Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes
Abstract Background Critically ill hospitalized patients with COVID-19 have greater antibody titers than those with mild to moderate illness, but their association with recovery or death from COVID-19 has not been characterized. Methods In a cohort study of 178 COVID-19 patients, 73 non-hospitalized...
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Nature Portfolio
2024-11-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00658-w |
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| author | Santosh Dhakal Anna Yin Marta Escarra-Senmarti Zoe O. Demko Nora Pisanic Trevor S. Johnston Maria Isabel Trejo-Zambrano Kate Kruczynski John S. Lee Justin P. Hardick Patrick Shea Janna R. Shapiro Han-Sol Park Maclaine A. Parish Christopher Caputo Abhinaya Ganesan Sarika K. Mullapudi Stephen J. Gould Michael J. Betenbaugh Andrew Pekosz Christopher D. Heaney Annukka A. R. Antar Yukari C. Manabe Andrea L. Cox Andrew H. Karaba Felipe Andrade Scott L. Zeger Sabra L. Klein |
| author_facet | Santosh Dhakal Anna Yin Marta Escarra-Senmarti Zoe O. Demko Nora Pisanic Trevor S. Johnston Maria Isabel Trejo-Zambrano Kate Kruczynski John S. Lee Justin P. Hardick Patrick Shea Janna R. Shapiro Han-Sol Park Maclaine A. Parish Christopher Caputo Abhinaya Ganesan Sarika K. Mullapudi Stephen J. Gould Michael J. Betenbaugh Andrew Pekosz Christopher D. Heaney Annukka A. R. Antar Yukari C. Manabe Andrea L. Cox Andrew H. Karaba Felipe Andrade Scott L. Zeger Sabra L. Klein |
| author_sort | Santosh Dhakal |
| collection | DOAJ |
| description | Abstract Background Critically ill hospitalized patients with COVID-19 have greater antibody titers than those with mild to moderate illness, but their association with recovery or death from COVID-19 has not been characterized. Methods In a cohort study of 178 COVID-19 patients, 73 non-hospitalized and 105 hospitalized patients, mucosal swabs and plasma samples were collected at hospital enrollment and up to 3 months post-enrollment (MPE) to measure virus RNA, cytokines/chemokines, binding antibodies, ACE2 binding inhibition, and Fc effector antibody responses against SARS-CoV-2. The association of demographic variables and more than 20 serological antibody measures with intubation or death due to COVID-19 was determined using machine learning algorithms. Results Predictive models reveal that IgG binding and ACE2 binding inhibition responses at 1 MPE are positively and anti-Spike antibody-mediated complement activation at enrollment is negatively associated with an increased probability of intubation or death from COVID-19 within 3 MPE. Conclusions At enrollment, serological antibody measures are more predictive than demographic variables of subsequent intubation or death among hospitalized COVID-19 patients. |
| format | Article |
| id | doaj-art-f7ea6c1555904c5aa73e7df2ef8983ac |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-f7ea6c1555904c5aa73e7df2ef8983ac2024-12-01T12:42:04ZengNature PortfolioCommunications Medicine2730-664X2024-11-014111510.1038/s43856-024-00658-wApplication of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomesSantosh Dhakal0Anna Yin1Marta Escarra-Senmarti2Zoe O. Demko3Nora Pisanic4Trevor S. Johnston5Maria Isabel Trejo-Zambrano6Kate Kruczynski7John S. Lee8Justin P. Hardick9Patrick Shea10Janna R. Shapiro11Han-Sol Park12Maclaine A. Parish13Christopher Caputo14Abhinaya Ganesan15Sarika K. Mullapudi16Stephen J. Gould17Michael J. Betenbaugh18Andrew Pekosz19Christopher D. Heaney20Annukka A. R. Antar21Yukari C. Manabe22Andrea L. Cox23Andrew H. Karaba24Felipe Andrade25Scott L. Zeger26Sabra L. Klein27W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthDivision of Rheumatology, Johns Hopkins School of MedicineDepartment of Medicine, Johns Hopkins School of MedicineDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public HealthDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public HealthDivision of Rheumatology, Johns Hopkins School of MedicineDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthDivision of Infectious Diseases, Department of Medicine, Johns Hopkins School of MedicineW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthDepartment of Medicine, Johns Hopkins School of MedicineDepartment of Biological Chemistry, Johns Hopkins School of MedicineDepartment of Chemical and Biomolecular Engineering, Advanced Mammalian Biomanufacturing Innovation Center, Johns Hopkins UniversityW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public HealthDepartment of Medicine, Johns Hopkins School of MedicineW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthDepartment of Medicine, Johns Hopkins School of MedicineDivision of Rheumatology, Johns Hopkins School of MedicineDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthAbstract Background Critically ill hospitalized patients with COVID-19 have greater antibody titers than those with mild to moderate illness, but their association with recovery or death from COVID-19 has not been characterized. Methods In a cohort study of 178 COVID-19 patients, 73 non-hospitalized and 105 hospitalized patients, mucosal swabs and plasma samples were collected at hospital enrollment and up to 3 months post-enrollment (MPE) to measure virus RNA, cytokines/chemokines, binding antibodies, ACE2 binding inhibition, and Fc effector antibody responses against SARS-CoV-2. The association of demographic variables and more than 20 serological antibody measures with intubation or death due to COVID-19 was determined using machine learning algorithms. Results Predictive models reveal that IgG binding and ACE2 binding inhibition responses at 1 MPE are positively and anti-Spike antibody-mediated complement activation at enrollment is negatively associated with an increased probability of intubation or death from COVID-19 within 3 MPE. Conclusions At enrollment, serological antibody measures are more predictive than demographic variables of subsequent intubation or death among hospitalized COVID-19 patients.https://doi.org/10.1038/s43856-024-00658-w |
| spellingShingle | Santosh Dhakal Anna Yin Marta Escarra-Senmarti Zoe O. Demko Nora Pisanic Trevor S. Johnston Maria Isabel Trejo-Zambrano Kate Kruczynski John S. Lee Justin P. Hardick Patrick Shea Janna R. Shapiro Han-Sol Park Maclaine A. Parish Christopher Caputo Abhinaya Ganesan Sarika K. Mullapudi Stephen J. Gould Michael J. Betenbaugh Andrew Pekosz Christopher D. Heaney Annukka A. R. Antar Yukari C. Manabe Andrea L. Cox Andrew H. Karaba Felipe Andrade Scott L. Zeger Sabra L. Klein Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes Communications Medicine |
| title | Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes |
| title_full | Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes |
| title_fullStr | Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes |
| title_full_unstemmed | Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes |
| title_short | Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes |
| title_sort | application of machine learning algorithms to identify serological predictors of covid 19 severity and outcomes |
| url | https://doi.org/10.1038/s43856-024-00658-w |
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