Validating a Proteomic Signature of Severe COVID-19

OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Christopher V. Cosgriff, MD, MPH [verfasserIn] Todd A. Miano, PhD, PharmD [verfasserIn] Divij Mathew, PhD [verfasserIn] Alexander C. Huang, MD, PhD [verfasserIn] Heather M. Giannini, MD, MS [verfasserIn] Leticia Kuri-Cervantes, PhD [verfasserIn] M. Betina Pampena, PhD [verfasserIn] Caroline A. G. Ittner, PhD [verfasserIn] Ariel R. Weisman, MS [verfasserIn] Roseline S. Agyekum, BS [verfasserIn] Thomas G. Dunn, BS [verfasserIn] Oluwatosin Oniyide, BS [verfasserIn] Alexandra P. Turner, BS [verfasserIn] Kurt D’Andrea, BS [verfasserIn] Sharon Adamski, BS [verfasserIn] Allison R. Greenplate, PhD [verfasserIn] Brian J. Anderson, MD, MSCE [verfasserIn] Michael O. Harhay, PhD [verfasserIn] Tiffanie K. Jones, MD, MPH, MSCE [verfasserIn] John P. Reilly, MD, MSCE [verfasserIn] Nilam S. Mangalmurti, MD [verfasserIn] Michael G. S. Shashaty, MD, MSCE [verfasserIn] Michael R. Betts, PhD [verfasserIn] E. John Wherry, PhD [verfasserIn] Nuala J. Meyer, MD, MS [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Critical Care Explorations - Wolters Kluwer, 2020, 4(2022), 12, p e0800
Übergeordnetes Werk:	volume:4 ; year:2022 ; number:12, p e0800

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1097/CCE.0000000000000800

Katalog-ID:	DOAJ020752474

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520			\|a OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.
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700	0		\|a Divij Mathew, PhD \|e verfasserin \|4 aut
700	0		\|a Alexander C. Huang, MD, PhD \|e verfasserin \|4 aut
700	0		\|a Heather M. Giannini, MD, MS \|e verfasserin \|4 aut
700	0		\|a Leticia Kuri-Cervantes, PhD \|e verfasserin \|4 aut
700	0		\|a M. Betina Pampena, PhD \|e verfasserin \|4 aut
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700	0		\|a Sharon Adamski, BS \|e verfasserin \|4 aut
700	0		\|a Allison R. Greenplate, PhD \|e verfasserin \|4 aut
700	0		\|a Brian J. Anderson, MD, MSCE \|e verfasserin \|4 aut
700	0		\|a Michael O. Harhay, PhD \|e verfasserin \|4 aut
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allfields	10.1097/CCE.0000000000000800 doi (DE-627)DOAJ020752474 (DE-599)DOAJ987a6075e22c4b8b8c873ee3a0bd699e DE-627 ger DE-627 rakwb eng RC86-88.9 Christopher V. Cosgriff, MD, MPH verfasserin aut Validating a Proteomic Signature of Severe COVID-19 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia. Medical emergencies. Critical care. Intensive care. First aid Todd A. Miano, PhD, PharmD verfasserin aut Divij Mathew, PhD verfasserin aut Alexander C. Huang, MD, PhD verfasserin aut Heather M. Giannini, MD, MS verfasserin aut Leticia Kuri-Cervantes, PhD verfasserin aut M. Betina Pampena, PhD verfasserin aut Caroline A. G. Ittner, PhD verfasserin aut Ariel R. Weisman, MS verfasserin aut Roseline S. Agyekum, BS verfasserin aut Thomas G. Dunn, BS verfasserin aut Oluwatosin Oniyide, BS verfasserin aut Alexandra P. Turner, BS verfasserin aut Kurt D’Andrea, BS verfasserin aut Sharon Adamski, BS verfasserin aut Allison R. Greenplate, PhD verfasserin aut Brian J. Anderson, MD, MSCE verfasserin aut Michael O. Harhay, PhD verfasserin aut Tiffanie K. Jones, MD, MPH, MSCE verfasserin aut John P. Reilly, MD, MSCE verfasserin aut Nilam S. Mangalmurti, MD verfasserin aut Michael G. S. Shashaty, MD, MSCE verfasserin aut Michael R. Betts, PhD verfasserin aut E. John Wherry, PhD verfasserin aut Nuala J. Meyer, MD, MS verfasserin aut In Critical Care Explorations Wolters Kluwer, 2020 4(2022), 12, p e0800 (DE-627)1694211630 26398028 nnns volume:4 year:2022 number:12, p e0800 https://doi.org/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/article/987a6075e22c4b8b8c873ee3a0bd699e kostenfrei http://journals.lww.com/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/toc/2639-8028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 12, p e0800
