Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients
<h3>Introduction</h3><p dir="ltr">Detection of early metabolic changes in critically-ill coronavirus disease 2019 (COVID-19) patients under invasive mechanical ventilation (IMV) at the intensive care unit (ICU) could predict recovery patterns and help in disease managemen...
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2021
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| _version_ | 1864513516136824832 |
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| author | Sara Taleb (11264352) |
| author2 | Hadi M. Yassine (4675846) Fatiha M. Benslimane (6695087) Maria K. Smatti (4675852) Sven Schuchardt (9681428) Omar Albagha (8977856) Asmaa A. Al-Thani (11264355) Ali Ait Hssain (9538617) Ilhame Diboun (3522413) Mohamed A. Elrayess (7956179) |
| author2_role | author author author author author author author author author |
| author_facet | Sara Taleb (11264352) Hadi M. Yassine (4675846) Fatiha M. Benslimane (6695087) Maria K. Smatti (4675852) Sven Schuchardt (9681428) Omar Albagha (8977856) Asmaa A. Al-Thani (11264355) Ali Ait Hssain (9538617) Ilhame Diboun (3522413) Mohamed A. Elrayess (7956179) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sara Taleb (11264352) Hadi M. Yassine (4675846) Fatiha M. Benslimane (6695087) Maria K. Smatti (4675852) Sven Schuchardt (9681428) Omar Albagha (8977856) Asmaa A. Al-Thani (11264355) Ali Ait Hssain (9538617) Ilhame Diboun (3522413) Mohamed A. Elrayess (7956179) |
| dc.date.none.fl_str_mv | 2021-08-12T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fmed.2021.733657 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Predictive_Biomarkers_of_Intensive_Care_Unit_and_Mechanical_Ventilation_Duration_in_Critically-Ill_Coronavirus_Disease_2019_Patients/25780983 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Medical biochemistry and metabolomics COVID-19 metabolomics biomarkers ICU outcome ICU management |
| dc.title.none.fl_str_mv | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Introduction</h3><p dir="ltr">Detection of early metabolic changes in critically-ill coronavirus disease 2019 (COVID-19) patients under invasive mechanical ventilation (IMV) at the intensive care unit (ICU) could predict recovery patterns and help in disease management.</p><p><br></p><h3>Methods</h3><p dir="ltr">Targeted metabolomics of serum samples from 39 COVID-19 patients under IMV in ICU was performed within 48 h of intubation and a week later. A generalized linear model (GLM) was used to identify, at both time points, metabolites and clinical traits that predict the length of stay (LOS) at ICU (short ≤ 14 days/long >14 days) as well as the duration under IMV. All models were initially trained on a set of randomly selected individuals and validated on the remaining individuals in the cohort. Further validation in recently published metabolomics data of COVID-19 severity was performed.</p><p><br></p><h3>Results</h3><p dir="ltr">A model based on hypoxanthine and betaine measured at first time point was best at predicting whether a patient is likely to experience a short or long stay at ICU [area under curve (AUC) = 0.92]. A further model based on kynurenine, 3-methylhistidine, ornithine, p-cresol sulfate, and C24.0 sphingomyelin, measured 1 week later, accurately predicted the duration of IMV (Pearson correlation = 0.94). Both predictive models outperformed Acute Physiology and Chronic Health Evaluation II (APACHE II) scores and differentiated COVID-19 severity in published data.</p><p><br></p><h3>Conclusion</h3><p dir="ltr">This study has identified specific metabolites that can predict in advance LOS and IMV, which could help in the management of COVID-19 cases at ICU.</p><p dir="ltr"><br></p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fmed.2021.733657" target="_blank">https://dx.doi.org/10.3389/fmed.2021.733657</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bd43aa81c5d6df6e8857ac0361726dae |
| identifier_str_mv | 10.3389/fmed.2021.733657 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25780983 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 PatientsSara Taleb (11264352)Hadi M. Yassine (4675846)Fatiha M. Benslimane (6695087)Maria K. Smatti (4675852)Sven Schuchardt (9681428)Omar Albagha (8977856)Asmaa A. Al-Thani (11264355)Ali Ait Hssain (9538617)Ilhame Diboun (3522413)Mohamed A. Elrayess (7956179)Biomedical and clinical sciencesMedical biochemistry and metabolomicsCOVID-19metabolomicsbiomarkersICU outcomeICU management<h3>Introduction</h3><p dir="ltr">Detection of early metabolic changes in critically-ill coronavirus disease 2019 (COVID-19) patients under invasive mechanical ventilation (IMV) at the intensive care unit (ICU) could predict recovery patterns and help in disease management.</p><p><br></p><h3>Methods</h3><p dir="ltr">Targeted metabolomics of serum samples from 39 COVID-19 patients under IMV in ICU was performed within 48 h of intubation and a week later. A generalized linear model (GLM) was used to identify, at both time points, metabolites and clinical traits that predict the length of stay (LOS) at ICU (short ≤ 14 days/long >14 days) as well as the duration under IMV. All models were initially trained on a set of randomly selected individuals and validated on the remaining individuals in the cohort. Further validation in recently published metabolomics data of COVID-19 severity was performed.</p><p><br></p><h3>Results</h3><p dir="ltr">A model based on hypoxanthine and betaine measured at first time point was best at predicting whether a patient is likely to experience a short or long stay at ICU [area under curve (AUC) = 0.92]. A further model based on kynurenine, 3-methylhistidine, ornithine, p-cresol sulfate, and C24.0 sphingomyelin, measured 1 week later, accurately predicted the duration of IMV (Pearson correlation = 0.94). Both predictive models outperformed Acute Physiology and Chronic Health Evaluation II (APACHE II) scores and differentiated COVID-19 severity in published data.</p><p><br></p><h3>Conclusion</h3><p dir="ltr">This study has identified specific metabolites that can predict in advance LOS and IMV, which could help in the management of COVID-19 cases at ICU.</p><p dir="ltr"><br></p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fmed.2021.733657" target="_blank">https://dx.doi.org/10.3389/fmed.2021.733657</a></p>2021-08-12T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fmed.2021.733657https://figshare.com/articles/journal_contribution/Predictive_Biomarkers_of_Intensive_Care_Unit_and_Mechanical_Ventilation_Duration_in_Critically-Ill_Coronavirus_Disease_2019_Patients/25780983CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/257809832021-08-12T03:00:00Z |
| spellingShingle | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients Sara Taleb (11264352) Biomedical and clinical sciences Medical biochemistry and metabolomics COVID-19 metabolomics biomarkers ICU outcome ICU management |
| status_str | publishedVersion |
| title | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients |
| title_full | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients |
| title_fullStr | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients |
| title_full_unstemmed | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients |
| title_short | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients |
| title_sort | Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients |
| topic | Biomedical and clinical sciences Medical biochemistry and metabolomics COVID-19 metabolomics biomarkers ICU outcome ICU management |