Abstract
BackgroundThe COVID-19 pandemic continues to ravage and burden hospitals around the world. The epidemic started in Wuhan, China, and was subsequently recognized by the World Health Organization as an international public health emergency and declared a pandemic in March 2020. Since then, the disruptions caused by the COVID-19 pandemic have had an unparalleled effect on all aspects of life.ObjectiveWith increasing total hospitalization and intensive care unit admissions, a better understanding of features related to patients with COVID-19 could help health care workers stratify patients based on the risk of developing a more severe case of COVID-19. Using predictive models, we strive to select the features that are most associated with more severe cases of COVID-19.MethodsOver 3 million participants reported their potential symptoms of COVID-19, along with their comorbidities and demographic information, on a smartphone-based app. Using data from the >10,000 individuals who indicated that they had tested positive for COVID-19 in the United Kingdom, we leveraged the Elastic Net regularized binary classifier to derive the predictors that are most correlated with users having a severe enough case of COVID-19 to seek treatment in a hospital setting. We then analyzed such features in relation to age and other demographics and their longitudinal trend.ResultsThe most predictive features found include fever, use of immunosuppressant medication, use of a mobility aid, shortness of breath, and severe fatigue. Such features are age-related, and some are disproportionally high in minority populations.ConclusionsPredictors selected from the predictive models can be used to stratify patients into groups based on how much medical attention they are expected to require. This could help health care workers devote valuable resources to prevent the escalation of the disease in vulnerable populations.
Highlights
The COVID-19 pandemic, caused by SARS-CoV-2, continues to burden medical institutions around the world by increasing total hospitalization and intensive care unit (ICU) admissions [1,2,3,4,5,6,7,8,9]
The average cross-validated area under the receiver operating characteristic curve for the training set at the optimal parameters was 0.752 (SD 0.021)
To better understand the fluctuations in the symptoms selected by the Elastic Net model, we analyzed the symptoms in a longitudinal manner
Summary
The COVID-19 pandemic, caused by SARS-CoV-2, continues to burden medical institutions around the world by increasing total hospitalization and intensive care unit (ICU) admissions [1,2,3,4,5,6,7,8,9]. Using data from the >10,000 individuals who indicated that they had tested positive for COVID-19 in the United Kingdom, we leveraged the Elastic Net regularized binary classifier to derive the predictors that are most correlated with users having a severe enough case of COVID-19 to seek treatment in a hospital setting We analyzed such features in relation to age and other demographics and their longitudinal trend. Conclusions: Predictors selected from the predictive models can be used to stratify patients into groups based on how much medical attention they are expected to require This could help health care workers devote valuable resources to prevent the escalation of the disease in vulnerable populations
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