spelling	10.1097/CCE.0000000000000800 doi (DE-627)DOAJ020752474 (DE-599)DOAJ987a6075e22c4b8b8c873ee3a0bd699e DE-627 ger DE-627 rakwb eng RC86-88.9 Christopher V. Cosgriff, MD, MPH verfasserin aut Validating a Proteomic Signature of Severe COVID-19 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia. Medical emergencies. Critical care. Intensive care. First aid Todd A. Miano, PhD, PharmD verfasserin aut Divij Mathew, PhD verfasserin aut Alexander C. Huang, MD, PhD verfasserin aut Heather M. Giannini, MD, MS verfasserin aut Leticia Kuri-Cervantes, PhD verfasserin aut M. Betina Pampena, PhD verfasserin aut Caroline A. G. Ittner, PhD verfasserin aut Ariel R. Weisman, MS verfasserin aut Roseline S. Agyekum, BS verfasserin aut Thomas G. Dunn, BS verfasserin aut Oluwatosin Oniyide, BS verfasserin aut Alexandra P. Turner, BS verfasserin aut Kurt D’Andrea, BS verfasserin aut Sharon Adamski, BS verfasserin aut Allison R. Greenplate, PhD verfasserin aut Brian J. Anderson, MD, MSCE verfasserin aut Michael O. Harhay, PhD verfasserin aut Tiffanie K. Jones, MD, MPH, MSCE verfasserin aut John P. Reilly, MD, MSCE verfasserin aut Nilam S. Mangalmurti, MD verfasserin aut Michael G. S. Shashaty, MD, MSCE verfasserin aut Michael R. Betts, PhD verfasserin aut E. John Wherry, PhD verfasserin aut Nuala J. Meyer, MD, MS verfasserin aut In Critical Care Explorations Wolters Kluwer, 2020 4(2022), 12, p e0800 (DE-627)1694211630 26398028 nnns volume:4 year:2022 number:12, p e0800 https://doi.org/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/article/987a6075e22c4b8b8c873ee3a0bd699e kostenfrei http://journals.lww.com/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/toc/2639-8028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 12, p e0800
allfields_unstemmed	10.1097/CCE.0000000000000800 doi (DE-627)DOAJ020752474 (DE-599)DOAJ987a6075e22c4b8b8c873ee3a0bd699e DE-627 ger DE-627 rakwb eng RC86-88.9 Christopher V. Cosgriff, MD, MPH verfasserin aut Validating a Proteomic Signature of Severe COVID-19 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia. Medical emergencies. Critical care. Intensive care. First aid Todd A. Miano, PhD, PharmD verfasserin aut Divij Mathew, PhD verfasserin aut Alexander C. Huang, MD, PhD verfasserin aut Heather M. Giannini, MD, MS verfasserin aut Leticia Kuri-Cervantes, PhD verfasserin aut M. Betina Pampena, PhD verfasserin aut Caroline A. G. Ittner, PhD verfasserin aut Ariel R. Weisman, MS verfasserin aut Roseline S. Agyekum, BS verfasserin aut Thomas G. Dunn, BS verfasserin aut Oluwatosin Oniyide, BS verfasserin aut Alexandra P. Turner, BS verfasserin aut Kurt D’Andrea, BS verfasserin aut Sharon Adamski, BS verfasserin aut Allison R. Greenplate, PhD verfasserin aut Brian J. Anderson, MD, MSCE verfasserin aut Michael O. Harhay, PhD verfasserin aut Tiffanie K. Jones, MD, MPH, MSCE verfasserin aut John P. Reilly, MD, MSCE verfasserin aut Nilam S. Mangalmurti, MD verfasserin aut Michael G. S. Shashaty, MD, MSCE verfasserin aut Michael R. Betts, PhD verfasserin aut E. John Wherry, PhD verfasserin aut Nuala J. Meyer, MD, MS verfasserin aut In Critical Care Explorations Wolters Kluwer, 2020 4(2022), 12, p e0800 (DE-627)1694211630 26398028 nnns volume:4 year:2022 number:12, p e0800 https://doi.org/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/article/987a6075e22c4b8b8c873ee3a0bd699e kostenfrei http://journals.lww.com/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/toc/2639-8028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 12, p e0800
allfieldsGer	10.1097/CCE.0000000000000800 doi (DE-627)DOAJ020752474 (DE-599)DOAJ987a6075e22c4b8b8c873ee3a0bd699e DE-627 ger DE-627 rakwb eng RC86-88.9 Christopher V. Cosgriff, MD, MPH verfasserin aut Validating a Proteomic Signature of Severe COVID-19 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia. Medical emergencies. Critical care. Intensive care. First aid Todd A. Miano, PhD, PharmD verfasserin aut Divij Mathew, PhD verfasserin aut Alexander C. Huang, MD, PhD verfasserin aut Heather M. Giannini, MD, MS verfasserin aut Leticia Kuri-Cervantes, PhD verfasserin aut M. Betina Pampena, PhD verfasserin aut Caroline A. G. Ittner, PhD verfasserin aut Ariel R. Weisman, MS verfasserin aut Roseline S. Agyekum, BS verfasserin aut Thomas G. Dunn, BS