Machine learning for prediction of septic shock at initial triage in emergency department

Abstract

Background: We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS.

Methods: The study population was adult (≥20 years) patients visiting ED for suspected infection. Target event was septic shock within 24 h after arrival. Demographics, vital signs, level of consciousness, chief complaints (CC) and initial blood test results were used as predictors. CC were embedded into 16-dimensional vector space using singular value decomposition. Six base learners including support vector machine, gradient-boosting machine, random forest, multivariate adaptive regression splines and least absolute shrinkage and selection operator and ridge regression and their ensembles were tested. We also trained and tested MLP networks with various setting.

Results: A total of 49,560 patients were included and 4817 (9.7%) had septic shock within 24 h. All ML classifiers significantly outperformed qSOFA score, MEWS and their age-sex adjusted versions with their AUROC ranging from 0.883 to 0.929. The ensembles of the base classifiers showed the best performance and addition of CC embedding was associated with statistically significant increases in performance.

Conclusions: ML classifiers significantly outperforms clinical scores in screening septic shock at ED triage.

Keywords: Clinical decision support tool; Diagnosis; Emergency department triage tool; Machine learning; Prediction; Sepsis; Septic shock.