Efficacy of Human COVID-19 Immunoglobulin (pH4) for Intravenous Injection against COVID-19

doi:10.19803/j.1672-8629.2021.10.02

Abstract

Abstract: Objective To investigate the safety and efficacy of human COVID-19 immunoglobulin (pH4) (hCoVIgG pH4) for intravenous injection in the treatment of common COVID-19 by obtaining the study cohort using the propensity score matching method. Methods A retrospective study was conducted of 525 patients with COVID-19 who were hospitalized in Beijing Ditan Hospital between January 2020 and January 2021. These patients were divided into two groups: the treatment group (n=10) and control group (n=515). The propensity scores of the two groups were matched according to age, gender, clinical classification, complications and the level of C-reactive protein at admission. After matching, the demographic characteristics, clinical manifestations, laboratory examination results and other indexes were compared between the two groups, while the nucleic acid negative conversion time, imaging inflammatory absorption time and clinical improvement time of the two groups were analyzed. At the same time, artificial intelligence was used to assist pulmonary CT in analyzing pulmonary inflammation. Results Nine cases were obtained from the treatment group and from the control group respectively after matching. Compared with the control group, the nucleic acid negative conversion time and imaging inflammatory absorption time in the treatment group were significantly shortened. C-reactive protein decreased significantly (P<0.05). Conclusion hCoVIgG can help the body to clear the virus and promote the absorption of pulmonary inflammation.

Key words: SARS-CoV-2, COVID-19, immunoglobulin（pH4）, artificial intelligence, propensity score matching

CLC Number:

R978.7

SONG Meihua, ZHANG Tingyu, GE Ziruo, XU Yanli, CHEN Zhihai, QIAN Fang. Efficacy of Human COVID-19 Immunoglobulin (pH4) for Intravenous Injection against COVID-19[J]. Chinese Journal of Pharmacovigilance, 2021, 18(10): 905-909.

References

[1] Worldometers. COVID-19 Coronaviruspandemic [EB/OL].(2021-06-01) [2021-06-01]. https://www.worldometers.info/coronavirus/.
[2] Ji Y, Ma Z, Peppelenbosch MP, et al.Potential association between COVID-19 mortality and health-care resource availability[J]. Lancet Glob Health, 2020, 8(4): e480.
[3] Baud D, Qi X, Nielsen-Saines K, et al.Real estimates of mortality following COVID-19 infection[J]. Lancet Infect Dis, 2020, 20(7): 773.
[4] Li L, Zhang W, Hu Y, et al.Effect of convalescent plasma therapy on time to clinical improvement in patients with severe and life-threatening COVID-19: A randomized clinical trial[J]. JAMA, 2020, 324(5): 460-470.
[5] Ali S, Uddin SM, Ali A, et al.Production of hyperimmune anti-SARS-CoV-2 intravenous immunoglobulin from pooled COVID-19 convalescent plasma[J]. Immunotherapy, 2021, 13(5): 397-407.
[6] General Office of the National Health Commission. New Coronavirus Pneumonia Diagnosis and Treatment Plan (Trial Eighth Edition Revised Edition) [EB/OL]. (2020-04-14)[2021-04-20]. http://www.gov.cn/zhengce/zhengceku/2021-04/15/content_5599795.htm.
[7] Hansell DM, Bankier AA, Macmahon H, et al.Fleischner society: glossary of terms for thoracic imaging[J]. Radiology, 2008, 246(3): 697-722.
[8] Marano G, Vaglio S, Pupella S, et al.Convalescent plasma: new evidence for an old therapeutic tool[J]. Blood Transfus, 2016, 14(2): 152-157.
[9] Rabenau HF, Biesert L, Schmidt T, et al.SARS-coronavirus (SARS-CoV) and the safety of a solvent/detergent (S/D) treated immunoglobulin preparation[J]. Biologicals, 2005, 33(2): 95-99.
[10] Korneyeva M, Hotta J, Lebing W, et al.Enveloped virus inactivation by caprylate: a robust alternative to solvent-detergent treatment in plasma derived intermediates[J]. Biologicals, 2002, 30(2): 153-162.
[11] Liu X, Cao W, Li T. High-dose intravenous immunoglobulins in the treatment of severe acute viral pneumonia: the known mechanisms and clinical effects [EB/OL].(2020-07-14)[2021-05-01]. https://www.frontiersin.org/articles/10.3389/fimmu.2020.01660/full.
[12] Liu L, Wang P, Nair MS, et al.Potent neutralizing antibodies against multiple epitopes on SARS-CoV-2 spike[J]. Nature, 2020, 584(7821): 450-456.
[13] Van Dorp L, Acman M, Richard D, et al.Emergence of genomic diversity and recurrent mutations in SARS-CoV-2[J]. Infect Genet Evol, 2020, 83: 104351.
[14] Xiao Y, Guo YM, Liu SY.The role of medical imaging in the diagnosis and treatment of COVID-19[J]. Chinese Journal of Radiology(中华放射学杂志), 2020, 54(4): 266-268.
[15] Litjens G, Kooi T, Bejnordi BE, et al.A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42: 60-88.
[16] Liu SY, Xiao Y.Challenges and opportunities of deep learning artificial intelligence in medical imaging[J]. Chinese Journal of Radiology(中华放射学杂志), 2017, 51(12): 899-901.
[17] Cicero M, Bilbily A, Colak E, et al.Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[J]. Invest Radiol, 2017, 52(5): 281-287.
[18] Lyu ZB, Guan CS, Yan S, et al.Value of CT findings in predicting transformation of clinical types of COVID-19[J]. Journal of Capital Medical University(首都医科大学学报), 2020,41(3): 340-344.