Comparison of the efficacy of PSI, CURB-65, CALL and BCRSS in predicting prognosis and mortality in COVID-19 patients

Abstract

Introduction This study aimed to determine whether the PSI, CURB-65, CALL and BCRSS had any superiority over each other as a prognostic determinant in patients with COVID-19. Material and Method This prospective cohort study included patients over 18 years of age that presented to the emergency department between May 12 and August 12, 2020 and had a positive COVID-19 polymerase chain reaction (PCR) test. The PSI, CURB-65, CALL and BCRS scores were calculated. SPSS version 22 was used for all statistical analyses. Results A total of 213 patients with a positive COVID-19 PCR result were included in the study. The total 30-day mortality rate was determined as 14.08%. PSI, CURB-65, CALL and BCRSS had a statistically significant relationship with mortality (p<0.001). The best parameter in predicting mortality was determined as PSI (area under the curve: 0.900; 95% CI: 0.972-0.828). A positive correlation was found between each scoring system, both with the length of hospital stay (PSI, CURB-65, CALL and BCRSS: r=0.696, p=0; r=0.621, p=0; r=0.75, p=0; and r=0.666, p=0, respectively). Conclusion Scoring systems, which include comorbidity, vital signs as well as laboratory, imaging findings, will be more effective than other scoring systems in determining the mortality in patients with covid-19.

Keywords

• PSI
• CURB-65
• CALL
• BCRSS
• Covid-19

References

1. Barlow G, Nathwani D, Davey P. The CURB65 pneumonia severity score outperforms generic sepsis and early warning scores in predicting mortality in community-acquired pneumonia. Thorax 2007;62(3):253-9. Doi: 10.1136/thx.2006.067371.
2. Clinical management of COVID-19. 2020 (Updated 2020 mar 11; cited 2021 Feb 16). Available from: https://www.who.int/publications/i/item/clinical-management-of-covid-19.
3. Duca A, Piva S, Foca E, Latronico N, Rizzi M. Calculated Decisions: Brescia-COVID Respiratory Severity Scale (BCRSS)/Algorithm.)=6. Emerg Med Pract 2020;16:22(5 Suppl):CD1-CD2.
4. Fan G, Tu C, Zhou F, Liu Z, Wang Y, Song B et al. Comparison of severity scores for COVID-19 patients with pneumonia: a retrospective study. European Respiratory Journal 2020;56:2002113. Doi: 10.1183/13993003.02113-2020.
5. Garcia Clemente MM, Huertas JH, Fernandez AF, Escosura Munoz C, Enriquez Rodriguez AI, Martinez LP et al. Assessment of risk scores in Covid 19. Int. J. Clin. Pract2020;00:e13705. https://doi.org/10.1111/ijcp.13705.
6. Grifoni E, Valoriani A, Cei F, Vannucchi V, Moroni F, Pelagatti L et al. The CALL score for predicting outcomes in patients with COVID-19. Clin Infect Dis 2020. Doi:10.1093/cid/ciaa686.
7. Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P et al. Prediction for Progression Risk in Patients With COVID19 Pneumonia: The CALL Score. Clinical Infectious disaeses 2020;71(6):1393-9. Doi: 10.1093/cid/ciaa414.
8. Kamran S M, Mirza Z, Moeed H, Naseem A, Hussain M, Fazal Sr I et al. CALL Score and RAS Score as Predictive Models for Coronavirus Disease 2019. Cureus 2020;12(11):e11368. Doi:10.7759/cureus.11368.

9. Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax 2003;58:377-82. Lombardy Section Italian Society Infectious And Tropical Diseases. Vademecum for the treatment of people with COVID 19. Edition 2.0, 13 March 2020. Infez Med 2020;28(2):143-52.
10. Menéndez R, Martinez R, Reyes S, Mensa J, Filella X, Marcos MA et al. Biomarkers improve mortality prediction by prognostic scales in community-acquired pneumonia. Thorax 2009;64:587-91. Doi:10.1136/thx.2008.105312.
11. Moreno-Pérez O, Andres M, Leon-Ramirez J-M, Sánchez-Payá J, Rodríguez JC, Sánchez R et al. Experience with tocilizumab in severe COVID-19 pneumonia after 80 days of follow-up: A retrospective cohort study, Journal of Autoimmunity 2020;114:102523. https://doi.org/10.1016/j.jaut.2020.102523.
12. Republic of Turkey Ministry of Health. Covid-19 Information page. 2020 (Updated 2020 mar 11; cited 2021 Feb 16). Available from: https://covid19bilgi.-saglik.gov.tr/depo/rehberler/COVID-19_Rehberi.pdf.
13. Rodriguez-Nava G, Yanez-Bello MA, Trelles-Garcia DP, Chung CW, Friedman HJ, Hines DW. Performance of the quick COVID-19 severity index and the Brescia-COVID respiratory severity scale in hospitalized patients with COVID-19 in a community hospital setting. International Journal of Infectious Diseases 2021;102: 571-6. https://doi.org/10.1016/j.ijid.2020.11.003.
14. Toniati P, Piva S, Cattalini M, Garrafa E, Regola F, Castelli F et al. Tocilizumab for the treatment of severe COVID-19 pneumonia with hyperinflammatory syndrome and acute respiratory failure: A single center study of 100 patients in Brescia, Italy. Autoimmunity Reviews 2020;19( 7):102568. https://doi.org/10.1016/j.autrev.2020.102568.
15. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H et al.Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med 2020;8:475-481.
16. Zhang C, Qin L, Li K, Wang Q, Zhao Y, Xu B et al. A novel scoring system for prediction of disease severity in COVID-19. Front Cell Infect Microbiol 2020;10:318. Doi:10.3389/fcimb.2020.00318.
17. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z et al. Clinical course and risk factors for mortality of adult in patients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020;395:1054-62. Doi:10.1016/S0140-6736(20)30566-3.
18. Zhu JS, Ge P, Jiang C, Zhang Y, Li X, Zhao Z et al. Deep learning artificial intelligence analysis of clinical variables predicts mortality in COVID 19 patients. Infectious Disease. JACEP Open 2020;1:1364-73. https://doi.org/10.1002/emp2.12205.