ABSTRACT

Background: Several studies reported that the National Early Warning Score (NEWS) has shown superiority over other screening tools in discriminating emergency department (ED) patients who are likely to progress to septic shock.

Objectives: To improve the performance of the NEWS for septic shock prediction by adding variables collected during ED triage, and to implement a machine-learning algorithm.

Methods: The study population comprised adult ED patients with suspected infection. To detect septic shock within 24 h after ED arrival, the Sepsis-3 clinical criteria and nine variables were used: NEWS, age, gender, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, and oxygen saturation. The model was developed using logistic regression (LR), extreme gradient boosting (XGB), and artificial neural network (ANN) algorithms. The evaluations were performed using an area under the receiver operating characteristic curve (AUROC), Hosmer-Lemeshow test, and net reclassification index (NRI).

Results: Overall, 41,687 patients were enrolled. The AUROC of the model with NEWS, age, gender, and the six vital signs (0.835-0.845) was better than that of the baseline model (0.804). The XGB model (AUROC 0.845) was the most accurate, compared with LR (0.844) and ANN (0.835). The LR and XGB models were well calibrated; however, the ANN showed poor calibration power. The LR and XGB models showed better reclassification than the baseline model with positive NRI.

Conclusion: The discrimination power of the model for screening septic shock using NEWS, age, gender, and the six vital signs collected at ED triage outperformed the baseline NEWS model.