A method for predicting the effectiveness of glucocorticoid therapy in patients with moderate COVID-19 based on simple clinical and laboratory data

Abstract

BACKGROUND: In patients hospitalized with coronavirus infection (COVID-19), methods for predicting the effectiveness of anti-inflammatory therapy have important practical implications for optimizing treatment and outcomes. To date, several indicators of COVID-19 patients (age, comorbidities, and laboratory criteria for the intensity of inflammation) have been identified to indicate a high probability of a severe course and a risk of an adverse outcome. However, the problem of predicting the effectiveness of anti-inflammatory therapy in patients with moderate COVID-19 is not well understood.

AIM: This study aimed to develop a predictive model to determine the effectiveness/failure of anti-inflammatory therapy with glucocorticosteroids (GCS) in patients with moderate COVID-19 to improve the treatment outcomes of hospitalized patients.

MATERIALS AND METHODS: This study retrospectively analyzed electronic medical record data of all patients admitted consecutively from October 1, 2020, to January 31, 2021. The study included 71 patients with probable (clinically confirmed) and confirmed (laboratory) COVID-19 of moderate course, with characteristic changes in the lungs according to computed tomography of the chest organs (CT-CCT). Given the severity of the course, all study patients were prescribed GCS in accordance with the current version of the Interim Guidelines of the Ministry of Health of the Russian Federation.

RESULTS: A total of 71 patients were studied, and 53 (74.7%) of them did not require an escalation of anti-inflammatory therapy, which is regarded as an effective use of corticosteroids as an anti-inflammatory therapy (group 1). In the remaining 18 patients, the use of corticosteroids for an average of 5.5 (3–6) days did not have a definite clinical effect and required the additional use of monoclonal antibodies (MCA) to interleukin-6 (IL-6) or to its receptor (group 2). Using logistic regression analysis and receiver operating characteristic analysis, a mathematical model was developed and evaluated to predict the outcome of anti-inflammatory corticosteroid therapy in patients with moderate COVID-19. As risk factors, indicators that had significant differences in the studied groups before GCS initiation were selected: number of lymphocytes, platelets, and body temperature. The quality of the constructed model is assessed as very good, and the optimal cutoff point is 0.697. The sensitivity index of the model is 81.1%, and the specificity index is 72.2%.

CONCLUSIONS: The mathematical model makes it possible to predict the effectiveness of GCS therapy according to the number of lymphocytes, platelets, and body temperature. The mathematical model is adequate and has a high sensitivity and specificity.

Keywords

COVID-19 / glucocorticoids hormones / treatment efficacy / risk factors / predictive model

References

[1] Drapkina OM, Maev IV, Bakulin IG, et al. Interim guidelines: Diseases of the digestive organs in the context of a new coronavirus infection pandemic (COVID-19). Profilakticheskaya Meditsina. 2020;23(3):2120–2152. (In Russ). doi: 10.17116/profmed202023032120

[2] Драпкина О.М., Маев И.В., Бакулин И.Г., и др. Временные методические рекомендации: «Болезни органов пищеварения в условиях пандемии новой коронавирусной инфекции (COVID-19)» // Профилактическая медицина. 2020. Т. 23, № 3. С. 2120–2152. doi: 10.17116/profmed202023032120

[3] Fang J, Wu Q, Ye F, et al. Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome. Front Genet. 2021;12:728960. doi: 10.3389/fgene.2021.728960

[4] Fang J., Wu Q., Ye F., et al. Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome // Front Genet. 2021. Vol. 12. P. 728960. doi: 10.3389/fgene.2021.728960

[5] Aprajita, Choudhary M. Design, synthesis and characterization of novel Ni(II) and Cu(II) complexes as antivirus drug candidates against SARS-CoV-2 and HIV virus. J Mol Struct. 2022;1263:133114. doi: 10.1016/j.molstruc.2022.133114

