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基于机器学习算法构建重症急性胰腺炎病人肠内营养误吸风险的预测模型 被引量:10

Establishment and validation of early enteral nutrition aspiration risk prediction model for patients with severe acute pancreatitis based on machine learning algorithm
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摘要 目的探讨重症急性胰腺炎病人早期肠内营养误吸发生的独立危险因素,基于机器学习算法构建误吸风险的预测模型。方法我院2012年1月~2019年12月收治的重症急性胰腺炎并行早期肠内营养病人296例,其中未发生误吸268例,发生误吸28例。比较两组病人性别、年龄、体质量指数、急性生理与慢性健康评分(APACHE-II评分)、意识状况、营养风险、鼻饲管置入长度、中性粒细胞/淋巴细胞比值和血小板/淋巴细胞比值等数据。将误吸危险因素分别导入随机森林、神经网络、决策树、支持向量机和广义线性回归算法,建立5种预测模型并检验模型的预测效能。结果APACHE-II评分、意识状况、营养风险、鼻饲管置入长度和血小板/淋巴细胞比值是预测早期肠内营养误吸发生的危险因素。随机森林、神经网络、决策树、支持向量机和广义线性回归算法曲线下面积分别为0.976、0.973、0.961、0.932和0.921,其中随机森林算法的预测效能最佳。结论基于机器学习算法建立的预测模型可准确预测重症急性胰腺炎病人早期肠内营养误吸发生的风险,有利于并发症的预测评估及临床决策的制定。 Objective To explore the independent risk factors of early enteral nutrition aspiration in patients with severe acute pancreatitis(SAP)based on deep learning,and to establish a prediction model to predict the risk of early enteral nutrition aspiration.Methods The clinical data of 296 patients with severe acute pancreatitis in Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of science and technology from January 2012 to December 2019 were retrospectively analyzed,including 268 patients without aspiration and 28 patients with aspiration.The clinical data of gender,age,body mass index,APACHE II score,consciousness,nutritional risk,nasogastric tube length,neutrophil-lymphocyte ratio(NLR)and platelet-lymphocyte ratio(PLR)were compared between the two groups Support vector machine(SVM)and generalized linear regression(GLR)algorithm were used to establish five prediction models to obtain the importance of prediction variables.At the same time,the subject work curve and decision curve were drawn to test the predictive value of the model.Results APACHE-II score,consciousness,nutritional risk,length of nasogastric feeding tube and PLR were the related variables to predict early enteral nutrition aspiration.The areas under the curve of random forest,neural network,decision tree,support vector machine and generalized linear regression algorithm were 0.976,0.973,0.961,0.932 and 0.921,respectively.Through comparison,the performance of random forest algorithm was the best.Conclusion The prediction model based on machine learning algorithm can accurately predict the possibility of early enteral nutrition aspiration in patients with severe acute pancreatitis,which is conducive to postoperative evaluation and clinical nursing decision-making.
作者 官艳 张国娇 罗茵 GUAN Yan;ZHANG Guojiao;LUO Yin(Hepatic Surgery Center,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China;不详)
出处 《临床外科杂志》 2022年第7期634-638,共5页 Journal of Clinical Surgery
关键词 重症急性胰腺炎 肠内营养 误吸风险 机器学习 危险因素 severe acute pancreatitis enteral nutrition risk of aspiration machine learning risk factors
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