Construction and validation of a prediction model of aspiration risk of acute poisoning patients during gastric lavage
10.3761/j.issn.0254-1769.2024.17.008
- VernacularTitle:急性中毒患者洗胃期间误吸风险预测模型的构建及验证
- Author:
Shuoni ZHANG
1
;
Junjie WANG
;
Bo ZHANG
;
Xuelan LIU
Author Information
1. 315000 宁波市医疗中心李惠利医院急诊科
- Keywords:
Acute Poisoning;
Gastric Lavage;
Aspiration Risk;
Predictive Model;
Nursing Care
- From:
Chinese Journal of Nursing
2024;59(17):2100-2107
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the influencing factors of aspiration risk in patients with acute poisoning during gastric lavage,and to build and validation a prediction model of aspiration risk in patients with acute poisoning during gastric lavage.Methods Through literature search and analysis,the risk factors of aspiration during gastric lavage was summarized in patients with acute poisoning.A retrospective study was conducted on patients with acute poisoning in the emergency department of a tertiary A general hospital in Ningbo from January 2020 to June 2023.Through R 4.2.1 and Python 3.11 programming language,the random forest,logistic regression,extreme gradient boosting tree and gradient boosting decision tree algorithms in machine learning were used to establish a prediction model of aspiration risk during gastric lavage in patients with acute poisoning and carry out internal verification.The prediction effects of the 4 prediction models were evaluated by confusion matrix,calibration curve,receiver operating characteristic curve,area under curve,Kolmogorov-Smirnov value,accuracy,precision,recall rate and F1 score,and the best model was selected.Results The modeling results of the 4 machine learning algorithms show that the area under the curve of the Random Forest,Logistic Regression,Extreme Gradient Boosting Tree,and Gradient Boosting Decision Tree algorithms are 0.954(0.934～0.974),0.878(0.843～0.913),0.910(0.880～0.939),and 0.917(0.889～0.945),respectively.The internal validation results show that the area under the curve of the random forest,logistic regression,extreme gradient boosting tree,and gradient boosting decision tree algorithms are 0.910(0.864～0.955),0.877(0.824～0.931),0.849(0.790～0.908),and 0.873(0.819～0.928),respectively.Age,state of consciousness,D-dimer and the time of absorption of poison are the 3 characteristics that are particularly prominent in the order of importance of the influencing factors of aspiration during gastric lavage in patients with acute poisoning.Conclusion Among the 4 prediction models,random forest model has better prediction effect,with good discrimination ability for the risk of aspiration during gastric lavage in patients with acute poisoning,and it is convenient for clinical use,which can provide references for medical staff to take preventive treatment and care.
