Zhang Bo, Xu Huanqing, Xiao Qigui, Wei Wanzhen, Ma Yifei, Chen Xinlong, Gu Jingtao, Zhang Jiaoqiong, Lang Lan, Ma Qingyong, Han Liang
Department of Hepatobiliary Surgery, The First Affiliated Hospital, Xi'an Jiaotong University, NO.277 Yanta West Road, Xi'an, China.
School of Medical Information Engineering, Anhui University of Traditional Chinese Medicine, Hefei, Anhui Province, China.
Heliyon. 2024 Nov 17;10(23):e40236. doi: 10.1016/j.heliyon.2024.e40236. eCollection 2024 Dec 15.
The aim of this study was to build and validate a risk prediction model for aspiration in severe acute pancreatitis patients receiving early enteral nutrition (EN) by identifying risk factors for aspiration in these patients.
The risk factors for aspiration were analyzed to build a prediction model based on the data collected from 339 patients receiving enteral nutrition. Subsequently, we used six machine learning algorithms and the model was validated by the area under the curve.
In this study, the collected data were divided into two groups: a training cohort and a validation cohort. The results showed that 28.31 % (77) of patients had aspiration and 71.69 % (195) of patients had non-aspiration in training cohort. Moreover, age, consciousness, mechanical ventilation, aspiration history, nutritional risk and number of comorbidities were included as predictive factors for aspiration in patients receiving EN. The XGBoost model is the best of all machine learning models, with an AUROC of 0.992 and an F1 value of 0.902. The specificity and accuracy of XGBoost are higher than those of traditional logistic regression.
In accordance with the predictive factors, XGBoost model, characterized by excellent discrimination and high accuracy, can be used to clinically identify severe acute pancreatitis patients with a high risk of enteral nutrition aspiration.
This study contributed to the development of a predictive model for early enteral nutrition aspiration in severe acute pancreatitis patients during hospitalization that can be shared with medical staff and patients in the future.
This is a retrospective cohort study, and no patient or public contribution was required to design or undertake this research.
本研究旨在通过识别接受早期肠内营养（EN）的重症急性胰腺炎患者发生误吸的危险因素，构建并验证该类患者误吸的风险预测模型。
基于从339例接受肠内营养的患者收集的数据，分析误吸的危险因素以构建预测模型。随后，我们使用六种机器学习算法，并通过曲线下面积对模型进行验证。
在本研究中，收集的数据分为两组：训练队列和验证队列。结果显示，训练队列中28.31%（77例）患者发生误吸，71.69%（195例）患者未发生误吸。此外，年龄、意识、机械通气、误吸史、营养风险和合并症数量被纳入接受EN患者误吸的预测因素。XGBoost模型是所有机器学习模型中表现最佳的，其曲线下面积（AUROC）为0.992，F1值为0.902。XGBoost的特异性和准确性高于传统逻辑回归。
根据预测因素，以出色的区分度和高准确性为特征的XGBoost模型可用于临床识别有肠内营养误吸高风险的重症急性胰腺炎患者。
本研究有助于开发一种针对重症急性胰腺炎患者住院期间早期肠内营养误吸的预测模型，该模型未来可与医护人员和患者共享。
这是一项回顾性队列研究，设计或开展本研究无需患者或公众参与。
