Covid-19 Phenotypes and Comorbidity
A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States
Bibliographic Data
| ID | 15501124 |
|---|---|
| Authors | George D Vavougios (0000-0002-0413-4028, University of Thessaly, corresponding author), Vasileios Stavrou (0000-0002-2437-5339, University of Thessaly, corresponding author), Christoforos Konstantatos (0000-0002-3312-0186, University of Patras), Pavlos-Christoforos Sinigalias (University of Patras), Sotirios G Zarogiannis (0000-0002-3083-3244, University of Thessaly), Konstantinos Kolomvatsos (University of Thessaly), George Stamoulis (0009-0006-3562-2274, University of Thessaly), Konstantinos I Gourgoulianis (0000-0001-9541-1010, University of Thessaly) |
| Year | 2022 |
| Volume | 19 |
| Issue | 8 |
| Pages | 4630-4630 |
| Publication date | 2022-04-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph19084630 |
| PMID | 35457497 |
| OpenAlex | W4223645161 |
| Language | EN |
| References cited | 31 |
The aim of our study was to determine COVID-19 syndromic phenotypes in a data-driven manner using the survey results based on survey results from Carnegie Mellon University’s Delphi Group. Monthly survey results (>1 million responders per month; 320,326 responders with a certain COVID-19 test status and disease duration 75%. These scores, along with symptom duration, were subsequently used by the Two Step Clustering algorithm to produce symptom clusters. Post-hoc logistic regression models adjusting for age, gender, and comorbidities and confirmatory linear principal components analyses were used to further explore the data. Model creation, based on August’s 66,165 included responders, was subsequently validated in data from March−December 2020. Five validated COVID-19 syndromes were identified in August: 1. Afebrile (0%), Non-Coughing (0%), Oligosymptomatic (ANCOS); 2. Febrile (100%) Multisymptomatic (FMS); 3. Afebrile (0%) Coughing (100%) Oligosymptomatic (ACOS); 4. Oligosymptomatic with additional self-described symptoms (100%; OSDS); 5. Olfaction/Gustatory Impairment Predominant (100%; OGIP). Our findings indicate that the COVID-19 spectrum may be undetectable when applying current disease definitions focusing on respiratory symptoms alone
2019-20 coronavirus outbreak · Biology · Comorbidity · Coronavirus disease 2019 (COVID-19 · Disease · Outbreak · Phenotype · Psychiatry · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · COVID-19 and Mental Health · COVID-19 Clinical Research Studies · Long-Term Effects of COVID-19 · Medicine · Genetics · Internal Medicine · Virology
Symptom Duration and Risk Factors for Delayed Return to Usual Health Among Outpatients with Covid-19 in a Multistate Health Care Systems Network — United States, March–June 2020
Using multiple correspondence analysis to identify behaviour patterns associated with overweight and obesity in Vanuatu adults
| Citation velocity | historical |
|---|---|
| Highly cited | No |