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

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.

Keywords: Extremely Severe Burn; Logistic Regression; Risk Factors; Sepsis; XGboost.

Figure 1.

Flow chart of the study population.

Figure 2.

A Forest map shows the results of the multivariable logistic regression (LR) analysis. Age, admission time, Body Index (BI), fibrinogen, and neutrophil-to-lymphocyte ratio (NLR) were independent risk factors for the occurrence of sepsis. OR, odds ratio; CI, confidence interval.

Figure 3.

Individualized nomogram of the logistic regression (LR) model for the risk of sepsis in patients with severe burns. NLR, neutrophil-to-lymphocyte ratio; BI, body index. *P < 0.05; **P < 0.01.

Figure 4.

Receiver operating characteristic (ROC) curve of the logistic regression (LR) model. Area under the curve (AUC) was 0.88, (P < 0.001; 95% CI, 0.82, 0.95). Youden index was 0.22 (95% CI, 0.83, 0.90).

Figure 5.

Receiver operating characteristic (ROC) curve of the XGboost model. Area under the curve (AUC) was 0.91, (P < 0.001; 95% CI, 0.82, 0.95). Youden index was 0.08 (95% CI, 0.82, 1.0).

Figure 6.

SHAP summary plot of the features of the XGBoost model. The higher the SHAP value of a feature, the higher the probability of a predictive value for sepsis. Fibrinogen had the strongest predictive value for all predicted levels, followed by total body surface area (TBSA) in burns, neutrophil-to-lymphocyte ratio (NLR), body index (BI), and age. PLT, platelets; CRP, C-reactive protein; III, area of third-degree burns; RBC, red blood cells.

Figure 7.

Analysis of XGboost model features using SHAP values to show mortality risk factors. An increase in fibrinogen, NLR, BI, age, and admission time had a positive effect and pushed the prediction towards the occurrence of sepsis. SHAP, shapely additive explanation analysis; TBSA, total body surface area in burns; NLR, neutrophil-to-lymphocyte ratio; TBSI, total burn surface involved; PLT, platelets; CRP, C-reactive protein; III, area of third-degree burns; RBC, red blood cells.