verfasserin aut Oluwatosin Oniyide, BS verfasserin aut Alexandra P. Turner, BS verfasserin aut Kurt D’Andrea, BS verfasserin aut Sharon Adamski, BS verfasserin aut Allison R. Greenplate, PhD verfasserin aut Brian J. Anderson, MD, MSCE verfasserin aut Michael O. Harhay, PhD verfasserin aut Tiffanie K. Jones, MD, MPH, MSCE verfasserin aut John P. Reilly, MD, MSCE verfasserin aut Nilam S. Mangalmurti, MD verfasserin aut Michael G. S. Shashaty, MD, MSCE verfasserin aut Michael R. Betts, PhD verfasserin aut E. John Wherry, PhD verfasserin aut Nuala J. Meyer, MD, MS verfasserin aut In Critical Care Explorations Wolters Kluwer, 2020 4(2022), 12, p e0800 (DE-627)1694211630 26398028 nnns volume:4 year:2022 number:12, p e0800 https://doi.org/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/article/987a6075e22c4b8b8c873ee3a0bd699e kostenfrei http://journals.lww.com/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/toc/2639-8028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 12, p e0800
allfieldsSound	10.1097/CCE.0000000000000800 doi (DE-627)DOAJ020752474 (DE-599)DOAJ987a6075e22c4b8b8c873ee3a0bd699e DE-627 ger DE-627 rakwb eng RC86-88.9 Christopher V. Cosgriff, MD, MPH verfasserin aut Validating a Proteomic Signature of Severe COVID-19 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia. Medical emergencies. Critical care. Intensive care. First aid Todd A. Miano, PhD, PharmD verfasserin aut Divij Mathew, PhD verfasserin aut Alexander C. Huang, MD, PhD verfasserin aut Heather M. Giannini, MD, MS verfasserin aut Leticia Kuri-Cervantes, PhD verfasserin aut M. Betina Pampena, PhD verfasserin aut Caroline A. G. Ittner, PhD verfasserin aut Ariel R. Weisman, MS verfasserin aut Roseline S. Agyekum, BS verfasserin aut Thomas G. Dunn, BS verfasserin aut Oluwatosin Oniyide, BS verfasserin aut Alexandra P. Turner, BS verfasserin aut Kurt D’Andrea, BS verfasserin aut Sharon Adamski, BS verfasserin aut Allison R. Greenplate, PhD verfasserin aut Brian J. Anderson, MD, MSCE verfasserin aut Michael O. Harhay, PhD verfasserin aut Tiffanie K. Jones, MD, MPH, MSCE verfasserin aut John P. Reilly, MD, MSCE verfasserin aut Nilam S. Mangalmurti, MD verfasserin aut Michael G. S. Shashaty, MD, MSCE verfasserin aut Michael R. Betts, PhD verfasserin aut E. John Wherry, PhD verfasserin aut Nuala J. Meyer, MD, MS verfasserin aut In Critical Care Explorations Wolters Kluwer, 2020 4(2022), 12, p e0800 (DE-627)1694211630 26398028 nnns volume:4 year:2022 number:12, p e0800 https://doi.org/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/article/987a6075e22c4b8b8c873ee3a0bd699e kostenfrei http://journals.lww.com/10.1097/CCE.0000000000000800 kostenfrei https://doaj.org/toc/2639-8028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 12, p e0800
language	English
source	In Critical Care Explorations 4(2022), 12, p e0800 volume:4 year:2022 number:12, p e0800
sourceStr	In Critical Care Explorations 4(2022), 12, p e0800 volume:4 year:2022 number:12, p e0800
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Medical emergencies. Critical care. Intensive care. First aid
isfreeaccess_bool	true
container_title	Critical Care Explorations
authorswithroles_txt_mv	Christopher V. Cosgriff, MD, MPH @@aut@@ Todd A. Miano, PhD, PharmD @@aut@@ Divij Mathew, PhD @@aut@@ Alexander C. Huang, MD, PhD @@aut@@ Heather M. Giannini, MD, MS @@aut@@ Leticia Kuri-Cervantes, PhD @@aut@@ M. Betina Pampena, PhD @@aut@@ Caroline A. G. Ittner, PhD @@aut@@ Ariel R. Weisman, MS @@aut@@ Roseline S. Agyekum, BS @@aut@@ Thomas G. Dunn, BS @@aut@@ Oluwatosin Oniyide, BS @@aut@@ Alexandra P. Turner, BS @@aut@@ Kurt D’Andrea, BS @@aut@@ Sharon Adamski, BS @@aut@@ Allison R. Greenplate, PhD @@aut@@ Brian J. Anderson, MD, MSCE @@aut@@ Michael O. Harhay, PhD @@aut@@ Tiffanie K. Jones, MD, MPH, MSCE @@aut@@ John P. Reilly, MD, MSCE @@aut@@ Nilam S. Mangalmurti, MD @@aut@@ Michael G. S. Shashaty, MD, MSCE @@aut@@ Michael R. Betts, PhD @@aut@@ E. John Wherry, PhD @@aut@@ Nuala J. Meyer, MD, MS @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	1694211630
id	DOAJ020752474
language_de	englisch
