Prevention of platelet transfusion refractoriness by platelet antigen gene matching: a systematic review and meta-analysis
Original Article

Prevention of platelet transfusion refractoriness by platelet antigen gene matching: a systematic review and meta-analysis

Jiayi Wang#, Xueyou Zhang#, Yong Li

Department of Blood Transfusion, The First Affiliated Hospital of Soochow University, Suzhou, China

Contributions: (I) Conception and design: J Wang; (II) Administrative support: Y Li; (III) Provision of study materials or patients: Y Li; (IV) Collection and assembly of data: X Zhang; (V) Data analysis and interpretation: J Wang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

#These authors contributed equally to this work and considered as co-first authors.

Correspondence to: Yong Li. Department of Blood Transfusion, The First Affiliated Hospital of Soochow University, Suzhou 215000, China. Email: yonglisxk@163.com.

Background: To investigate the prevention of platelet transfusion refractoriness (PTR) by platelet antigen gene matching using literature search and meta-analysis.

Methods: PubMed (2000.1–2021.8), Embase (2000.1–2021.8), Cochrane (2010.1–2021.8), and the Chinese Biomedical Literature Database CBM (2010.1–2021.8) were selected as the search database platform. The keywords (HLA/Human leukocyte antigen), (HPA/Human platelet alloantigens), (genotyping/cross-match), platelet transfusion (PLT), and (CCI/Corrected Count Increment) were used for the joint search. After the literature was screened for inclusion and exclusion criteria, the Cochrane intervention handbook was used for bias risk assessment, and Revman 5.3.5 software was used for analysis to obtain the statistical forest plot and funnel plot.

Results: The preliminary results revealed 255 publications, and seven (297 patients in total) were finally included in the quantitative analysis. A total of five publications reported comparison of the 1 h CCI index of HLA or HPA gene matching and PLT after random selection, and the heterogeneity test showed statistical difference (I2=49%, P=0.10). The combined statistical analysis results were: (MD =8.57, 95% CI: 7.30–9.80, Z=13.30, P<0.00001), and while six publications reported the effective rate index of PLT, and the heterogeneity test showed no statistical difference (I2=43%, P=0.12). The fixed effect mode was used to compare the effective rate of the two intervention methods (OR =4.90, 95% CI: 3.50–6.86, Z=9.23, P<0.00001).

Discussion: HLA or HPA gene matching can improve the increment after PLT and reduce the incidence of ineffective PLT.

Keywords: Platelet transfusion (PLT); gene matching; ineffective platelet transfusion (ineffective PLT); meta-analysis

Submitted Aug 23, 2021. Accepted for publication Oct 09, 2021.

doi: 10.21037/apm-21-2603

Introduction

Platelet transfusion (PLT) is a blood transfusion method to prevent and treat thrombocytopenia caused by disease to maintain coagulation function (1). However, platelet transfusion refractoriness (PTR) after multiple transfusions is widespread and seen when the platelet count in serum is not significantly higher after transfusion, or the platelet survival time is short. This not only wastes platelet resources, but also delays the patient’s condition and aggravates their economic burden (2). Some studies (3) in cancer patients and those with hematological diseases have statistically shown the ineffective rate of blood transfusion to be 7–34%. Among the many factors causing PTR, alloimmunization caused by antibodies to platelet surface antigens is one of the important factors, accounting for about 25% to 50% (4). Platelet alloantigens mainly include ABO blood group antigens, human leukocyte antigens (HLA), and human platelet alloantigens (HPA) (5), and blood group crossmatching is often used in clinical practice to reduce immune infusion reactions, although it cannot be completely avoided. With the development of polymerase chain reaction (PCR) molecular diagnostic technology, platelet antigen typing has been gradually stabilized and applied in clinical genotyping and polymorphism investigation (6). In this study, we investigated the role of gene matching in platelet matching and the prevention of PTR by meta-analysis. We present the following article in accordance with the PRISMA reporting checklist (available at https://dx.doi.org/10.21037/apm-21-2603).

Methods

Literature inclusion criteria

Literature type

The included publications were randomized controlled trials (RCTs), quasi-randomized controlled clinical trials (CCTs), or non-randomized controlled trials (NRCTs). There was no requirement on whether a blind method was adopted for the study.

Study subjects

Patients included in the study were those requiring PLT due to different disease types, including acute leukemia, myelodysplastic syndrome, chronic lymphocytic leukemia, and breast cancer, regardless of age or gender, or whether PTR had occurred during PLT before.

Intervention measures

The intervention group and control group were clearly specified in the literature, HLA or HPA gene matching was performed before PLT, PCR technique was used as the genotyping technique for the intervention group, and routine serological ABO matching or random selection without any matching was performed for the control group.

