| Literature DB >> 34782209 |
Ryan Ruiyang Ling1, Jackie Jia Lin Sim1, Felicia Liying Tan1, Bee Choo Tai2, Nicholas Syn1, Sharavan Sadasiv Mucheli3, Bingwen Eugene Fan4, Saikat Mitra5, Kollengode Ramanathan6.
Abstract
Current evidence from randomized controlled trials (RCTs) and systematic reviews on the utility of convalescent plasma (CP) in patients with coronavirus disease 2019 (COVID-19) suggests a lack of benefit. We conducted an updated meta-analysis of RCTs with trial sequential analysis to investigate whether convalescent plasma is futile in reducing mortality in patients hospitalized with COVID-19. We searched 6 databases from December 1, 2019 to August 1, 2021 for RCTs comparing the use of CP with standard of care or transfusion of non-CP standard plasma in patients with COVID-19. The risk of bias was assessed using the Cochrane Risk-of-Bias 2 Tool. Random effects (DerSimonian and Laird) meta-analyses were conducted. The primary outcome was the aggregate risk for in-hospital mortality between both arms. We conducted a trial sequential analysis (TSA) based on the pooled relative risks (RRs) for in-hospital mortality. Secondary outcomes included the pooled RR for receipt of mechanical ventilation and mean difference in hospital length of stay. We included 18 RCTs (8702 CP, 7906 control). CP was not associated with a significant mortality benefit (RR: 0.95, 95%-CI: 0.86-1.04, P = .27, high certainty). Subgroup analysis did not find any significant differences (pinteraction = 0.30) between patients who received CP within 8 days of symptom onset (RR: 0.97, 95%-CI: 0.79-1.19, P = .80), or after 8 days (RR: 0.79, 95%-CI: 0.57-1.10, P = .16). TSA based on a RR reduction of 10% from a baseline mortality of 20% found that CP was not effective, with the pooled effect within the boundary for futility. CP did not significantly reduce the requirement for mechanical ventilation (RR: 1.00, 95%-CI: 0.91-1.10, P = .99, moderate certainty) or hospital length of stay (+1.32, 95%-CI: -1.86 to +4.52, P = .42, low certainty). CP does not improve relevant clinical outcomes in patients with COVID-19, especially in severe disease. The pooled effect of mortality was within the boundary of futility, suggesting the lack of benefit of CP in patients hospitalized with COVID-19.Entities:
Keywords: Convalescent plasma; Coronavirus disease 2019; Meta-analysis; Mortality; Severe acute respiratory syndrome coronavirus 2
Mesh:
Year: 2021 PMID: 34782209 PMCID: PMC8502250 DOI: 10.1016/j.tmrv.2021.09.001
Source DB: PubMed Journal: Transfus Med Rev ISSN: 0887-7963
Fig. 1Preferred Reporting Items for Systematic Reviews and Meta-analyses Flow Diagram.
Demographics and outcomes of included studies
| Study | Year | Design | Groups | No. of patients | Patient characteristics | Medication regimen | Mortality | Follow-up duration |
|---|---|---|---|---|---|---|---|---|
| Agarwal | 2020 | CP | 235 | 52 (42-60) y | 2 × 200 mL CP, 24 h apart | 34 | 28 d | |
| Control | 229 | 52 (41-60) y | HCQ, Remdesivir, Lopinavir/Ritonavir, PRED, Dexamethasone, Hydrocortisone, Tocilizumab, Heparin, Azithromycin, Antibiotics | 31 | ||||
| Al Qahtani | 2020 | Multicenter RCT in Bahrain | CP | 20 | 52.6±14.9 y | 2 × 200 mL CP, 24 h apart | 1 | 28 d |
| Control | 20 | 50.7 ± 12.5 y | HCQ, Lopinavir/Ritonavir, Ribavirin, Azithromycin, Peginterferon, Tocilizumab, PRED, antibiotics, anticoagulation, PPI, ACE-I/ARB, CCB, B blocker, Aspirin, Diuretics, Insulin, Metformin, Thyroxine, Acetylcysteine | 2 | ||||
| Avendano- | 2020 | CP | 38 | 61.3 ± 16.3 y | 1 × 250-300 mL CP | 0 | 30 d | |
| Control | 43 | 60.3 ± 15.0 y | HCQ, Lopinavir-Ritonavir, Azithromycin, Remdesivir, Glucocorticoid, Tocilizumab, LMWH | 4 | ||||
| Bajpai | 2020 | CP | 14 | 48.1±9.1 y | 2 × 250 mL CP, 24 hours apart | 3 | 29 d | |
| Control | 15 | 48.3 ± 10.8 y | HCQ, Azithromycin, Oseltamivir, standard medications for diabetes mellitus and hypertension control | 1 | ||||
| Bennett- | 2021 | CP | 59 | 67±15.8 y | 2 × 240 mL CP over 1-4 h each | 14 (28 d) | 90 d | |
| Control | 15 | 64±17.4 y | Glucocorticoids, Remdesivir, HCQ, Tocilizumab | 4 (28 d) | ||||
