| Literature DB >> 33800528 |
Monika Maria Biernat1, Anna Kolasińska1, Jacek Kwiatkowski1, Donata Urbaniak-Kujda1, Paweł Biernat2, Justyna Janocha-Litwin3, Małgorzata Szymczyk-Nużka4, Dawid Bursy2, Elżbieta Kalicińska1, Krzysztof Simon3, Grzegorz Mazur5, Tomasz Wróbel1.
Abstract
The use of convalescent plasma in the treatment of COVID-19 may lead to a milder course of infection and has been associated with improved outcomes. Determining optimal treatments in high risk populations is crucial, as is the case in those with hematological malignancies. We analyzed a cohort of 23 patients with hematological malignancies and COVID-19 who had received plasma 48-72 h after the diagnosis of infection and compared it with a historical group of 22 patients who received other therapy. Overall survival in those who received convalescent plasma was significantly higher than in the historical group (p = 0.03460). The plasma-treated group also showed a significantly milder course of infection (p = 0.03807), characterized by less severe symptoms and faster recovery (p = 0.00001). In conclusion, we have demonstrated that convalescent plasma is an effective treatment and its early administration leads to clinical improvement, increased viral clearance and longer overall survival in patients with hematological malignancies and COVID-19. To our knowledge, this is the first report to analyze the efficacy of convalescent plasma in a cohort of patients with hematological malignancies.Entities:
Keywords: COVID-19; convalescent plasma; hematological malignancies
Mesh:
Year: 2021 PMID: 33800528 PMCID: PMC8001057 DOI: 10.3390/v13030436
Source DB: PubMed Journal: Viruses ISSN: 1999-4915 Impact factor: 5.048
Baseline clinical characteristics, laboratory data and outcome of patients with COVID-19.
| Variable | Treatment Group of Patients, | Control (Historical) Group of Patients, | All Patients, |
|
|---|---|---|---|---|
|
| 57 (31–72) | 62.5 (20–80) | 59 (20–80) | |
|
| 14 (61) | 14 (64) | 28 (62) | |
|
| 9 (39) | 8 (36) | 17 (38) | |
|
| ||||
|
| 14 (61) | 9 (41) | 23 (51) | |
|
| 2 (8.7) | 4 (18) | 6 (13) | |
|
| 4 (17) | 4 (18) | 8 (18) | |
|
| 2 (8.7) | 4 (18) | 6 (13) | |
|
| 1 (4.3) | 1 (4.5) | 2 (4.4) | |
|
| ||||
|
| 11 (48) | 8 (36) | 19 (42) | |
|
| 7 (30) | 9 (41) | 16 (36) | |
|
| 5 (22) | 5 (23) | 10 (22) | |
|
| ||||
|
| 4 (17) | 3 (14) | 7 (16) | |
|
| 5 (22) | 3 (14) | 8 (18) | |
|
| 14 (61) | 16 (73) | 30 (67) | |
|
| ||||
|
| 15 (65) | 15 (68) | 30 (67) | |
|
| 12 (52) | 15 (68) | 27 (60) | |
|
| 13 (57) | 10 (45) | 23 (51) | |
|
| 6 (26) | 12 (55) | 18 (40) | |
|
| 17 (74) | 18 (82) | 35 (78) | |
|
| ||||
|
| 3.4 (0.01–36.02) | 3.5 (0.02–44.7) | 3.5 (0.01–44.7) | |
|
| 0.6 (0.01–5.87) | 0.6 (0.02–3.5) | 0.6 (0.01–5.87) | |
|
| 2.3 (0.01–11.78) | 2.3 (0.01–15.8) | 2.3 (0.01–15.8) | |
|
| 73 (1–473) | 79 (1–511) | 79 (1–511) | |
|
| 9.65 (4.9–13.2) | 9.7 (6.9–13.7) | 9.7 (4.9–13.7) | |
|
| 32 (0.5–306) | 32 (2–350) | 32 (0.5–350) | |
|
| 97 (70–100) | 97 (75–100) | 97 (70–100) | |
|
| ||||
|
| 12 (52.2) | 6 (27.3) | 18 (40) | |
|
| 6 (26.1) | 3 (13.6) | 9 (20) | |
|
| 5 (21.7) | 13 (59.1) | 18 (40) | |
|
| 18 (8–28) | 37 (20–53) | 21 (8–53) | |
|
| ||||
|
| 15 (65.2) | 16 (72.7) | 31 (68.9) | |
|
| 1 (4.3) | 5 (22.7) | 4 (8.9) | |
|
| 3 (13) | 4 (18.2) | 7 (15.5) | |
|
| 23 (100) | 0 (0) | 23 (51.1) | N.D. |
|
| 0 (0) | 22 (100) | 22 (48.9) | N.D. |
|
| 8 (34.8) | 12 (54.5) | 20 (44.4) | |
|
| 0 (0) | 3 (13.6) | 3 (6.7) | N.D. |
|
| 3 (13) | 9 (41) | 12 (27) | |
* ITP—Immune thrombocytopenic purpura, aplastic anemia; ** diarrhea, nausea and vomiting, loss of smell and taste, conjunctivitis; n—number; N.D.—Not Determined.
Figure 1Kaplan–Meier analysis of hematological patients with COVID-19 who received convalescent plasma compared with patients from a historical group treated with other therapy (remdesivir, tocilizumab, hydroxychloroquine, lopinavir/ritonavir). Hazard ratio (HR) and 95% CI are calculated from a Cox model without covariates.
Figure 2The PCA analysis presented in the pca1 vs. pca2 load diagram. Analysis of the effect of applied early plasma therapy on the course of COVID-19 in hematological patients using generalized principal component analysis PCA. The PCA model was estimated using the NIPALS iterative algorithm, the convergence criterion was set at the level of 0.00001, setting the maximum number of iterations at 50. The number of components was determined by determining the maximum predictive capability Q^2 using the V-fold cross-validation method, setting the maximum number of components at the level of V_max = 7. The obtained optimal PCA model was finally reduced to 2 components.