COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States
dc.creator | Vavougios G.D., Stavrou V.T., Konstantatos C., Sinigalias P.-C., Zarogiannis S.G., Kolomvatsos K., Stamoulis G., Gourgoulianis K.I. | en |
dc.date.accessioned | 2023-01-31T10:30:44Z | |
dc.date.available | 2023-01-31T10:30:44Z | |
dc.date.issued | 2022 | |
dc.identifier | 10.3390/ijerph19084630 | |
dc.identifier.issn | 16617827 | |
dc.identifier.uri | http://hdl.handle.net/11615/80539 | |
dc.description.abstract | 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 <30 days were included in this study) were used sequen-tially in identifying and validating COVID-19 syndromic phenotypes. Logistic Regression-weighted multiple correspondence analysis (LRW-MCA) was used as a preprocessing procedure, in order to weigh and transform symptoms recorded by the survey to eigenspace coordinates, capturing a total variance of >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. Olfac-tion/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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. | en |
dc.language.iso | en | en |
dc.source | International Journal of Environmental Research and Public Health | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127867230&doi=10.3390%2fijerph19084630&partnerID=40&md5=60979a5ea54eb16a16bc4027786dc840 | |
dc.subject | COVID-19 | en |
dc.subject | epidemic | en |
dc.subject | epidemiology | en |
dc.subject | morbidity | en |
dc.subject | pattern recognition | en |
dc.subject | phenotype | en |
dc.subject | adult | en |
dc.subject | aged | en |
dc.subject | Article | en |
dc.subject | artificial intelligence | en |
dc.subject | big data | en |
dc.subject | cohort analysis | en |
dc.subject | comorbidity | en |
dc.subject | controlled study | en |
dc.subject | coronavirus disease 2019 | en |
dc.subject | correspondence analysis | en |
dc.subject | coughing | en |
dc.subject | COVID-19 testing | en |
dc.subject | cross-sectional study | en |
dc.subject | diagnostic accuracy | en |
dc.subject | disease duration | en |
dc.subject | epidemiological surveillance | en |
dc.subject | female | en |
dc.subject | human | en |
dc.subject | longitudinal study | en |
dc.subject | major clinical study | en |
dc.subject | male | en |
dc.subject | middle aged | en |
dc.subject | pattern recognition | en |
dc.subject | phenotype | en |
dc.subject | principal component analysis | en |
dc.subject | retrospective study | en |
dc.subject | smelling | en |
dc.subject | United States | en |
dc.subject | validation study | en |
dc.subject | young adult | en |
dc.subject | comorbidity | en |
dc.subject | epidemiology | en |
dc.subject | phenotype | en |
dc.subject | Delhi | en |
dc.subject | India | en |
dc.subject | United States | en |
dc.subject | Comorbidity | en |
dc.subject | Cough | en |
dc.subject | COVID-19 | en |
dc.subject | Humans | en |
dc.subject | Phenotype | en |
dc.subject | SARS-CoV-2 | en |
dc.subject | United States | en |
dc.subject | MDPI | en |
dc.title | COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States | en |
dc.type | journalArticle | en |
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