Outcome measures

The following indicators were used: (I) corrected count increment (CCI), which was calculated by the observed value of CCI detected within 1–4 h after PLT: CCI = [(posttransfusion PLT count within 1 h of completion of transfusion − pretransfusion PLT count) × body surface area]/number of PLTs transfused. (II) Effective rate of PLT: PTR was defined as the CCI value <5×109/L within 1–4 h. The effective rate of PLT was calculated as (total cases − PTR cases)/total cases.

Literature exclusion criteria

(I) Case analyses, investigations, reviews, guidelines, and other literature without data indicators; (II) literature in which there were no intervention measures, or studies with missing indicators or in which the data could not be transformed or used.

Database and search strategy and literature screening

PubMed (2000.1–2021.8), Embase (2000.1–2021.8), Cochrane (2010.1–2021.8), and the Chinese Biomedical Literature Database CBM (2010.1–2021.8) were selected as the search database platforms. The keywords (HLA/Human leukocyte antigen), (HPA/Human platelet alloantigens), (genotyping/cross-match), platelet transfusion, (CCI/Corrected Count Increment) were used for joint searching to obtain the list of publications in English. Two researchers reorganized the literature list, read the title and abstract one by one according to the inclusion and exclusion criteria, eliminated obviously non-conforming literature, obtained the full text of the remaining literature for full-text reading, continued screening, and obtained the final included literature list.

Literature quality evaluation and risk of bias evaluation

The Cochrane Handbook of Interventions (7) was used to evaluate the risk of bias in the included studies, which were divided into six aspects: (I) whether randomization was performed; (II) whether a blind method was used; (III) whether there was a hidden scheme; (IV) a description of the phenomenon of loss to follow-up in the scheme; (V) selective reporting bias; (VI) other biases, which were described as “yes”, “no”, and “unclear”.

Data extraction

The two researchers read the full text and extracted literature data from the text independently, which included the following: (I) basic characteristics of the literature: publisher, publication time, fund source; (II) study characteristics: study time, study site, study type; (III) object characteristics: number of patients, gender ratio, age, primary disease; (IV) intervention characteristics: intervention method in the intervention group, intervention method in the control group; (V) outcome measures: outcome types and data. The obtained data was transformed to facilitate the subsequent meta-analysis, such as data shown in %, being transformed into an actual case number.

Statistical analysis

Revman 5.3.5 was used for the meta-analysis. Inverse variance statistics were used for continuous variables (CCI count), and mean method and 95% CI: were used to report statistics. The Mantel-Haenszel statistical method was used for binary variables (effective rate), and OR value and 95% CI: were used to report statistics. The above differences were statistically significant at P<0.05. Every publication reporting the 1h CCI count after transfusion was included for the CCI meta synthesis, and those reporting the effective rate of transfusion were included for the effective rate synthesis. Forest plot descriptive statistics were used for comparison, I2 analysis and Q test were used for literature heterogeneity, I2>50% or P<0.1 were used to indicate the heterogeneity of results, and a random effect model was used to obtain OR values, otherwise fixed effect was used to obtain OR value. If heterogeneity between publications was suggested, sensitivity analysis was performed using a subgroup analysis method, and a funnel plot was used to indicate publication bias.

Results

Literature screening results

In this study, 255 publications (PubMed 68, Embase 43, Cochrane 52, and CBM 92) were preliminarily searched. The titles and abstracts were read for duplicate removal, and 169 publications that obviously did not meet the requirements (non-controlled study, no intervention group, or no outcome measures) were removed, five of which were excluded due to a full text retrieval fail. The full text of the remaining 77 publications was then read, resulting in 70 being removed as they did not include outcome measures. This left seven publications with a total of 297 patients to be included in the final quantitative analysis, as shown in Figure 1. The basic characteristics, intervention measures, and outcome measures of the seven publications is shown in Table 1.

Figure 1 Identification and inclusion flow chart.

