Value of clinical laboratory test for early prediction of mortality in patients with COVID-19: the BGM score

Authors

  • Laura Macías-Muñoz Department of Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic, Barcelona - Spain
  • Robin Wijngaard Department of Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic, Barcelona - Spain
  • Bernardino González-de la Presa Department of Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic, Barcelona - Spain
  • Jose Luis Bedini Department of Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic, Barcelona and Department of Biomedicine, University of Barcelona, Barcelona - Spain
  • Manuel Morales-Ruiz Department of Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic, Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Barcelona and Department of Biomedicine, University of Barcelona, Barcelona - Spain
  • Wladimiro Jiménez Department of Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic, Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Barcelona and Department of Biomedicine, University of Barcelona, Barcelona - Spain

DOI:

https://doi.org/10.33393/jcb.2021.2194

Keywords:

BGM score, Clinical biochemistry, COVID-19, Mortality prediction, Risk score, Serum biomarkers

Abstract

Background: COVID-19 causes high mortality and long hospitalization periods. The aim of this study was to search for new early prognostic strategies accessible to most health care centers.

Methods: Laboratory results, demographic and clinical data from 500 patients with positive SARS-CoV-2 infection were included in our study. The data set was split into training and test set prior to generating different multivariate models considering the occurrence of death as the response variable. A final computational method called the BGM score was obtained by combining the previous models and is available as an interactive web application.

Results: The logistic regression model comprising age, creatinine (CREA), D-dimer (DD), C-reactive protein (CRP), platelet count (PLT), and troponin I (TNI) showed a sensitivity of 47.3%, a specificity of 98.7%, a kappa of 0.56, and a balanced accuracy of 0.73. The CART classification tree yielded TNI, age, DD, and CRP as the most potent early predictors of mortality (sensitivity = 68.4%, specificity = 92.5%, kappa = 0.61, and balanced accuracy = 0.80). The artificial neural network including age, CREA, DD, CRP, PLT, and TNI yielded a sensitivity of 66.7%, a specificity of 92.3%, a kappa of 0.54, and a balanced accuracy of 0.79. Finally, the BGM score surpassed the prediction accuracy performance of the independent multivariate models, yielding a sensitivity of 73.7%, a specificity of 96.5%, a kappa of 0.74, and a balanced accuracy of 0.85.

Conclusions: The BGM score may support clinicians in managing COVID-19 patients and providing focused interventions to those with an increased risk of mortality.

