用于预测特重度烧伤后脓毒症的XGBoost模型与逻辑回归模型的比较。

Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns.

作者信息

Liu Peng, Li Xiao-Jian, Zhang Tao, Huang Yi-Hui

机构信息

Department of Burn and Plastic, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, China.

Department of Pediatric Medicine, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, China.

出版信息

J Int Med Res. 2024 May;52(5):3000605241247696. doi: 10.1177/03000605241247696.

DOI:10.1177/03000605241247696

PMID:38698505

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11067675/

Abstract

OBJECTIVE

To compare an Extreme Gradient Boosting (XGboost) model with a multivariable logistic regression (LR) model for their ability to predict sepsis after extremely severe burns.

METHODS

For this observational study, patient demographic and clinical information were collected from medical records. The two models were evaluated using area under curve (AUC) of the receiver operating characteristic (ROC) curve.

RESULTS

Of the 103 eligible patients with extremely severe burns, 20 (19%) were in the sepsis group, and 83 (81%) in the non-sepsis group. The LR model showed that age, admission time, body index (BI), fibrinogen, and neutrophil to lymphocyte ratio (NLR) were risk factors for sepsis. Comparing AUC of the ROC curves, the XGboost model had a higher predictive performance (0.91) than the LR model (0.88). The SHAP visualization tool indicated fibrinogen, NLR, BI, and age were important features of sepsis in patients with extremely severe burns.

CONCLUSIONS

The XGboost model was superior to the LR model in predictive efficacy. Results suggest that, fibrinogen, NLR, BI, and age were correlated with sepsis after extremely severe burns.

摘要

目的

比较极端梯度提升（XGboost）模型和多变量逻辑回归（LR）模型预测特重度烧伤后脓毒症的能力。

方法

在这项观察性研究中，从病历中收集患者的人口统计学和临床信息。使用受试者工作特征（ROC）曲线下面积（AUC）对这两种模型进行评估。

结果

在103例符合条件的特重度烧伤患者中，20例（19%）为脓毒症组，83例（81%）为非脓毒症组。LR模型显示年龄、入院时间、身体指数（BI）、纤维蛋白原和中性粒细胞与淋巴细胞比值（NLR）是脓毒症的危险因素。比较ROC曲线的AUC，XGboost模型的预测性能（0.91）高于LR模型（0.88）。SHAP可视化工具表明，纤维蛋白原、NLR、BI和年龄是特重度烧伤患者脓毒症的重要特征。