[6] Aprajita, Choudhary M. Design, synthesis and characterization of novel Ni(II) and Cu(II) complexes as antivirus drug candidates against SARS-CoV-2 and HIV virus // J Mol Struct. 2022. Vol. 1263. P. 133114. doi: 10.1016/j.molstruc.2022.133114

[7] Ren PX, Shang WJ, Yin WC, et al. A multi-targeting drug design strategy for identifying potent anti-SARS-CoV-2 inhibitors. Acta Pharmacol Sin. 2022;43(2):483–493. doi: 10.1038/s41401-021-00668-7

[8] Ren P.X., Shang W.J., Yin W.C., et al. A multi-targeting drug design strategy for identifying potent anti-SARS-CoV-2 inhibitors // Acta Pharmacol Sin. 2022. Vol. 43, N 2. P. 483–493. doi: 10.1038/s41401-021-00668-7

[9] Horby P, Lim WS, Emberson JR, et al.; RECOVERY Collaborative Group. Dexamethasone in Hospitalized Patients with Covid-19. N Engl J Med. 2021;384(8):693–704. doi: 10.1056/NEJMoa2021436

[10] Horby P., Lim W.S., Emberson J.R., et al.; RECOVERY Collaborative Group. Dexamethasone in Hospitalized Patients with Covid-19 // N Engl J Med. 2021. Vol. 384, N 8. P. 693–704. doi: 10.1056/NEJMoa2021436

[11] Li Y, Zhou X, Li T, et al. Corticosteroid prevents COVID-19 progression within its therapeutic window: a multicentre, proof-of-concept, observational study. Emerg Microbes Infect. 2020;9(1): 1869–1877. doi: 10.1080/22221751.2020.1807885

[12] Li Y., Zhou X., Li T., et al. Corticosteroid prevents COVID-19 progression within its therapeutic window: a multicentre, proof-of-concept, observational study // Emerg Microbes Infect. 2020. Vol. 9, N 1. P. 1869–1877. doi: 10.1080/22221751.2020.1807885

[13] Fadel R, Morrison AR, Vahia A, et al.; Henry Ford COVID-19 Management Task Force. Early Short-Course Corticosteroids in Hospitalized Patients With COVID-19. Clin Infect Dis. 2020;71(16): 2114–2120. doi: 10.1093/cid/ciaa601

[14] Fadel R., Morrison A.R., Vahia A., et al.; Henry Ford COVID-19 Management Task Force. Early Short-Course Corticosteroids in Hospitalized Patients With COVID-19 // Clin Infect Dis. 2020. Vol. 71, N 16. P. 2114–2120. doi: 10.1093/cid/ciaa601

[15] Chalmers JD, Crichton ML, Goeminne PC, et al. Management of hospitalised adults with coronavirus disease 2019 (COVID-19): a European Respiratory Society living guideline. Eur Respir J. 2021; 57(4):2100048. doi: 10.1183/13993003.00048-2021. Erratum in: Eur Respir J. 2022;60(2).

[16] Chalmers J.D., Crichton M.L., Goeminne P.C., et al. Management of hospitalised adults with coronavirus disease 2019 (COVID-19): a European Respiratory Society living guideline // Eur Respir J. 2021. Vol. 57, N 4. P. 2100048. doi: 10.1183/13993003.00048-2021. Erratum in: Eur Respir J. 2022. Vol. 60, N 2.

[17] Huang I, Pranata R, Lim MA, et al. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis. Ther Adv Respir Dis. 2020;14:1753466620937175. doi: 10.1177/1753466620937175

[18] Huang I., Pranata R., Lim M.A., et al. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis // Ther Adv Respir Dis. 2020. Vol. 14. P. 1753466620937175. doi: 10.1177/1753466620937175

[19] Choron RL, Butts CA, Bargoud C, et al. Fever in the ICU: A Predictor of Mortality in Mechanically Ventilated COVID-19 Patients. J Intensive Care Med. 2021;36(4):484–493. doi: 10.1177/0885066620979622

