Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and
M Hasan1, P A Bath2, C Marincowitz1
1The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
Insights
Artificial intelligence models can predict adverse outcomes in suspected COVID-19 patients. Machine learning improved prediction accuracy, potentially reducing unnecessary hospital admissions for emergency medical services patients.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Emergency Medicine
Background:
- COVID-19 caused global mortality and strained emergency medical services (EMS).
- Accurate risk stratification is crucial for COVID-19 patients to guide treatment and hospital admission decisions.
- Developing predictive models can aid clinicians in identifying high-risk individuals.
Purpose of the Study:
- To develop and evaluate artificial intelligence (AI) models for predicting adverse outcomes in suspected COVID-19 patients.
- To compare AI model performance against existing clinical decision-making tools used by EMS.
Main Methods:
- Utilized linked ambulance service data for 7,549 adult patients with suspected COVID-19.
- Applied machine learning algorithms including Support Vector Machines (SVM), Extreme Gradient Boosting, Artificial Neural Networks (ANN), and ensemble methods.
- Predicted the primary outcome of death or need for organ support within 30 days, comparing against EMS conveyance decisions and the PRIEST score.
Main Results:
- 17.6% of patients experienced the primary outcome (death or organ support within 30 days).
- Machine learning models demonstrated slight improvements in sensitivity compared to baseline assessments.
- Ensemble methods, particularly using SVM and ANN as base learners, yielded the best geometric mean for maximizing sensitivity and specificity.
Conclusions:
- AI models show potential for improving risk prediction in suspected COVID-19 patients managed by EMS.
- These predictive tools could help reduce unnecessary hospital admissions without increasing adverse events.
- Further external validation and development of automated systems are needed for clinical implementation.
Background:
COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians.
Method:
Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score.
Results:
Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity.
Conclusions:
These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.
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