护理与康复2025，Vol.24Issue(1)：9-14,6.DOI:10.3969/j.issn.1671-9875.2025.01.002
利用分类树模型构建危重新生儿预后预警模型
Construction of a prognostic early warning model for critically ill neonates using a classification tree model
摘要
Abstract
Objective To construct a prognostic early warning model for critically ill neonates using a classification tree model,assisting clinical medical staff in making more scientific and rational judgments and decisions in complex clinical situa-tions,and implementing effective prevention and treatment measures to reduce the risk of neonatal mortality.Methods A total of 143 critically ill neonates admitted to the pediatrics department of Linping Campus,the Second Affiliated Hospital Zhejiang University School of Medicine between January 2021 and July 2023 were selected as study subjects.Based on hospi-talization outcomes,they were divided into a good prognosis group(n=105)and a poor prognosis group(n=38).The clini-cal data of the two groups were compared,and a classification tree model was used to construct a prognostic early warning model for critically ill neonates.The model's predictive ability was evaluated using sensitivity,specificity,predictive accura-cy,and the area under the receiver operating characteristic curve.Results The proportions of small for gestational age in-fant,fetal distress,1-minute Apgar score＜4,5-minute Apgar score＜4,PaO2＜50 mmHg,base excess＜-7 mmol/L,maternal age≥35 years,and abnormal amniotic fluid were significantly higher in the poor prognosis group than in the good prognosis group(P＜0.05).Multivariate logistic regression analysis indicated that 1-minute Apgar score＜4,PaO2＜50 mmHg,base excess＜-7 mmol/L,small for gestational age infant,fetal distress,maternal age≥35 years,and abnor-mal amniotic fluid were significant factors included in the regression equation(P＜0.05).The constructed classification tree model for the prognostic early warning of critically ill neonates comprised 4 levels,17 nodes,and 9 terminal nodes,identi-fying 7 explanatory variables,which were 1-minute Apgar score,PaO2,base excess,small for gestational age infant,fetal distress,maternal age,and abnormal amniotic fluid.The model's risk rate was 0.187,indicating good model fit.Conclusion The classification tree model can effectively fit the predictive risk factors for the prognosis of critically ill neo-nates and has good predictive value for their prognosis.关键词
危重症/新生儿/预后/危险因素/分类树/预警模型Key words
critical illness/neonates/prognosis/risk factors/classification tree/early warning model分类
医药卫生引用本文复制引用
陈云杰,胡忠华,梁晓燕,戚利那..利用分类树模型构建危重新生儿预后预警模型[J].护理与康复,2025,24(1):9-14,6.基金项目
杭州市医药卫生科技项目,编号B20230024 （）