[20] Choron R.L., Butts C.A., Bargoud C., et al. Fever in the ICU: A Predictor of Mortality in Mechanically Ventilated COVID-19 Patients // J Intensive Care Med. 2021. Vol. 36, N 4. P. 484–493. doi: 10.1177/0885066620979622

[21] Da Rosa Mesquita R, Francelino Silva Junior LC, Santos Santana FM, et al. Clinical manifestations of COVID-19 in the general population: systematic review. Wien Klin Wochenschr. 2021; 133(7-8):377–382. doi: 10.1007/s00508-020-01760-4

[22] Da Rosa Mesquita R., Francelino Silva Junior L.C., Santos Santana F.M., et al. Clinical manifestations of COVID-19 in the general population: systematic review // Wien Klin Wochenschr. 2021. Vol. 133, N 7-8. P. 377–382. doi: 10.1007/s00508-020-01760-4

[23] He X, Cheng X, Feng X, et al. Clinical Symptom Differences Between Mild and Severe COVID-19 Patients in China: A Meta-Analysis. Front Public Health. 2021;8:561264. doi: 10.3389/fpubh.2020.561264

[24] He X., Cheng X., Feng X., et al. Clinical Symptom Differences Between Mild and Severe COVID-19 Patients in China: A Meta-Analysis // Front Public Health. 2021. Vol. 8. P. 561264. doi: 10.3389/fpubh.2020.561264

[25] Xu P, Zhou Q, Xu J. Mechanism of thrombocytopenia in COVID-19 patients. Ann Hematol. 2020;99(6):1205–1208. doi: 10.1007/s00277-020-04019-0

[26] Xu P., Zhou Q., Xu J. Mechanism of thrombocytopenia in COVID-19 patients // Ann Hematol. 2020. Vol. 99, N 6. P. 1205–1208. doi: 10.1007/s00277-020-04019-0

[27] Huang I, Pranata R. Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis. J Intensive Care. 2020;8:36. doi: 10.1186/s40560-020-00453-4

[28] Huang I., Pranata R. Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis // J Intensive Care. 2020. Vol. 8. P. 36. doi: 10.1186/s40560-020-00453-4

[29] Chew NW, Ngiam JN, Tham SM, et al. Fever as a predictor of adverse outcomes in COVID-19. QJM. 2021;114(10):706–714. doi: 10.1093/qjmed/hcab023

[30] Chew N.W., Ngiam J.N., Tham S.M., et al. Fever as a predictor of adverse outcomes in COVID-19 // QJM. 2021. Vol. 114, N 10. P. 706–714. doi: 10.1093/qjmed/hcab023

[31] Sterne JAC, Murthy S, Diaz JV, et al.; WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group. Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19: A Meta-analysis. JAMA. 2020;324(13):1330–1341. doi: 10.1001/jama.2020.17023

[32] Sterne J.A.C., Murthy S., Diaz J.V., et al.; WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group. Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19: A Meta-analysis // JAMA. 2020. Vol. 324, N 13. P. 1330–1341. doi: 10.1001/jama.2020.17023

[33] Avdeev SN, Adamyan LV, Alekseeva EI, et al. Prevention, diagnosis and treatment of novel coronavirus infection (COVID-19): interim guidelines. Version 8 (09/03/2020). Moscow; 2020. 227 p. (In Russ).

[34] Авдеев С.Н., Адамян Л.В., Алексеева Е.И., и др. Профилактика, диагностика и лечение новой коронавирусной инфекции (COVID-19): временные методические рекомендации. Версия 8 (03.09.2020). Москва, 2020. 227 с.

[35] Avdeev SN, Adamyan LV, Alekseeva EI, et al. Prevention, diagnosis and treatment of novel coronavirus infection (COVID-19): interim guidelines. Version 8 (10/26/2020). Moscow; 2020. 236 p. (In Russ).

[36] Авдеев С.Н., Адамян Л.В., Алексеева Е.И., и др. Профилактика, диагностика и лечение новой коронавирусной инфекции (COVID-19): временные методические рекомендации. Версия 9 (26.10.2020). Москва, 2020. 236 с.

