Multivariate indicators of disease severity in COVID-19
Abstract The novel coronavirus pandemic continues to cause significant morbidity and mortality around the world. Diverse clinical presentations prompted numerous attempts to predict disease severity to improve care and patient outcomes. Equally important is understanding the mechanisms underlying su...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-31683-9 |
| _version_ | 1827974603872403456 |
|---|---|
| author | Joe Bean Leticia Kuri-Cervantes Michael Pennella Michael R. Betts Nuala J. Meyer Wail M. Hassan |
| author_facet | Joe Bean Leticia Kuri-Cervantes Michael Pennella Michael R. Betts Nuala J. Meyer Wail M. Hassan |
| author_sort | Joe Bean |
| collection | DOAJ |
| description | Abstract The novel coronavirus pandemic continues to cause significant morbidity and mortality around the world. Diverse clinical presentations prompted numerous attempts to predict disease severity to improve care and patient outcomes. Equally important is understanding the mechanisms underlying such divergent disease outcomes. Multivariate modeling was used here to define the most distinctive features that separate COVID-19 from healthy controls and severe from moderate disease. Using discriminant analysis and binary logistic regression models we could distinguish between severe disease, moderate disease, and control with rates of correct classifications ranging from 71 to 100%. The distinction of severe and moderate disease was most reliant on the depletion of natural killer cells and activated class-switched memory B cells, increased frequency of neutrophils, and decreased expression of the activation marker HLA-DR on monocytes in patients with severe disease. An increased frequency of activated class-switched memory B cells and activated neutrophils was seen in moderate compared to severe disease and control. Our results suggest that natural killer cells, activated class-switched memory B cells, and activated neutrophils are important for protection against severe disease. We show that binary logistic regression was superior to discriminant analysis by attaining higher rates of correct classification based on immune profiles. We discuss the utility of these multivariate techniques in biomedical sciences, contrast their mathematical basis and limitations, and propose strategies to overcome such limitations. |
| first_indexed | 2024-04-09T19:57:03Z |
| format | Article |
| id | doaj.art-f88aa008d52d4790bc0be4aace6049b5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T19:57:03Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f88aa008d52d4790bc0be4aace6049b52023-04-03T05:25:55ZengNature PortfolioScientific Reports2045-23222023-03-0113112810.1038/s41598-023-31683-9Multivariate indicators of disease severity in COVID-19Joe Bean0Leticia Kuri-Cervantes1Michael Pennella2Michael R. Betts3Nuala J. Meyer4Wail M. Hassan5Department of Biomedical Sciences, School of Medicine, University of Missouri – Kansas CityDepartment of Microbiology, Perelman School of Medicine, University of PennsylvaniaDepartment of Biomedical Sciences, School of Medicine, University of Missouri – Kansas CityDepartment of Microbiology, Perelman School of Medicine, University of PennsylvaniaDivision of Pulmonary, Allergy and Critical Care, Department of Medicine, Center for Translational Lung Biology, Lung Biology Institute, Perelman School of Medicine, University of PennsylvaniaDepartment of Biomedical Sciences, School of Medicine, University of Missouri – Kansas CityAbstract The novel coronavirus pandemic continues to cause significant morbidity and mortality around the world. Diverse clinical presentations prompted numerous attempts to predict disease severity to improve care and patient outcomes. Equally important is understanding the mechanisms underlying such divergent disease outcomes. Multivariate modeling was used here to define the most distinctive features that separate COVID-19 from healthy controls and severe from moderate disease. Using discriminant analysis and binary logistic regression models we could distinguish between severe disease, moderate disease, and control with rates of correct classifications ranging from 71 to 100%. The distinction of severe and moderate disease was most reliant on the depletion of natural killer cells and activated class-switched memory B cells, increased frequency of neutrophils, and decreased expression of the activation marker HLA-DR on monocytes in patients with severe disease. An increased frequency of activated class-switched memory B cells and activated neutrophils was seen in moderate compared to severe disease and control. Our results suggest that natural killer cells, activated class-switched memory B cells, and activated neutrophils are important for protection against severe disease. We show that binary logistic regression was superior to discriminant analysis by attaining higher rates of correct classification based on immune profiles. We discuss the utility of these multivariate techniques in biomedical sciences, contrast their mathematical basis and limitations, and propose strategies to overcome such limitations.https://doi.org/10.1038/s41598-023-31683-9 |
| spellingShingle | Joe Bean Leticia Kuri-Cervantes Michael Pennella Michael R. Betts Nuala J. Meyer Wail M. Hassan Multivariate indicators of disease severity in COVID-19 Scientific Reports |
| title | Multivariate indicators of disease severity in COVID-19 |
| title_full | Multivariate indicators of disease severity in COVID-19 |
| title_fullStr | Multivariate indicators of disease severity in COVID-19 |
| title_full_unstemmed | Multivariate indicators of disease severity in COVID-19 |
| title_short | Multivariate indicators of disease severity in COVID-19 |
| title_sort | multivariate indicators of disease severity in covid 19 |
| url | https://doi.org/10.1038/s41598-023-31683-9 |
| work_keys_str_mv | AT joebean multivariateindicatorsofdiseaseseverityincovid19 AT leticiakuricervantes multivariateindicatorsofdiseaseseverityincovid19 AT michaelpennella multivariateindicatorsofdiseaseseverityincovid19 AT michaelrbetts multivariateindicatorsofdiseaseseverityincovid19 AT nualajmeyer multivariateindicatorsofdiseaseseverityincovid19 AT wailmhassan multivariateindicatorsofdiseaseseverityincovid19 |