探讨人工神经网络机器学习模型准确预测后路腰椎融合术后并发症的能力。
Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion.
机构信息
Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY.
Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY.
出版信息
Spine (Phila Pa 1976). 2018 Jun 15;43(12):853-860. doi: 10.1097/BRS.0000000000002442.
STUDY DESIGN
A cross-sectional database study.
OBJECTIVE
The aim of this study was to train and validate machine learning models to identify risk factors for complications following posterior lumbar spine fusion.
SUMMARY OF BACKGROUND DATA
Machine learning models such as artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex datasets. ANNs have yet to be used for risk factor analysis in orthopedic surgery.
METHODS
The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent posterior lumbar spine fusion. This query returned 22,629 patients, 70% of whom were used to train our models, and 30% were used to evaluate the models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society for Anesthesiology (ASA) class ≥3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating curves (AUC) was used to determine the accuracy of our machine learning models.
RESULTS
On the basis of AUC values, ANN and LR both outperformed ASA class for predicting all four types of complications. ANN was the most accurate for predicting cardiac complications, and LR was most accurate for predicting wound complications, VTE, and mortality, though ANN and LR had comparable AUC values for predicting all types of complications. ANN had greater sensitivity than LR for detecting wound complications and mortality.
CONCLUSION
Machine learning in the form of logistic regression and ANNs were more accurate than benchmark ASA scores for identifying risk factors of developing complications following posterior lumbar spine fusion, suggesting they are potentially great tools for risk factor analysis in spine surgery.
LEVEL OF EVIDENCE
研究设计
一项横断面数据库研究。
目的
本研究旨在训练和验证机器学习模型，以确定后路腰椎融合术后并发症的危险因素。
背景资料概要
机器学习模型，如人工神经网络（ANNs），是分析和解释大型复杂数据集的有价值的工具。ANN 尚未用于骨科手术的危险因素分析。
方法
美国外科医师学会国家手术质量改进计划（ACS-NSQIP）数据库中检索接受后路腰椎融合术的患者。该查询返回了 22629 名患者，其中 70%用于训练我们的模型，30%用于评估模型。使用的预测变量包括性别、年龄、种族、糖尿病、吸烟、类固醇使用、凝血障碍、功能状态、美国麻醉师协会（ASA）分级≥3、体重指数（BMI）、肺部合并症和心脏合并症。模型用于预测心脏并发症、伤口并发症、静脉血栓栓塞（VTE）和死亡率。使用 ASA 分级作为预测的基准，使用接收器操作曲线下面积（AUC）来确定我们的机器学习模型的准确性。
结果
基于 AUC 值，ANN 和 LR 均优于 ASA 分级，可预测所有四种类型的并发症。ANN 对预测心脏并发症最准确，LR 对预测伤口并发症、VTE 和死亡率最准确，尽管 ANN 和 LR 对预测所有类型的并发症的 AUC 值相当。ANN 在检测伤口并发症和死亡率方面的敏感性高于 LR。
结论
以逻辑回归和 ANNs 形式表示的机器学习比基准 ASA 评分更能准确识别后路腰椎融合术后并发症的危险因素，这表明它们是脊柱外科危险因素分析的潜在强大工具。
证据水平
3 级。
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