AKI: chronic renal disease, hypertension, diabetes mellitus ± complications, older age (>50 years old), and congestive heart failure. These variables constitute a simple risk calculator with additive odds ratio ranging from 3.38 (1 risk factor) to 91.10 (5 risk factors) over 90 days after posterior spinal fusion surgery. These findings can help surgeons risk-stratify their patients for AKI risk, and potentially guide post-operative monitoring and medical management.
脊柱融合术后的急性肾损伤（AKI）是一种严重的并发症，可导致术后不良结局。识别AKI风险因素并建立风险模型可以提高外科医生的认识，并使他们能够采取措施降低风险。本研究的目的是开发机器学习（ML）模型，以评估后路脊柱内固定融合术后发生AKI的患者风险因素。
从IBM MarketScan数据库（2009 - 2021年）收集年龄大于18岁、接受后路器械辅助脊柱融合术（3 - 6个节段）患者的数据。术后90天记录AKI发病率（根据国际疾病分类代码定义）。通过包括逻辑回归、线性支持向量机（LSVM）、随机森林、极端梯度提升（XGBoost）和神经网络在内的几种ML模型研究并比较AKI的风险因素。
在141,697例行后路器械辅助融合术（3 - 6个节段）的患者中，90天AKI的总体发生率为2.96%。我们发现逻辑回归模型和LSVM表现出最佳预测效果，曲线下面积（AUC）值为0.75。最重要的AKI预测特征包括慢性肾病、高血压、糖尿病±并发症、老年（>50岁）和充血性心力衰竭。没有这五个关键风险因素的患者90天AKI发生率为0.29%。具有关键风险因素数量增加的患者随后发生术后AKI的风险更高。
使用不同ML模型对数据进行分析，确定了与AKI最密切相关的5个关键变量：慢性肾病、高血压、糖尿病±并发症、老年（>50岁）和充血性心力衰竭。这些变量构成了一个简单的风险计算器，后路脊柱融合术后90天内的相加比值比范围为3.38（1个风险因素）至91.10（5个风险因素）。这些发现可以帮助外科医生对患者的AKI风险进行分层，并可能指导术后监测和医疗管理。