[37] Trukhacheva NV. Mathematical statistics in biomedical research using the Statistica package. Moscow: GEOTAR-Media; 2013. 384 p. (In Russ).

[38] Трухачева Н.В. Математическая статистика в медико-биологических исследованиях с применением пакета Statistica. Москва: ГЭОТАР-Медиа, 2013. 384 с.

[39] Grigoryev SG, Lobzin YuV, Skripchenko NV. the Role and place of logistic regression and ROC analysis in solving medical diagnostic task. Journal Infectology. 2016;8(4):36–45. (In Russ). doi: 10.22625/2072-6732-2016-8-4-36-45

[40] Григорьев С.Г., Лобзин Ю.В., Скрипченко Н.В. Роль и место логистической регрессии и ROC-анализа в решении медицинских диагностических задач // Журнал инфектологии. 2016. Т. 8, № 4. С. 36–45. doi: 10.22625/2072-6732-2016-8-4-36-45

[41] Li J, He X, Yuan Yuan, et al. Meta-analysis investigating the relationship between clinical features, outcomes, and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Am J Infect Control. 2021;49(1):82–89. doi: 10.1016/j.ajic.2020.06.008

[42] Li J., He X., Yuan Yuan, et al. Meta-analysis investigating the relationship between clinical features, outcomes, and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia // Am J Infect Control. 2021. Vol. 49, N 1. P. 82–89. doi: 10.1016/j.ajic.2020.06.008

[43] Guo L, Wei D, Zhang X, et al. Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score. Front Microbiol. 2019;10:2752. doi: 10.3389/fmicb.2019.02752. Erratum in: Front Microbiol. 2020 Jun 09;11:1304.

[44] Guo L., Wei D., Zhang X., et al. Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score // Front Microbiol. 2019. Vol. 10. P. 2752. doi: 10.3389/fmicb.2019.02752. Erratum in: Front Microbiol. 2020. Vol. 11. P. 1304.

[45] Burian E, Jungmann F, Kaissis GA, et al. Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort. J Clin Med. 2020;9(5):1514. doi: 10.3390/jcm9051514

[46] Burian E., Jungmann F., Kaissis G.A., et al. Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort // J Clin Med. 2020. Vol. 9, N 5. P. 1514. doi: 10.3390/jcm9051514

[47] Assaf D, Gutman Y, Neuman Y, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020;15(8):1435–1443. doi: 10.1007/s11739-020-02475-0

[48] Assaf D., Gutman Y., Neuman Y., et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19 // Intern Emerg Med. 2020. Vol. 15, N 8. P. 1435–1443. doi: 10.1007/s11739-020-02475-0

[49] Liu J, Liu Y, Xiang P, et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J Transl Med. 2020;18(1):206. doi: 10.1186/s12967-020-02374-0

[50] Liu J., Liu Y., Xiang P., et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage // J Transl Med. 2020. Vol. 18, N 1. P. 206. doi: 10.1186/s12967-020-02374-0

[51] Yang X, Yang Q, Wang Y, et al. Thrombocytopenia and its association with mortality in patients with COVID-19. J Thromb Haemost. 2020;18(6):1469–1472. doi: 10.1111/jth.14848

[52] Yang X., Yang Q., Wang Y., et al. Thrombocytopenia and its association with mortality in patients with COVID-19 // J Thromb Haemost. 2020. Vol. 18, N 6. P. 1469–1472. doi: 10.1111/jth.14848

[53] Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi: 10.1136/bmj.m1328. Update in: BMJ. 2021;372:n236. Erratum in: BMJ. 2020;369:m2204.

[54] Wynants L., Van Calster B., Collins G.S., et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal // BMJ. 2020. Vol. 369. P. m1328. doi: 10.1136/bmj.m1328. Update in: BMJ. 2021. Vol. 372. P. n236. Erratum in: BMJ. 2020. Vol. 369. P. m2204.

RIGHTS & PERMISSIONS

Eco-vector