References

  1. The Lancet. Emerging understandings of 2019-nCoV. Lancet. 2020;395(10221):311. https://doi.org/10.1016/S0140-6736(20)30186-0 PMID:31986259
  2. Zhu N, Zhang D, Wang W, et al; China Novel Coronavirus Investigating and Research Team. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382(8):727-733. https://doi.org/10.1056/NEJMoa2001017 PMID:31978945
  3. World Health Organization. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Online https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf. (Accessed September 2020).
  4. European Centre for Disease Prevention and Control. Novel coronavirus disease 2019 (COVID-19) pandemic : increased transmission in the EU / EEA and the UK – sixth update. Online https://www.ecdc.europa.eu/sites/default/files/documents/RRA-sixth-update-Outbreak-of-novel-coronavirus-disease-2019-COVID-19.pdf. (Accessed September 2020).
  5. Richardson S, Hirsch JS, Narasimhan M, et al; the Northwell COVID-19 Research Consortium. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020;323(20):2052-2059. https://doi.org/10.1001/jama.2020.6775 PMID:32320003
  6. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507-513. https://doi.org/10.1016/S0140-6736(20)30211-7 PMID:32007143
  7. Wang D, Hu B, Hu C, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323(11):1061-1069. https://doi.org/10.1001/jama.2020.1585 PMID:32031570
  8. Hou H, Zhang B, Huang H, et al. Using IL-2R/lymphocytes for predicting the clinical progression of patients with COVID-19. Clin Exp Immunol. 2020;201(1):76-84. https://doi.org/10.1111/cei.13450 PMID:32365221
  9. Shen B, Yi X, Sun Y, et al. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. Cell. 2020;182(1):59-72.e15. https://doi.org/10.1016/j.cell.2020.05.032 PMID:32492406
  10. Messner CB, Demichev V, Wendisch D, et al. Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection. Cell Syst. 2020;11(1):11-24.e4. https://doi.org/10.1016/j.cels.2020.05.012 PMID:32619549
  11. Kuhn M. caret: Classification and Regression Training. R package version 6.0-86; 2020. Online https://CRAN.R-project.org/package=caret.
  12. Therneau T, Atkinson B, Ripley B, Ripley MB. rpart: Recursive Partitioning and Regression Trees. R Packag version 41-10; 2019. Online https://CRAN.R-project.org/package=rpart.
  13. Venables WN, Ripley BD. Modern Applied Statistics With S. 4th ed. New York: Springer; 2002. https://doi.org/10.1007/978-0-387-21706-2
  14. Chang W, Cheng J, Allaire J, Xie Y, McPherson J. shiny: Web Application Framework for R. R package version 1.5.0; 2020. Online https://CRAN.R-project.org/package=shiny.
  15. Sisó-Almirall A, Kostov B, Mas-Heredia M, et al. Prognostic factors in Spanish COVID-19 patients: A case series from Barcelona. PLoS One. 2020;15(8):e0237960. https://doi.org/10.1371/journal.pone.0237960 PMID:32822413
  16. Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844-847. https://doi.org/10.1111/jth.14768 PMID:32073213
  17. Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A meta-analysis. Clin Chim Acta. 2020;506:145-148. https://doi.org/10.1016/j.cca.2020.03.022 PMID:32178975
  18. Salamanna F, Maglio M, Landini MP, Fini M. Platelet functions and activities as potential hematologic parameters related to Coronavirus Disease 2019 (Covid-19). Platelets. 2020;31(5):627-632. https://doi.org/10.1080/09537104.2020.1762852 PMID:32397915
  19. Tersalvi G, Vicenzi M, Calabretta D, Biasco L, Pedrazzini G, Winterton D. Elevated Troponin in Patients With Coronavirus Disease 2019: possible Mechanisms. J Card Fail. 2020;26(6):470-475. https://doi.org/10.1016/j.cardfail.2020.04.009 PMID:32315733
  20. Sahu BR, Kampa RK, Padhi A, Panda AK. C-reactive protein: A promising biomarker for poor prognosis in COVID-19 infection. Clin Chim Acta. 2020;509:91-94. https://doi.org/10.1016/j.cca.2020.06.013 PMID:32511972
  21. Wang L. C-reactive protein levels in the early stage of COVID-19. Med Mal Infect. 2020;50(4):332-334. https://doi.org/10.1016/j.medmal.2020.03.007 PMID:32243911
  22. Liu Z, Long W, Tu M, et al. Lymphocyte subset (CD4+, CD8+) counts reflect the severity of infection and predict the clinical outcomes in patients with COVID-19. J Infect. 2020;81(2):318-356. https://doi.org/10.1016/j.jinf.2020.03.054 PMID:32283159
  23. Tavakolpour S, Rakhshandehroo T, Wei EX, Rashidian M. Lymphopenia during the COVID-19 infection: what it shows and what can be learned. Immunol Lett. 2020;225:31-32. https://doi.org/10.1016/j.imlet.2020.06.013 PMID:32569607
  24. Li Z, Wu M, Yao J, et al. Caution on Kidney Dysfunctions of COVID-19 Patients. Preprint https://www.medrxiv.org/content/10.1101/2020.02.08.20021212v2 (2020).
  25. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-1062. https://doi.org/10.1016/S0140-6736(20)30566-3 PMID:32171076
  26. Chang MC, Park YK, Kim BO, Park D. Risk factors for disease progression in COVID-19 patients. BMC Infect Dis. 2020;20(1):445. https://doi.org/10.1186/s12879-020-05144-x PMID:32576139
  27. Porcheddu R, Serra C, Kelvin D, Kelvin N, Rubino S. Similarity in Case Fatality Rates (CFR) of COVID-19/SARS-COV-2 in Italy and China. J Infect Dev Ctries. 2020;14(2):125-128. https://doi.org/10.3855/jidc.12600 PMID:32146445
  28. Reber AJ, Chirkova T, Kim JH, et al. Immunosenescence and challenges of vaccination against influenza in the aging population. Aging Dis. 2012;3(1):68-90. PMID:22500272
  29. Gordon A, Reingold A. The Burden of Influenza: a Complex Problem. Curr Epidemiol Rep. 2018;5(1):1-9. https://doi.org/10.1007/s40471-018-0136-1 PMID:29503792
  30. Gozalo PL, Pop-Vicas A, Feng Z, Gravenstein S, Mor V. Effect of influenza on functional decline. J Am Geriatr Soc. 2012;60(7):1260-1267. https://doi.org/10.1111/j.1532-5415.2012.04048.x PMID:22724499
  31. Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. 2020;369:m1328. https://doi.org/10.1136/bmj.m1328 PMID:32265220