Predictive value of decision tree-based machine learning model for prognosis in acute paraquat poisoning
10.3969/j.issn.1008-9691.2024.01.013
- VernacularTitle: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
- Author:
Guangwei LYU
1
;
Shunyi FENG
;
Yong LI
;
Jian WANG
Author Information
1. 沧州市中心医院急诊医学部,河北沧州 061000
- Keywords:
Paraquat;
Poisoning;
Machine learning;
Decision tree
- From:
Chinese Journal of Integrated Traditional and Western Medicine in Intensive and Critical Care
2024;31(1):63-67
- CountryChina
- Language:Chinese
-
Abstract:
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(PaO2)].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[×109/L: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 were independent risk factors of 90-day survival[odds ratio(OR)and 95%confidence interval(95%CI)were 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 values were 0.018,<0.001,0.011,<0.001,<0.001,<0.001,<0.001,<0.001].Multivariate Logistic regression analysis with serum PQ concentration showed that WBC,serum potassium,and serum PQ concentration were independent risk factors for 90-day survival[OR and 95%CI were 1.17(1.03-1.33),7.29(1.66-32.01),5.49(2.48-12.13),P values were 0.014,0.008,<0.001].Multivariate Logistic regression analysis without serum PQ concentration showed that age,WBC,serum potassium and BE were independent risk factors for 90-day survival[OR and 95%CI were 1.05(1.01-1.08),1.20(1.07-1.34),3.12(1.01-9.66),1.41(1.16-1.72),P values were 0.008,0.002,0.049,0.001].The decision tree model based on serum PQ concentration and serum potassium showed an AUC of 0.94(95%CI was 0.89-0.98),along with 91.9%sensitivity,89.3%specificity,and 90.0%accuracy.The decision tree model based on WBC,BE,and age showed an AUC of 0.89(95%CI was 0.84-0.95),with 86.5%sensitivity,91.1%specificity,and 88.5%accuracy.Pairwise comparison of the AUC using Hanley&McNeil method demonstrated that no statistical difference between the two decision tree models(Z = 1.34,P = 0.180).Conclusion The decision tree-based models can provide quantitative and intuitive prediction tools for the early detection of prognosis in APP patients in clinical practice.
