Predicting mortality among septic patients presenting to the emergency department– a cross sectional analysis using machine learning
Dublin Core
Title
Predicting mortality among septic patients presenting to the emergency department– a cross sectional analysis using machine learning
Subject
Assessment, Clinical assessment, Emergency care systems, Emergency department, Infectious diseases
Description
Background: Sepsis is a life-threatening condition, causing almost one fifth of all deaths worldwide. The aim of the
current study was to identify variables predictive of 7- and 30-day mortality among variables reflective of the
presentation of septic patients arriving to the emergency department (ED) using machine learning.
Methods: Retrospective cross-sectional design, including all patients arriving to the ED at Södersjukhuset in
Sweden during 2013 and discharged with an International Classification of Diseases (ICD)-10 code corresponding to
sepsis. All predictions were made using a Balanced Random Forest Classifier and 91 variables reflecting ED
presentation. An exhaustive search was used to remove unnecessary variables in the final model. A 10-fold cross
validation was performed and the accuracy was described using the mean value of the following: AUC, sensitivity,
specificity, PPV, NPV, positive LR and negative LR.
Results: The study population included 445 septic patients, randomised to a training (n = 356, 80%) and a
validation set (n = 89, 20%). The six most important variables for predicting 7-day mortality were: “fever”, “abnormal
verbal response”, “low saturation”, “arrival by emergency medical services (EMS)”, “abnormal behaviour or level of
consciousness” and “chills”. The model including these variables had an AUC of 0.83 (95% CI: 0.80–0.86). The final
model predicting 30-day mortality used similar six variables, however, including “breathing difficulties” instead of
“abnormal behaviour or level of consciousness”. This model achieved an AUC = 0.80 (CI 95%, 0.78–0.82).
Conclusions: The results suggest that six specific variables were predictive of 7- and 30-day mortality with good
accuracy which suggests that these symptoms, observations and mode of arrival may be important components to
include along with vital signs in a future prediction tool of mortality among septic patients presenting to the ED. In
addition, the Random Forests appears to be a suitable machine learning method on which to build future studies
current study was to identify variables predictive of 7- and 30-day mortality among variables reflective of the
presentation of septic patients arriving to the emergency department (ED) using machine learning.
Methods: Retrospective cross-sectional design, including all patients arriving to the ED at Södersjukhuset in
Sweden during 2013 and discharged with an International Classification of Diseases (ICD)-10 code corresponding to
sepsis. All predictions were made using a Balanced Random Forest Classifier and 91 variables reflecting ED
presentation. An exhaustive search was used to remove unnecessary variables in the final model. A 10-fold cross
validation was performed and the accuracy was described using the mean value of the following: AUC, sensitivity,
specificity, PPV, NPV, positive LR and negative LR.
Results: The study population included 445 septic patients, randomised to a training (n = 356, 80%) and a
validation set (n = 89, 20%). The six most important variables for predicting 7-day mortality were: “fever”, “abnormal
verbal response”, “low saturation”, “arrival by emergency medical services (EMS)”, “abnormal behaviour or level of
consciousness” and “chills”. The model including these variables had an AUC of 0.83 (95% CI: 0.80–0.86). The final
model predicting 30-day mortality used similar six variables, however, including “breathing difficulties” instead of
“abnormal behaviour or level of consciousness”. This model achieved an AUC = 0.80 (CI 95%, 0.78–0.82).
Conclusions: The results suggest that six specific variables were predictive of 7- and 30-day mortality with good
accuracy which suggests that these symptoms, observations and mode of arrival may be important components to
include along with vital signs in a future prediction tool of mortality among septic patients presenting to the ED. In
addition, the Random Forests appears to be a suitable machine learning method on which to build future studies
Creator
Adam Karlsson , Willem Stassen , Amy Loutfi , Ulrika Wallgren , Eric Larsson and Lisa Kurland
Publisher
BMC Emergency Medicine
Date
(2021) 21:84
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Adam Karlsson , Willem Stassen , Amy Loutfi , Ulrika Wallgren , Eric Larsson and Lisa Kurland, “Predicting mortality among septic patients presenting to the emergency department– a cross sectional analysis using machine learning,” Repository Horizon University Indonesia, accessed April 3, 2025, https://repository.horizon.ac.id/items/show/3800.