摘要
目的:基于多模态神经网络,构建新型冠状病毒感染(COVID-19)患者继发医院感染的预测模型,为临床患者继发医院感染的防治提供参考.方法:选取 2022 年 8 月 1 日—2023 年 1 月 20 日镇江市第三人民医院收治的 2519例COVID-19患者作为研究对象,收集患者的年龄、既往病史、住院时间、抗菌药物使用、行机械通气等信息,采用多模态神经网络预测模型分析患者继发医院感染的影响因素,并与传统的多因素Logistic回归分析模型进行比较.结果:2 519 例COVID-19 患者中发生医院感染的有 312 例,感染发生率为 12.39%;Logistic回归分析结果显示,COVID-19 患者继发医院感染与年龄、是否有高血压病史和呼吸系统疾病史、是否有经验性使用抗菌药物和免疫抑制剂、是否行机械通气具有相关性(P＜0.05),其中年龄＞65 岁、有高血压病史、有呼吸系统疾病史、住院时间＞7 d、经验性使用抗菌药物、行机械通气是患者继发医院感染的独立危险因素(P＜0.05);多模态神经网络预测结果显示,住院时间、呼吸系统疾病史、年龄、经验性使用抗菌药物和机械通气是患者继发医院感染的 5 个最大的危险因素,其训练样本、检验样本和坚持样本的准确度分别为 87.49%、86.31%和 90.28%;多模态神经网络预测模型和多因素Logistic回归分析模型的接受者操作特征曲线的曲线下面积分别为 0.879 和 0.852,并且Delong检验结果显示二者之间存在统计学差异(P＜0.05).结论:多模态神经网络预测模型和多因素Logistic回归分析模型均可以较好地预测COVID-19 患者继发医院感染的相关风险,但多模态神经网络预测模型的预测结果更好.
Abstract
Objective:To construct a prediction model for secondary nosocomial infections in patients with COVID-19 based on multi-modal neural network,and provide reference for the prevention and treatment of secondary nosocomial infections of clinical patients.Methods:A total of 2 519 patients with COVID-19 admitted to the Third People's Hospital of Zhenjiang from August 1,2022 to January 20,2023 were selected as the research objects,and the information of these patients such as age,past medical history,length of stay(LOS),use of antibacterial drugs,and mechanical ventilation was collected.A multi-modal neural network prediction model was used to analyze the factors influencing the patients'secondary nosocomial infections,and compare it with the conventional multi-factor Logistic regression analysis model.Results:Among the 2 519 patients with COVID-19,312 had nosocomial infections,with an infection incidence of 12.39%;the Logistic regression analysis results showed that,the secondary nosocomial infections in patients with COVID-19 were related to the age,history of hypertension and history of respiratory diseases,empirical use of antibacterial drugs and immunosuppressants,and mechanical ventilation(P<0.05);among which,the age>65 years,the presence of history of hypertension,the presence of history of respiratory diseases,LOS>7 days,empiric use of antibacterial drugs,and mechanical ventilation were independent risk factors for secondary nosocomial infections in patients(P<0.05);the multi-modal neural network prediction results showed that,the LOS,history of respiratory diseases,age,empirical use of antibacterial drugs,and mechanical ventilation were the five major risk factors of secondary nosocomial infections in patients.The accuracy of the training samples,test samples and persistence samples was 87.49%,86.31%and 90.28%respectively;the areas under the curve of receiver operating characteristic curves of the multi-modal neural network prediction model and multi-factor Logistic regression analysis model were 0.879 and 0.852 respectively,and the Delong test results showed there was a statistical difference between them(P<0.05).Conclusion:Both the multi-modal neural network prediction model and the multi-factor Logistic regression analysis model can be used to predict the risk of secondary nosocomial infections in patients with COVID-19,but the multi-modal neural network prediction model has better prediction results.
基金项目
国家重点研发计划(2020YFE0205100)
镇江市社会发展指导性科技计划项目(FZ2022104)
镇江市重点研发计划(社会发展)面上项目(SH2021075)
NETL
NSTL
万方数据