Early detection of severe COVID-19 disease patterns define near real-time personalised care, bioseverity in males, and decelerating mortality rates

Vizcaychipi, Marcela P.; Shovlin, CL; Hayes, Michelle; Singh, Suveer; Christie, Linsey; Sisson, Alice; Davies, R. G.; Lockie, Christopher; Howard, Alice; Brown, Alexander; McCarthy, Alex; Popescu, Monica; Gupta, Amandeep; Armstrong, James A.; Said, Hisham; Peters, Timothy R.; Keays, Richard

doi:10.1101/2020.05.08.20088393

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…Currently, few prognostic markers exist to forecast whether a COVID-19 patient may deteriorate to a critical condition and require intensive care. In general, patients can be grouped into three phenotypes, being at risk of thromboembolic disease, respiratory deterioration and cytokine storm 5 . Clinical reports find that age, sex and underlying comorbidities, such as hypertension, cardiovascular disease and diabetes, can adversely affect patient outcomes 6 , 7 .…”

Section: Introductionmentioning

confidence: 99%

Early risk assessment for COVID-19 patients from emergency department data using machine learning

Heldt¹,

Vizcaychipi

Peacock³

et al. 2021

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

Early risk assessment for COVID-19 patients from emergency department data using machine learning

Heldt¹,

Vizcaychipi

Peacock³

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…We identified several categories of potential cofounders that may have played an important role in the improved outcomes in the pre-post studies in our SR. These were: 1) training and education of staff (6,37), 2) broad quality improvement projects in which the HIT was just one component (10,23,43), 3) change management assessments and general improvements over time (30,35,40,45), 4) complex multicomponent or multifaceted interventions that also included DSS and dashboards (40,45), and 5) the Hawthorne effect (10,23,34,37,53).…”

Section: Discussionmentioning

confidence: 99%

“…Two studies examined HIT implementation in the ED (42,43) and then analyzed outcomes among those hospitalized following ED presentation. Five studies were based on both ICU and the hospital floor (44)(45)(46)(47)(48).…”

Section: Eligible Studies and Participant Characteristicsmentioning

confidence: 99%

The Impact of Health Information Technology for Early Detection of Patient Deterioration on Mortality and Length of Stay in the Hospital Acute Care Setting: Systematic Review and Meta-Analysis

Herasevich

Lipatov

Pinevich

et al. 2022

Critical Care Medicine

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“…Currently, few prognostic markers exist to forecast whether a COVID-19 patient may deteriorate to a critical condition and require intensive care. In general, patients can be grouped into three phenotypes, being at risk of thromboembolic disease, respiratory deterioration and cytokine storm [6]. Early clinical reports find that age, sex and underlying comorbidities, such as hypertension, cardiovascular disease and diabetes, can adversely affect patient outcomes [7,8].…”

Section: Introductionmentioning

confidence: 99%

Early risk assessment for COVID-19 patients from emergency department data using machine learning

Heldt¹,

Vizcaychipi

Peacock³

et al. 2020

Preprint

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Background Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic, with more than 4.8 million reported cases and 310 000 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. Methods In this retrospective study, we analysed data of 820 confirmed COVID-19 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and April 23rd, 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: A) admission to intensive care, B) need for mechanical ventilation and C) mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Results Considering our inclusion criteria, we have identified 126 of 820 (15%) patients that required intensive care, 62 of 808 (8%) patients needing mechanical ventilation, and 170 of 630 (27%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving AUC-ROC scores of 0.75 to 0.83 (F1 scores of 0.41 to 0.56). 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 were most predictive of COVID-19 patient trajectories. Conclusion 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.

show abstract

Early detection of severe COVID-19 disease patterns define near real-time personalised care, bioseverity in males, and decelerating mortality rates

Cited by 6 publications

References 26 publications

Early risk assessment for COVID-19 patients from emergency department data using machine learning

Early risk assessment for COVID-19 patients from emergency department data using machine learning

The Impact of Health Information Technology for Early Detection of Patient Deterioration on Mortality and Length of Stay in the Hospital Acute Care Setting: Systematic Review and Meta-Analysis

Early risk assessment for COVID-19 patients from emergency department data using machine learning

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