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Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for
Yingxiang Huang1, Dina Radenkovic2,3, Kevin Perez1
1Buck Institute for Research on Aging, Novato, CA, United States.
Insights
Predicting severe COVID-19 cases is crucial for hospital resource allocation. Key indicators like fever, shortness of breath, and fatigue help identify at-risk patients, especially in minority populations.
Area of Science:
- Epidemiology
- Public Health
- Health Informatics
Background:
- The COVID-19 pandemic has placed an unprecedented burden on global healthcare systems.
- Understanding factors associated with severe COVID-19 is vital for effective patient management and resource allocation.
Purpose of the Study:
- To identify key predictors of severe COVID-19 cases requiring hospitalization.
- To utilize predictive modeling for stratifying patient risk.
Main Methods:
- Analysis of data from over 3 million participants via a smartphone app.
- Application of an Elastic Net regularized binary classifier to >10,000 UK COVID-19 positive cases.
- Investigation of demographic factors and longitudinal trends associated with severe outcomes.
Main Results:
- Fever, immunosuppressant medication use, mobility aid use, shortness of breath, and severe fatigue were identified as the most predictive features of severe COVID-19.
- These predictors showed age-related patterns and disproportionate prevalence in minority populations.
Conclusions:
- Identified predictors can stratify patients based on expected medical needs.
- This stratification aids healthcare workers in prioritizing resources for vulnerable populations and preventing disease escalation.
Background:
The 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.
Objective:
With 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.
Methods:
Over 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.
Results:
The 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.
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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