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Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. 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callnumber-first	R - Medicine
author	Christopher V. Cosgriff, MD, MPH
spellingShingle	Christopher V. Cosgriff, MD, MPH misc RC86-88.9 misc Medical emergencies. Critical care. Intensive care. First aid Validating a Proteomic Signature of Severe COVID-19
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topic_title	RC86-88.9 Validating a Proteomic Signature of Severe COVID-19
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title	Validating a Proteomic Signature of Severe COVID-19
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title_full	Validating a Proteomic Signature of Severe COVID-19
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author_browse	Christopher V. Cosgriff, MD, MPH Todd A. Miano, PhD, PharmD Divij Mathew, PhD Alexander C. Huang, MD, PhD Heather M. Giannini, MD, MS Leticia Kuri-Cervantes, PhD M. Betina Pampena, PhD Caroline A. G. Ittner, PhD Ariel R. Weisman, MS Roseline S. Agyekum, BS Thomas G. Dunn, BS Oluwatosin Oniyide, BS Alexandra P. Turner, BS Kurt D’Andrea, BS Sharon Adamski, BS Allison R. Greenplate, PhD Brian J. Anderson, MD, MSCE Michael O. Harhay, PhD Tiffanie K. Jones, MD, MPH, MSCE John P. Reilly, MD, MSCE Nilam S. Mangalmurti, MD Michael G. S. Shashaty, MD, MSCE Michael R. Betts, PhD E. John Wherry, PhD Nuala J. Meyer, MD, MS
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author-letter	Christopher V. Cosgriff, MD, MPH
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title_sort	validating a proteomic signature of severe covid-19
callnumber	RC86-88.9
title_auth	Validating a Proteomic Signature of Severe COVID-19
abstract	OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.
abstractGer	OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.
abstract_unstemmed	OBJECTIVES:. COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN:. Prospective observational cohort study. SETTING:. Two hospitals in the United States. PATIENTS:. One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION:. None. MEASUREMENTS AND MAIN RESULTS:. We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.
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title_short	Validating a Proteomic Signature of Severe COVID-19
url	https://doi.org/10.1097/CCE.0000000000000800 https://doaj.org/article/987a6075e22c4b8b8c873ee3a0bd699e http://journals.lww.com/10.1097/CCE.0000000000000800 https://doaj.org/toc/2639-8028
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author2	Todd A. Miano, PhD, PharmD Divij Mathew, PhD Alexander C. Huang, MD, PhD Heather M. Giannini, MD, MS Leticia Kuri-Cervantes, PhD M. Betina Pampena, PhD Caroline A. G. Ittner, PhD Ariel R. Weisman, MS Roseline S. Agyekum, BS Thomas G. Dunn, BS Oluwatosin Oniyide, BS Alexandra P. Turner, BS Kurt D’Andrea, BS Sharon Adamski, BS Allison R. Greenplate, PhD Brian J. Anderson, MD, MSCE Michael O. Harhay, PhD Tiffanie K. Jones, MD, MPH, MSCE John P. Reilly, MD, MSCE Nilam S. Mangalmurti, MD Michael G. S. Shashaty, MD, MSCE Michael R. Betts, PhD E. John Wherry, PhD Nuala J. Meyer, MD, MS
author2Str	Todd A. Miano, PhD, PharmD Divij Mathew, PhD Alexander C. Huang, MD, PhD Heather M. Giannini, MD, MS Leticia Kuri-Cervantes, PhD M. Betina Pampena, PhD Caroline A. G. Ittner, PhD Ariel R. Weisman, MS Roseline S. Agyekum, BS Thomas G. Dunn, BS Oluwatosin Oniyide, BS Alexandra P. Turner, BS Kurt D’Andrea, BS Sharon Adamski, BS Allison R. Greenplate, PhD Brian J. Anderson, MD, MSCE Michael O. Harhay, PhD Tiffanie K. Jones, MD, MPH, MSCE John P. Reilly, MD, MSCE Nilam S. Mangalmurti, MD Michael G. S. Shashaty, MD, MSCE Michael R. Betts, PhD E. John Wherry, PhD Nuala J. Meyer, MD, MS
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We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS:. Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. 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Validating a Proteomic Signature of Severe COVID-19

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