基于机器学习决策树模型对急性百草枯中毒患者预后的预测价值  

Predictive value of decision tree-based machine learning model for prognosis in acute paraquat poisoning

作　　者：吕广卫 冯顺易[1] 李勇[1] 王剑[1] Lyu Guangwei;Feng Shunyi;Li Yong;Wang Jian(Department of Emergency,Cangzhou Central Hospital,Cangzhou 061000,Hebei,China)

机构地区：[1]沧州市中心医院急诊医学部,河北沧州061000

出　　处：《中国中西医结合急救杂志》2024年第1期63-67,共5页Chinese Journal of Integrated Traditional and Western Medicine in Intensive and Critical Care

基　　金：河北省沧州市科学技术研究与发展指导计划项目(172302092)。

摘　　要：目的探讨基于机器学习的决策树模型对急性百草枯(PQ)中毒(APP)患者预后的预测价值。方法采用回顾性研究方法。收集2012年5月至2021年8月沧州市中心医院急诊医学部救治的APP患者的临床数据,包括性别、年龄、服毒至洗胃的时间、血液灌流比例、血PQ浓度、生化指标[白细胞计数(WBC)丙氨酸转氨酶(ALT)血肌酐(SCr)血淀粉酶及血钾]以及血气指标[动脉血乳酸(Lac)、剩余碱和动脉血氧分压(PaO_(2))]。根据中毒后90d预后将患者分为生存组(56例)和死亡组(74例),比较不同预后两组患者临床指标的差异。通过多因素Logistic回归分析影响APP患者预后的危险因素,将危险因素作为变量构建含血PQ浓度及不含血PQ浓度的两种决策树模型。绘制受试者工作特征曲线(ROC曲线)评估决策树模型对APP患者预后的预测价值,通过Hanley&McNeil法对两种决策树模型的ROC曲线下面积(AUC)进行比较。结果患者90d的生存率为43.1%(56/130)。与死亡组比较,生存组WBC[×10^(9)/L:8.9(7.0,11.6)比17.4(11.9,23.1)]、ALT[U/L:25.3(21.2,31.8)比29.3(23.2,40.3)]、SCr[μmol/L:64.0(53.0,74.0)比91.0(72.5,141.5)]、Lac[mmol/L:2.5(1.4,4.0)比7.1(3.7,11.0)]和血PQ浓度[ng/L:0.3(0.10.9)比2.9(1.9,8.1)]均较低,差异均有统计学意义(均P<0.05),剩余碱[mmol/L:-2.5(-4.2,-1.1)比-7.2(-10.9,-4.7)]和血钾[mmol/L:3.7(3.5,4.0)比3.2(2.83.7)]均较高,差异均有统计学意义(均P<0.05),患者更年轻[岁:33.5(26.0,47.8)比42.5(26.0,58.0),P<0.05]。单因素Logistic回归分析结显示,年龄、WBC、ALT、SCr、血钾、Lac、剩余碱和血PQ浓度是影响APP患者90d预后的独立危险因素[优势比(0R)和95%可信区间(95%CI)分别为1.03(1.01~1.05)1.30(1.18~1.44)1.04(1.01~1.07)、1.02(1.01~1.04)、7.59(3.25~17.70)、1.64(1.35~1.99)、1.51(1.29~1.76)、7.00(3.41~14.37),P值分别为0.018、<0.001、0.011、<0.001、<0.001、<0.001、<0.001、<0.001]。含血PQ浓度的多因素Logistic回归分析结果显示,WBC、血�Objective To investigate the predictive value of a decision tree-based machine learning model for prognosis in acute paraquat(PQ)poisoning(APP)patients.Methods A retrospective study was conducted.The clinical data of APP patients from Cangzhou Central Hospital between May 2012 and August 2021 were collected,including gender,age,time from ingestion to gastric lavage,proportion of hemoperfusion,serum PQ concentration,biochemical indicators[white blood cell count(WBC),alanine aminotransferase(ALT),serum creatinine(SCr),serum amylase,and serum potassium],and blood gas indicators[arterial blood lactic acid(Lac),base excess(BE),and arterial partial pressure of oxygen(PaO_(2))].Patients were divided into a survival group(n=56)and a death group(n=74)based on 90-day prognosis,and the clinical data between the two groups were compared.The multivariate Logistic regression analysis was conducted to analyze the risk factors of prognosis in APP patients,and two decision tree models(i.e.,with/without serum PQ concentration)were constructed based on the risk factors.The predictive value was evaluated by the receiver operator characteristic(ROC)curve,and the area under the ROC curve(AUC)of two decision tree models was compared by Hanley&McNeil method.Results The 90-day survival rate of the patients was 43.1%(56/130).Compared with death group,patients in the survival group had lower WBC[×10^(9)/LL:8.9(7.0,11.6)vs.17.4(11.9,23.1)],ALT[U/L:25.3(21.2,31.8)vs.29.3(23.2,40.3)],SCr[μmol/L:64.0(53.0,74.0)vs.91.0(72.5,141.5)],Lac[mmol/L:2.5(1.4,4.0)vs.7.1(3.7,11.0)],and serum PQ concentration[ng/L:0.3(0.1,0.9)vs.2.9(1.9,8.1)],the difference were statistically significant(all P<0.05),higher BE[mmol/L:-2.5(-4.2,-1.1)vs.-7.2(-10.9,-4.7)]and serum potassium[mmol/L:3.7(3.5,4.0)vs.3.2(2.8,3.7)],the difference were statistically significant(all P<0.05),and patients were younger[years:33.5(26.0,47.8)vs.42.5(26.0,58.0),P<0.05].Univariate Logistic regression analysis showed that age,WBC,ALT,SCr,serum potassium,Lac,BE and serum PQ concentration we

关 键 词：百草枯 中毒 机器学习 决策树 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]