王雅琪, 苏艺伟, 刘移民. 百草枯中毒预后的lasso-logistic回归分析预测模型的建立[J]. 职业卫生与应急救援, 2022, 40(3): 259-264. DOI: 10.16369/j.oher.issn.1007-1326.2022.03.001
引用本文: 王雅琪, 苏艺伟, 刘移民. 百草枯中毒预后的lasso-logistic回归分析预测模型的建立[J]. 职业卫生与应急救援, 2022, 40(3): 259-264. DOI: 10.16369/j.oher.issn.1007-1326.2022.03.001
WANG Yaqi, SU Yiwei, LIU Yimin. Prognosis prediction of paraquat poisoning with lasso-logistic regression[J]. Occupational Health and Emergency Rescue, 2022, 40(3): 259-264. DOI: 10.16369/j.oher.issn.1007-1326.2022.03.001
Citation: WANG Yaqi, SU Yiwei, LIU Yimin. Prognosis prediction of paraquat poisoning with lasso-logistic regression[J]. Occupational Health and Emergency Rescue, 2022, 40(3): 259-264. DOI: 10.16369/j.oher.issn.1007-1326.2022.03.001

百草枯中毒预后的lasso-logistic回归分析预测模型的建立

Prognosis prediction of paraquat poisoning with lasso-logistic regression

  • 摘要:
      目的   确定临床中常用且简单的检测指标,预测百草枯中毒患者的预后,以便对患者采取及时有效的救治方案,提高患者生存率。
      方法   以广州市第十二人民医院2010—2019年收治的199例百草枯中毒患者为研究对象。收集患者基本资料以及入院24 h内的血气分析指标、凝血指标、血常规指标和血生化指标。使用SPSS 26和R 4.0.3软件对数据进行整理和统计学分析。采用lasso回归筛选出影响百草枯中毒患者预后的影响因素,然后使用R软件构建lasso-logistic临床预测模型对其进行验证。
      结果   199名患者中死亡率为62.31%(124/199)。lasso回归筛选出口服百草枯剂量、碳酸氢根离子(HCO3-)浓度、凝血酶时间(TT)、白细胞(WBC)计数、血糖(GLU)、尿素氮(BUN)和血肌酐(SCr)是百草枯中毒患者预后的潜在影响因素,进一步的logistic回归分析结果显示:口服剂量、WBC、SCr数值增加是百草枯中毒患者死亡的危险因素(OR>1,P<0.05),HCO3-浓度增加是百草枯中毒患者死亡的保护因素(OR = 0.811,P < 0.05)。百草枯中毒预测列线图模型的C值> 0.9,Calibration校准曲线中理想曲线与校准曲线高度重合。决策曲线分析表明,在整个阈值范围内预测模型都具有临床有效性。
      结论   百草枯中毒患者的死亡率高,基于lasso-logistic回归制作的百草枯中毒死亡风险列线图预测模型具有一定的准确性和可操作性。

     

    Abstract:
      Objective   The prognosis of patients with paraquat poisoning was predicted, according to the commonly used and simple detected clinic indicators, in order to take timely and effective treatment and improve their survival rate.
      Methods   Totally 199 patients with paraquat poisoning treated in Guangzhou 12th people's Hospital during 2010 to 2019 were studied. The basic information of patients and their blood gas analysis indexes, coagulation indexes, blood routine indexes and blood biochemical indexes within 24 hours after admission were abstracted. SPSS 26 and R 4.0.3 software were used to sort out and statistically analyze the data. Lasso regression was used to screen out the influencing factors affecting the prognosis of patients with paraquat poisoning, and then lasso -logistic clinical prediction model was constructed by R software to verify it.
      Results   The fatality among 199 patients was 62.31%(124 / 199). Lasso regression screening showed that the intake dose of paraquat, bicarbonate ion concentration(HCO3-), thrombin time(TT), leukocyte count(WBC), blood glucose (Glu), urea nitrogen (BUN) and blood creatinine (SCR) were the potential prognostic factors of these patients. Further logistic regression analysis showed that the increase of oral intake dose, WBC and SCR were the risk factors of death in patients with paraquat poisoning (OR > 1, P < 0.05); the increase of HCO3- concentration was a protective factor for the death of patients with paraquat poisoning (OR = 0.811, P < 0.05). The C value of the nomogram model for paraquat poisoning prediction was > 0.9, and the ideal curve in the calibration curve highly coincided with the calibration curve. The analysis of decision curve showed that the prediction model had clinical effectiveness in the whole threshold range.
      Conclusions   The fatality of paraquat poisoning was high. The prediction model of death risk nomogram of paraquat poisoning based on lasso-logistic regression had certain accuracy and operability.

     

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