| Concor | 2021 | CP | 614 | 67.8±16.0 y | 1 × 500 mL CP from 1 - 2 donors | 141 (30 days) | 90 d | |
| Control | 307 | 67.3 ± 14.8 y | Azithromycin, Systemic Corticosteroids, Antiviral Medications, Anticoagulants, Other Covid-19 medications, Other Antibiotics | 63 (30 d) | ||||
| Estcourt | 2021 | Early CP | 1120 | 60.3 ± 12.8 y | 406/1117 | 28 d | ||
| Delayed CP | 31 | 61.1 ± 17.5 y | 9 | |||||
| Control | 933 | 60.2±13.1 y | Steroids, Remdesivir, immunomodulators, Tocilizumab, Sarilumab | 354/928 | ||||
| Gharbharan | 2020 | CP | 43 | 61 (56-70) y | 1 × 300 mL CP | 6 | 60 d | |
| Control | 43 | 63 (55-77) y | EMA-approved drugs (chloroquine, azithromycin, lopinavir/ritonavir, tocilizumab, anakinra) | 11 | ||||
| Korper | 2021 | CP | 53 | 59 (53-65) y | 3 units of CP on day 1, 3, and 5 with a total median of 846 mL (824-855 mL). | 8 | 35 d | |
| Control | 52 | 62 (55-66) y | Antivirals, Steroids, Antibiotics, Vasopressors, Anticoagulants, Platelet aggregation inhibitor | 14 | ||||
| Li | 2020 | CP | 51 | 70 (62-80) y | 1 × 4-13 mL/kg of CP – 10 mL for the first 15 mins, 100 mL/h subsequently | 8 | 28 d | |
| Control | 50 | 69 (63-76) y | Antivirals, Interferon, Chinese herbal medicine, Antibacterials, Antifungals, Steroids, Human immunoglobulin | 12 | ||||
| Libster | 2018 | CP | 80 | 76.4 ± 8.7 y | Antihypertensives, antidiabetics | 2 | 25 d | |
| Control | 80 | 77.9 ± 8.4 y | Antihypertensives, antidiabetics | 4 | ||||
| O'Donnell | 2021 | CP | 150 | 60 (48-71) y | 1 × 200-250 mL CP over 2h | 19 | 28 d | |
| Control | 73 | 63 (49-72) y | Corticosteroids, Remdesivir, HCQ, Antibacterials | 18 | ||||
| Pouladzadeh | 2021 | CP | 30 | 53.5±10.3 y | 1 × 500 mL CP, 2nd unit if no improvement seen after 24 h | 3 | 2 mo | |
| Control | 30 | 57.2 ± 17.0 y | Chloroquine phosphate, Lopinavir/Ritonavir | 5 | ||||
| Rasheed | 2020 | CP | 21 | 21 ± 55.7 y | 1 × 400 mL CP over 2h | 1 | 30 d | |
| Control | 28 | 28 ± 47.8 y | HCQ, Azithromycin, PRED | 8 | ||||
| Ray | 2020 | CP | 40 | 27 males | 2 × 200 mL CP, 24 h apart | 10 | 30 d | |
| Control | 40 | Tocilizumab, Remdesivir, HCQ, AZA, Ivermectin, Doxycycline, Corticosteroids, LMWH / unfractionated heparin, Antibiotics, Antidiabetics, Antihypertensives | 14 | |||||
| RECOVERY (Horby) | 2021 | CP | 5795 | 63.6 ± 14.7 y | 1 × 275 mL CP, 2nd unit 75 mL at least 12 hrs later the following day (4675 (81%)) | 1398 | 28 d | |
| Control | 5763 | 63.4±14.6 y | Dexamethasone, Lopinavir-Ritonavir, HCQ, azithromycin, colchicine, REGN-COV2 (monoclonal neutralising antibody cocktail), aspirin, tocilizumab | 1408 | ||||
| Sekine | 2021 | CP | 80 | 59.0 (48.0 - 68.5) y | 2 × 300 mL, 48 h apart | 18 | 28 d | |
| Control | 80 | 62.0 (49.5 - 68.0) y | Glucocorticoids and Antibacterials | 13 | ||||
| Simonovich | 2020 | CP | 228 | 62.5 (53-72.5) y | 5-10 mL/kg/h of CP with an inferior limit 400 mL for patients <70 kg and a superior limit of 600 mL for those >70 kg. Median 500 mL (IQR 415-600 mL) | 25 | 30 d | |
| Control | 105 | 62 (49-71) y | Steroids, Lopinavir/Ritonavir, Tocilizumab, Ivermectin | 12 |
ACE-I/ARB, Angiotensin converting enzyme inhibitor/angiotensin receptor blocker; B blocker, beta blocker; CCB, calcium channel blocker; CP, convalescent plasma; HCQ, Hydroxychloroquine; LMWH, Low molecular weight heparin; PRED, Methylprednisolone; PPI, Proton pump inhibitor.
Fig. 2Pooled in-hospital mortality for patients receiving convalescent plasma and standard of care for COVID-19.
Fig. 3Funnel plot after correcting for small-study effects using the trim-and-fill (R0) estimator.
Fig. 4Trial sequential analysis for a baseline mortality rate of 20%. As the RECOVERY trial had found no significant benefit at a relative risk reduction (RRR) in mortality of 20%, modelled our TSA based on a 10% RRR in mortality to further elicit the effect of convalescent plasma. The required information size is 17,257, and this is not achieved. The cumulative Z-curve (red line) does not cross the boundary for conventional (light-blue dotted lines) or TSA-adjusted (upper and lower-most curves) boundaries for benefit or harm. The Z-curve is within the boundary of futility (triangular lines beginning from the middle of the graph).