Zhu Yu, Zhou Ning, Liang Renrui, Yang Jian-Jun, Zhou Cheng-Mao
Department of Anaesthesiology and Nuring, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.
Department of Emergency, Central People's Hospital of Zhanjiang, Guangdong, China.
BMC Neurol. 2025 Sep 2;25(1):373. doi: 10.1186/s12883-025-04389-w.
Postoperative delirium (POD) is a common occurrence following orthopedic surgery, particularly in the older population. However, there is a relative scarcity of research on the use of intelligent algorithms to predict POD in older patients after orthopedic surgery. Therefore, the objective of this study was to evaluate the efficacy of ten distinct intelligent algorithms in predicting POD in older patients undergoing femoral neck fracture surgery.
This study selected ten advanced artificial intelligence algorithms to predict the occurrence of postoperative delirium by analyzing patient data.
A total of 1889 patients were included in this study. The dataset for this study was divided into a training dataset, which consisted of synthetic data, and a testing dataset, representing real-world clinical scenarios. In the training dataset, we identified 267 cases of POD, accounting for 26.70% of the group. In the testing dataset, 172 cases of POD were confirmed, representing 19.35% of the group. Analysis using the Gradient Boosting Decision Tree (GBDT) algorithm revealed that age, preoperative hemoglobin levels, duration of anesthesia, and intraoperative blood loss are key predictive factors for POD in older patients with femoral neck fractures. Among the intelligent algorithms tested for predicting POD in the testing group, logistic regression, random forest, and the Multilayer Perceptron Classifier (MLPC) performed best with accuracy rates of 0.810, 0.810, and 0.808, respectively. In terms of precision, MLPC led with a score of 1.000, followed by random forest (0.714) and logistic regression (0.548). The highest recall rates were achieved by Gaussian Naive Bayes (gnb, 0.337) and AdaBoost (adab, 0.198). Gaussian Naive Bayes also performed best in F1 score (0.244). In the evaluation of the Area Under the Curve (AUC), logistic regression, MLPC, and XGBoost (XGB) demonstrated the best performance, with values of 0.669, 0.669, and 0.652, respectively.
The results of this study indicate that the Multilayer Perceptron Classifier (MLPC) algorithm performed the most excellently in predicting POD after femoral neck fracture surgery in older adults, with an accuracy rate reaching 80.8%. These findings suggest that machine learning algorithms, particularly MLPC, have significant potential and practical effectiveness in predicting POD in specific older patient populations.
The online version contains supplementary material available at 10.1186/s12883-025-04389-w.
术后谵妄（POD）是骨科手术后的常见情况，尤其是在老年人群中。然而，关于使用智能算法预测老年患者骨科手术后POD的研究相对较少。因此，本研究的目的是评估十种不同智能算法在预测老年股骨颈骨折手术患者POD方面的疗效。
本研究选择了十种先进的人工智能算法，通过分析患者数据来预测术后谵妄的发生。
本研究共纳入1889例患者。本研究的数据集分为由合成数据组成的训练数据集和代表真实临床场景的测试数据集。在训练数据集中，我们确定了267例POD病例，占该组的26.70%。在测试数据集中，确诊了172例POD病例，占该组的19.35%。使用梯度提升决策树（GBDT）算法分析显示，年龄、术前血红蛋白水平、麻醉持续时间和术中失血量是老年股骨颈骨折患者POD的关键预测因素。在测试组中用于预测POD的智能算法中，逻辑回归、随机森林和多层感知器分类器（MLPC）表现最佳，准确率分别为0.810、0.810和0.808。在精度方面，MLPC得分最高，为1.000，其次是随机森林（0.714）和逻辑回归（0.548）。高斯朴素贝叶斯（gnb，0.337）和AdaBoost（adab，0.198）的召回率最高。高斯朴素贝叶斯在F1得分方面也表现最佳（0.244）。在曲线下面积（AUC）评估中，逻辑回归、MLPC和XGBoost（XGB）表现最佳，值分别为0.669、0.669和0.652。
本研究结果表明，多层感知器分类器（MLPC）算法在预测老年股骨颈骨折手术后的POD方面表现最为出色，准确率达到80.8%。这些发现表明，机器学习算法，特别是MLPC，在预测特定老年患者群体的POD方面具有巨大潜力和实际效果。
在线版本包含可在10.1186/s12883-025-04389-w获取的补充材料。
