利用机器学习预测微血管减压术后谵妄

Predicting postoperative delirium after microvascular decompression surgery with machine learning.

作者信息

Wang Ying, Lei Lei, Ji Muhuo, Tong Jianhua, Zhou Cheng-Mao, Yang Jian-Jun

机构信息

Department of Anesthesiology and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.

出版信息

J Clin Anesth. 2020 Nov;66:109896. doi: 10.1016/j.jclinane.2020.109896. Epub 2020 Jun 3.

DOI:10.1016/j.jclinane.2020.109896

PMID:32504969

Abstract

OBJECTIVE

The aim of this study was to predict early delirium after microvascular decompression using machine learning.

DESIGN

Retrospective cohort study.

SETTING

Second Hospital of Lanzhou University.

PATIENTS

This study involved 912 patients with primary cranial nerve disease who had undergone microvascular decompression surgery between July 2007 and June 2018.

INTERVENTIONS

None.

MEASUREMENTS

We collected data on preoperative, intraoperative, and postoperative variables. Statistical analysis was conducted in R, and the model was constructed with python. The machine learning model was run using the following models: decision tree, logistic regression, random forest, gbm, and GBDT models.

RESULTS

912 patients were enrolled in this study, 221 of which (24.2%) had postoperative delirium. The machine learning Gbm algorithm finds that the first five factors accounting for the weight of postoperative delirium are CBZ use duration, hgb, serum CBZ level measured 24 h before surgery, preoperative CBZ dose, and BUN. Through machine learning five algorithms to build prediction models, we found the following values for the training group: Logistic algorithm (AUC value = 0.925, accuracy = 0.900); Forest algorithm (AUC value = 0.994, accuracy = 0.948); GradientBoosting algorithm (AUC value = 0.994, accuracy = 0.970) and DecisionTree algorithm (aucvalue = 0.902, accuracy = 0.861); Gbm algorithm (AUC value = 0.979, accuracy = 0.944). The test group had the following values: Logistic algorithm (aucvalue = 0.920, accuracy = 0.901); DecisionTree algorithm (aucvalue = 0.888, accuracy = 0.883); Forest algorithm (aucvalue = 0.963, accuracy = 0.909); GradientBoostingc algorithm (aucvalue = 0.962, accuracy = 0.923); Gbm algorithm (AUC value = 0.956, accuracy = 0.920).

CONCLUSION

Machine learning algorithms predict the occurrence of delirium after microvascular decompression with an accuracy rate of 96.7%. And the major risk factors for the development of post-cardiac delirium are carbamazepine, hgb, and BUN.

摘要

目的

本研究旨在利用机器学习预测微血管减压术后早期谵妄。

设计

回顾性队列研究。

地点

兰州大学第二医院。

患者

本研究纳入了912例原发性颅神经疾病患者，这些患者在2007年7月至2018年6月期间接受了微血管减压手术。

干预措施

无。

测量指标

我们收集了术前、术中和术后变量的数据。在R中进行统计分析，并使用Python构建模型。使用以下模型运行机器学习模型：决策树、逻辑回归、随机森林、gbm和GBDT模型。

结果

本研究共纳入912例患者，其中221例（24.2%）术后出现谵妄。机器学习Gbm算法发现，术后谵妄权重占比前五的因素为卡马西平使用时长、血红蛋白（hgb）、术前24小时测得的血清卡马西平水平、术前卡马西平剂量和血尿素氮（BUN）。通过机器学习的五种算法构建预测模型，我们得到训练组的以下值：逻辑算法（AUC值 = 0.925，准确率 = 0.900）；森林算法（AUC值 = 0.994，准确率 = 0.948）；梯度提升算法（AUC值 = 0.994，准确率 = 0.970）和决策树算法（auc值 = 0.902，准确率 = 0.861）；gbm算法（AUC值 = 0.979，准确率 = 0.944）。测试组的值如下：逻辑算法（auc值 = 0.920，准确率 = 0.901）；决策树算法（auc值 = 0.888，准确率 = 0.883）；森林算法（auc值 = 0.963，准确率 = 0.909）；梯度提升算法（auc值 = 0.962，准确率 = 0.923）；gbm算法（AUC值 = 0.956，准确率 = 0.920）。