Early risk assessment for COVID-19 patients from emergency department data using machine learning
Abstract Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, ri...
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2021
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oai:doaj.org-article:3a7ab14094eb4060a2ca9e6827b2ed552021-12-02T14:21:53ZEarly risk assessment for COVID-19 patients from emergency department data using machine learning10.1038/s41598-021-83784-y2045-2322https://doaj.org/article/3a7ab14094eb4060a2ca9e6827b2ed552021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83784-yhttps://doaj.org/toc/2045-2322Abstract Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients’ initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42–0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient’s oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient’s first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.Frank S. HeldtMarcela P. VizcaychipiSophie PeacockMattia CinelliLachlan McLachlanFernando AndreottiStojan JovanovićRobert DürichenNadezda LipunovaRobert A. FletcherAnne HancockAlex McCarthyRichard A. PointonAlexander BrownJames EatonRoberto LiddiLucy MackillopLionel TarassenkoRabia T. KhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
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DOAJ |
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DOAJ |
| language |
EN |
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Medicine R Science Q |
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Medicine R Science Q Frank S. Heldt Marcela P. Vizcaychipi Sophie Peacock Mattia Cinelli Lachlan McLachlan Fernando Andreotti Stojan Jovanović Robert Dürichen Nadezda Lipunova Robert A. Fletcher Anne Hancock Alex McCarthy Richard A. Pointon Alexander Brown James Eaton Roberto Liddi Lucy Mackillop Lionel Tarassenko Rabia T. Khan Early risk assessment for COVID-19 patients from emergency department data using machine learning |
| description |
Abstract Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients’ initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42–0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient’s oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient’s first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes. |
| format |
article |
| author |
Frank S. Heldt Marcela P. Vizcaychipi Sophie Peacock Mattia Cinelli Lachlan McLachlan Fernando Andreotti Stojan Jovanović Robert Dürichen Nadezda Lipunova Robert A. Fletcher Anne Hancock Alex McCarthy Richard A. Pointon Alexander Brown James Eaton Roberto Liddi Lucy Mackillop Lionel Tarassenko Rabia T. Khan |
| author_facet |
Frank S. Heldt Marcela P. Vizcaychipi Sophie Peacock Mattia Cinelli Lachlan McLachlan Fernando Andreotti Stojan Jovanović Robert Dürichen Nadezda Lipunova Robert A. Fletcher Anne Hancock Alex McCarthy Richard A. Pointon Alexander Brown James Eaton Roberto Liddi Lucy Mackillop Lionel Tarassenko Rabia T. Khan |
| author_sort |
Frank S. Heldt |
| title |
Early risk assessment for COVID-19 patients from emergency department data using machine learning |
| title_short |
Early risk assessment for COVID-19 patients from emergency department data using machine learning |
| title_full |
Early risk assessment for COVID-19 patients from emergency department data using machine learning |
| title_fullStr |
Early risk assessment for COVID-19 patients from emergency department data using machine learning |
| title_full_unstemmed |
Early risk assessment for COVID-19 patients from emergency department data using machine learning |
| title_sort |
early risk assessment for covid-19 patients from emergency department data using machine learning |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/3a7ab14094eb4060a2ca9e6827b2ed55 |
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