An Algorithm for Classifying Patients Most Likely to Develop Severe Coronavirus Disease 2019 Illness

Kattan, Michael W.; Ji, Xinge; Milinovich, Alex; Adegboye, Janet; Duggal, Abhijit; Dweik, Raed A.; Khouli, Hassan; Gordon, Steve; Young, James B.; Jehi, Lara

doi:10.1097/cce.0000000000000300

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…5,6 It is currently difficult to determine which subset of patients will develop life-threatening disease and, therefore, most benefit from receiving treatment when resources are limited. 7 Early identification of these patients would allow optimal allocation of care. To that end, the objective of the current study was to identify metabolites in patient plasma that accurately predict life-threatening cases of COVID-19 before the onset of severe symptoms.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

Sindelar

Stancliffe

Schwaiger-Haber

et al. 2021

Cell Reports Medicine

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There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determine disease severity. Through analysis of longitudinal samples, we confirm that the majority of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.

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Section: Introductionmentioning

confidence: 99%

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

Sindelar

Stancliffe

Schwaiger-Haber

et al. 2021

Cell Reports Medicine

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“…Thus, predictions for COVID hospital occupancy should be used as an important input in hospital management not only to avoid the health system collapse but to recover the resource situation pre-COVID-19 [ 9 ]. While a large number of freely available tools have been designed for patient-level predictions [ [10] , [11] , [12] , [13] ], only a few predict hospital occupancy [ 9 , 14 ]. Therefore, tools focused on cohort-level predictions are needed for COVID-19 management.…”

Section: Introductionmentioning

confidence: 99%

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

Lafuente

López

Mateo

et al. 2023

Heliyon

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“…Nearly 80% of patients affected by COVID-19 have only mild symptoms, recovering with conventional medical treatment or even without any treatment [ 7 , 8 ]. Around 20% of affected patients, however, develop respiratory distress, requiring oxygen therapy or even mechanical ventilation, and nearly 10% of them must be admitted to intensive care units (ICUs) [ 9 ]. Moreover, the mortality of late-stage ARDS precipitated by COVID-19 is remarkably high.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Plasma Proteins Involved in Inflammation, Immune Response/Complement System, and Blood Coagulation upon Admission of COVID-19 Patients to Hospital May Help to Predict the Prognosis of the Disease

Flora

Dionízio

Pereira

et al. 2023

Cells

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The development of new approaches allowing for the early assessment of COVID-19 cases that are likely to become critical and the discovery of new therapeutic targets are urgently required. In this prospective cohort study, we performed proteomic and laboratory profiling of plasma from 163 COVID-19 patients admitted to Bauru State Hospital (Brazil) between 4 May 2020 and 4 July 2020. Plasma samples were collected upon admission for routine laboratory analyses and shotgun quantitative label-free proteomics. Based on the course of the disease, the patients were divided into three groups: (a) mild (n = 76) and (b) severe (n = 56) symptoms, whose patients were discharged without or with admission to an intensive care unit (ICU), respectively, and (c) critical (n = 31), a group consisting of patients who died after admission to an ICU. Based on our data, potential therapies for COVID-19 should target proteins involved in inflammation, the immune response and complement system, and blood coagulation. Other proteins that could potentially be employed in therapies against COVID-19 but that so far have not been associated with the disease are CD5L, VDBP, A1BG, C4BPA, PGLYRP2, SERPINC1, and APOH. Targeting these proteins’ pathways might constitute potential new therapies or biomarkers of prognosis of the disease.

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An Algorithm for Classifying Patients Most Likely to Develop Severe Coronavirus Disease 2019 Illness

Abstract: Supplemental Digital Content is available in the text.

Cited by 6 publications

References 22 publications

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

Analysis of Plasma Proteins Involved in Inflammation, Immune Response/Complement System, and Blood Coagulation upon Admission of COVID-19 Patients to Hospital May Help to Predict the Prognosis of the Disease

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