Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA.
Department of Pediatrics, UT Health San Antonio, San Antonio, TX, USA.
Int Orthop. 2024 Jul;48(7):1887-1896. doi: 10.1007/s00264-024-06194-5. Epub 2024 May 3.
This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical site infection in patients with lower extremity fractures.
A machine learning analysis was conducted on a dataset comprising 1,579 patients who underwent surgical fixation for lower extremity fractures to create a predictive model for risk stratification of postoperative surgical site infection. We evaluated different clinical and demographic variables to train four machine learning models (neural networks, boosted generalised linear model, naïve bayes, and penalised discriminant analysis). Performance was measured by the area under the curve score, Youdon's index and Brier score. A multivariate adaptive regression splines (MARS) was used to optimise predictor selection.
The final model consisted of five predictors. (1) Operating room time, (2) ankle region, (3) open injury, (4) body mass index, and (5) age. The best-performing machine learning algorithm demonstrated a promising predictive performance, with an area under the ROC curve, Youdon's index, and Brier score of 77.8%, 62.5%, and 5.1%-5.6%, respectively.
The proposed predictive model not only assists surgeons in determining high-risk factors for surgical site infections but also empowers patients to closely monitor these factors and take proactive measures to prevent complications. Furthermore, by considering the identified predictors, this model can serve as a reference for implementing preventive measures and reducing postoperative complications, ultimately enhancing patient outcomes. However, further investigations involving larger datasets and external validations are required to confirm the reliability and applicability of our model.
本研究旨在开发机器学习算法，以识别与下肢骨折患者术后手术部位感染风险相关的预测因素。
对 1579 名接受下肢骨折手术固定的患者数据集进行机器学习分析，以创建用于术后手术部位感染风险分层的预测模型。我们评估了不同的临床和人口统计学变量，以训练四种机器学习模型（神经网络、增强广义线性模型、朴素贝叶斯和惩罚判别分析）。通过曲线下面积评分、Youdon 指数和 Brier 评分来衡量性能。使用多变量自适应回归样条（MARS）来优化预测因子选择。
最终模型由五个预测因子组成。（1）手术时间，（2）踝关节区域，（3）开放性损伤，（4）体重指数，（5）年龄。表现最佳的机器学习算法表现出有前途的预测性能，ROC 曲线下面积、Youdon 指数和 Brier 评分分别为 77.8%、62.5%和 5.1%-5.6%。
该预测模型不仅可以帮助外科医生确定手术部位感染的高风险因素，还可以使患者密切监测这些因素并采取积极措施预防并发症。此外，通过考虑确定的预测因子，该模型可以作为实施预防措施和减少术后并发症的参考，从而最终改善患者的预后。然而，需要进一步的研究，包括更大的数据集和外部验证，以确认我们模型的可靠性和适用性。
