Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Nov 14, 2020
Date Accepted: Feb 8, 2021
Date Submitted to PubMed: Feb 23, 2021

The final, peer-reviewed published version of this preprint can be found here:

Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study

Huang Y, Radenkovic D, Perez K, Nadeau3 K, Verdin E, Furman D

Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study

J Med Internet Res 2021;23(3):e25696

DOI: 10.2196/25696

PMID: 33621185

PMCID: 7996196

COVID-19 Patients Seeking Treatment: Modeling Predictive Age-dependent and Independent Symptoms and Comorbidities

  • Yingxiang Huang; 
  • Dina Radenkovic; 
  • Kevin Perez; 
  • Kari Nadeau3; 
  • Eric Verdin; 
  • David Furman

ABSTRACT

Background:

The coronavirus disease 2019 (COVID-19) pandemic continues to ravage and burden hospitals around the world. The epidemic started in Wuhan, China was recognized by the World Health Organisation (WHO) as an international public health emergency and declared the outbreak a pandemic in March 2020. Since then, the scale of disruptions caused by the COVID-19 pandemic is having an unparalleled effect in recent history.

Objective:

With increasing total hospitalization and Intensive Care Unit (ICU) admissions, a better understanding of features related to patients with COVID-19 could help healthcare workers stratify patients based on the risk of developing more severe diseases. Using predictive models, we strive to select features that are the most relevant to more severe COVID-19 diseases.

Methods:

Over 3 million participants have reported their potential symptoms of COVID-19 along with their comorbidities, demographics and symptoms on a smartphone-based app. Of the >10,000 who were tested positive, we leveraged Elastic Net regularized binary classifier to derive predictors that are most correlated with users having severe enough disease 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, the use of immunosuppressant medication, mobility aid, shortness of breath, and fatigue. Such features are age-related and some are manifesting disproportionally in minority populations.

Conclusions:

Predictors selected from the predictive models can be used to stratify patients into groups that may need additional attention than others. It could help health care workers to devote valuable resources to prevent the escalation of diseases in vulnerable populations.


 Citation

Please cite as:

Huang Y, Radenkovic D, Perez K, Nadeau3 K, Verdin E, Furman D

Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study

J Med Internet Res 2021;23(3):e25696

DOI: 10.2196/25696

PMID: 33621185

PMCID: 7996196

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.