Tags

Type your tag names separated by a space and hit enter

IL-6-based mortality risk model for hospitalized patients with COVID-19.
J Allergy Clin Immunol. 2020 10; 146(4):799-807.e9.JA

Abstract

BACKGROUND

Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Because the severity of the disease is highly variable, predictive models to stratify patients according to their mortality risk are needed.

OBJECTIVE

Our aim was to develop a model able to predict the risk of fatal outcome in patients with COVID-19 that could be used easily at the time of patients' arrival at the hospital.

METHODS

We constructed a prospective cohort with 611 adult patients in whom COVID-19 was diagnosed between March 10 and April 12, 2020, in a tertiary hospital in Madrid, Spain. The analysis included 501 patients who had been discharged or had died by April 20, 2020. The capacity of several biomarkers, measured at the beginning of hospitalization, to predict mortality was assessed individually. Those biomarkers that independently contributed to improve mortality prediction were included in a multivariable risk model.

RESULTS

High IL-6 level, C-reactive protein level, lactate dehydrogenase (LDH) level, ferritin level, d-dimer level, neutrophil count, and neutrophil-to-lymphocyte ratio were all predictive of mortality (area under the curve >0.70), as were low albumin level, lymphocyte count, monocyte count, and ratio of peripheral blood oxygen saturation to fraction of inspired oxygen (SpO2/FiO2). A multivariable mortality risk model including the SpO2/FiO2 ratio, neutrophil-to-lymphocyte ratio, LDH level, IL-6 level, and age was developed and showed high accuracy for the prediction of fatal outcome (area under the curve 0.94). The optimal cutoff reliably classified patients (including patients with no initial respiratory distress) as survivors and nonsurvivors with 0.88 sensitivity and 0.89 specificity.

CONCLUSION

This mortality risk model allows early risk stratification of hospitalized patients with COVID-19 before the appearance of obvious signs of clinical deterioration, and it can be used as a tool to guide clinical decision making.

Authors+Show Affiliations

Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain. Electronic address: rocio.laguna@salud.madrid.org.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Pneumology, Hospital Universitario 12 de Octubre, Madrid, Spain.Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain; Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Madrid, Spain.Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain; Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Madrid, Spain; Department of Medicine, Universidad Complutense de Madrid, Madrid, Spain.Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain; Department of Medicine, Universidad Complutense de Madrid, Madrid, Spain; Department of Hematology, Hospital Universitario 12 de Octubre, Madrid, Spain; Centro Nacional de Investigaciones Oncológicas, Madrid, Spain; CIBERONC, Madrid, Spain.Department of Biochemistry, Hospital Universitario 12 de Octubre, Madrid, Spain.Intensive Care Unit, Hospital Universitario 12 de Octubre, Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación, Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain; Department of Immunology, Ophthalmology and ENT, Universidad Complutense de Madrid, Madrid, Spain.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32710975

Citation

Laguna-Goya, Rocio, et al. "IL-6-based Mortality Risk Model for Hospitalized Patients With COVID-19." The Journal of Allergy and Clinical Immunology, vol. 146, no. 4, 2020, pp. 799-807.e9.
Laguna-Goya R, Utrero-Rico A, Talayero P, et al. IL-6-based mortality risk model for hospitalized patients with COVID-19. J Allergy Clin Immunol. 2020;146(4):799-807.e9.
Laguna-Goya, R., Utrero-Rico, A., Talayero, P., Lasa-Lazaro, M., Ramirez-Fernandez, A., Naranjo, L., Segura-Tudela, A., Cabrera-Marante, O., Rodriguez de Frias, E., Garcia-Garcia, R., Fernández-Ruiz, M., Aguado, J. M., Martinez-Lopez, J., Lopez, E. A., Catalan, M., Serrano, A., & Paz-Artal, E. (2020). IL-6-based mortality risk model for hospitalized patients with COVID-19. The Journal of Allergy and Clinical Immunology, 146(4), 799-e9. https://doi.org/10.1016/j.jaci.2020.07.009
Laguna-Goya R, et al. IL-6-based Mortality Risk Model for Hospitalized Patients With COVID-19. J Allergy Clin Immunol. 2020;146(4):799-807.e9. PubMed PMID: 32710975.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - IL-6-based mortality risk model for hospitalized patients with COVID-19. AU - Laguna-Goya,Rocio, AU - Utrero-Rico,Alberto, AU - Talayero,Paloma, AU - Lasa-Lazaro,Maria, AU - Ramirez-Fernandez,Angel, AU - Naranjo,Laura, AU - Segura-Tudela,Alejandro, AU - Cabrera-Marante,Oscar, AU - Rodriguez de Frias,Edgar, AU - Garcia-Garcia,Rocio, AU - Fernández-Ruiz,Mario, AU - Aguado,Jose Maria, AU - Martinez-Lopez,Joaquin, AU - Lopez,Elena Ana, AU - Catalan,Mercedes, AU - Serrano,Antonio, AU - Paz-Artal,Estela, Y1 - 2020/07/22/ PY - 2020/06/16/received PY - 2020/07/13/revised PY - 2020/07/16/accepted PY - 2020/7/28/pubmed PY - 2020/10/22/medline PY - 2020/7/26/entrez KW - COVID-19 KW - IL-6 KW - mortality risk KW - predictive model SP - 799 EP - 807.e9 JF - The Journal of allergy and clinical immunology JO - J Allergy Clin Immunol VL - 146 IS - 4 N2 - BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Because the severity of the disease is highly variable, predictive models to stratify patients according to their mortality risk are needed. OBJECTIVE: Our aim was to develop a model able to predict the risk of fatal outcome in patients with COVID-19 that could be used easily at the time of patients' arrival at the hospital. METHODS: We constructed a prospective cohort with 611 adult patients in whom COVID-19 was diagnosed between March 10 and April 12, 2020, in a tertiary hospital in Madrid, Spain. The analysis included 501 patients who had been discharged or had died by April 20, 2020. The capacity of several biomarkers, measured at the beginning of hospitalization, to predict mortality was assessed individually. Those biomarkers that independently contributed to improve mortality prediction were included in a multivariable risk model. RESULTS: High IL-6 level, C-reactive protein level, lactate dehydrogenase (LDH) level, ferritin level, d-dimer level, neutrophil count, and neutrophil-to-lymphocyte ratio were all predictive of mortality (area under the curve >0.70), as were low albumin level, lymphocyte count, monocyte count, and ratio of peripheral blood oxygen saturation to fraction of inspired oxygen (SpO2/FiO2). A multivariable mortality risk model including the SpO2/FiO2 ratio, neutrophil-to-lymphocyte ratio, LDH level, IL-6 level, and age was developed and showed high accuracy for the prediction of fatal outcome (area under the curve 0.94). The optimal cutoff reliably classified patients (including patients with no initial respiratory distress) as survivors and nonsurvivors with 0.88 sensitivity and 0.89 specificity. CONCLUSION: This mortality risk model allows early risk stratification of hospitalized patients with COVID-19 before the appearance of obvious signs of clinical deterioration, and it can be used as a tool to guide clinical decision making. SN - 1097-6825 UR - https://www.unboundmedicine.com/medline/citation/32710975/IL_6_based_mortality_risk_model_for_hospitalized_patients_with_COVID_19_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0091-6749(20)31027-7 DB - PRIME DP - Unbound Medicine ER -