Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods. (December 2022)

• Record Type: Journal Article
• Authors: Hasan, M.; Bath, P.A.; Marincowitz, C.; Sutton, L.; Pilbery, R.; Hopfgartner, F.; Mazumdar, S.; Campbell, R.; Stone, T.; Thomas, B.; Bell, F.; Turner, J.; Biggs, K.; Petrie, J.; Goodacre, S.
• Is Part Of: Computers in biology and medicine. Volume 151:Part A(2022)
• Publication Date: 2022-12
• Subjects: COVID-19 -- Emergency services -- Support vector machine -- Extreme gradient boosting -- Artificial neural networks -- Stacking ensemble -- Logistic regression; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106024
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: Rapid and accurate assessment of suspected COVID-19 patients is required to identify at-risk patients. Our machine learning models provided better predictive performance than existing triage methods item There were some differences in the features selected between the machine learning algorithms. Oxygen saturation, a patient's level of consciousness and frailty were the most important features. Our models could be deployed to provide accurate predictions of patient outcomes.