Table 1

Statistics of characteristics of included studies

Author Region Total samples Number of transfusions Basic characteristics of included patients Observation group Primary outcome measures
Sex ratio (male: female) Age (years) Primary disease Gene matching Control group
Seike et al. (8), 2020 Okayama, Japan 16 354 1:15 51 [42–67] Hematopoietic stem cell transplantation HLA-A, B ABO only 1 infusion effective rate
Rioux-Massé et al. (9), 2014 Minnesota, USA 64 202 12:20 39.5 [9–67] Acute myelogenous leukemia HLA-A, B1U, or B1X ABO only 1 CCI 1 h after infusion, 2 infusion effective rate
Gavva et al. (10), 2019 Washington, USA 94 489 12:82 55 [20–84] Acute leukemia HLA Random selection 1 CCI 1 h after infusion, 2 infusion effective rate
Salama et al. (11), 2014 Mansoura, Egypt 40 120 16:24 37.6±13.35 Acute myelogenous leukemia HLA-A, B Random selection 1 CCI 1 h after infusion, 2 infusion effective rate
Hyun et al. (12), 2009 Seoul, Korea 16 71 – – – HLA-A, B Random selection 1 CCI 1 h after infusion
Li et al. (13), 2007 Changsha, China 44 44 – – – HPA 1-5, 15 Random selection 1 CCI 1 h after infusion, 2 infusion effective rate
Liu et al. (14), 2014 Mudanjiang, China 23 63 – – – HPA 1-17 Random selection 1 infusion effective rate

–, not mentioned; HLA, human leukocyte antigen; HPA, human platelet alloantigen; CCI, corrected count increment.

Literature bias assessment

Only publications (11) mentioned the use of randomization, without necessarily detailing the specific method. None of the publications mentioned whether there was classification concealment or a blind method implementation, and the phenomenon of case loss was reported in only three publications (8,9,11), although the data was still complete because the case loss was not included in the outcome statistics. There were no selective reporting in all included studies results, and other risk of bias was unknown, as shown in Table 2.

Table 2

Risk bias assessment based on the Cochrane Handbook of Interventions

Study Randomization or not Hide classification Whether blind method is applied Whether the outcome data is complete Optional reporting Other bias
Seike et al. (8), 2020 N U U Y N U
Rioux-Massé et al. (9), 2014 N U U Y N U
Gavva et al. (10), 2019 N U U Y N U
Salama et al. (11), 2014 Y U U Y N U
Hyun et al. (12), 2009 N U U Y N U
LI et al. (13), 2007 U U U Y N U
Liu et al. (14), 2014 N U U Y N U

Y, yes; N, no; U, unclear.

Meta-analysis results

Comparison of 1-hour CCI between PLT after gene matching and unmatched transfusion

A total of five publications (9-13) reported the CCI index at 1 h after infusion. As the heterogeneity test showed statistical difference (I2=49%, P=0.10), publications were divided into two subgroups according to the intervention method: HLA genotyping and HPA genotyping. The internal heterogeneity of HLA genotyping subgroup showed no statistical difference (HLA matched: I2=0%, P=0.53), the fixed effect analysis was used, result showed the HLA genotyping could significantly improve the CCI value at 1 h (HLA matched: MD =9.00, 95% CI: 7.69–10.32, Z=13.44, P<0.00001. HPA matched subgroup result: MD =3.20, 95% CI: −1.40–7.80, Z=1.36, P=0.17). The merged statistical analysis results were: (MD =8.57, 95% CI: 7.30–9.83, Z=13.30, P<0.00001), as shown in Figure 2.

Figure 2 Comparative analysis of 1-h CCI between platelet transfusion after gene matching and unmatched transfusion. CCI, corrected count increment.

Comparison of the effective rate of PLT after gene matching and unmatched transfusion

This index was reported in six publications (8-11,13,14), and the heterogeneity test showed no statistically significant difference (I2=43%, P=0.12) using the fixed-effect mode, and the obtained effective rate of the two intervention methods was compared (OR =4.90, 95% CI: 3.50–6.86, Z=9.23, P<0.00001), as shown in Figure 3.

Figure 3 Comparative analysis of the effective rate of platelet transfusion after gene matching and unmatched transfusion.

Analysis of publication bias

Subgroup analysis was performed for five publications with heterogeneity in “Comparison of 1-hour CCI between PLT after gene matching and unmatched transfusion”, and there was no statistically significant internal heterogeneity between the two subgroups, suggesting that the sensitivity analysis results were stable. As there was no statistical heterogeneity in the six included publications in analysis of the infusion response rate, a funnel plot was used to represent the publication bias analysis. The literatures showed asymmetric distribution, suggesting that there may be publication bias, as shown in Figure 4.

Figure 4 Funnel plot of the comparison of the effective rate of platelet transfusion after gene matching and unmatched transfusion.

Discussion

PLT is an important supportive therapy for blood disease. However, after multiple transfusions, platelet antibodies, such as HLA antibodies and HPA antibodies, may be produced, increasing the incidence of PTR and reducing the utilization rate of platelet resources (15). Allo-antibodies against HLA and HPA could lead to allo-immune reaction against platelet antigens, which would accelerate platelet destruction and decrease the platelet count due to antibodies fixation on incompatible platelet antigens (16). In a study conducted by Liu et al. (17), 156 patients received multiple platelet transfusion, 74 of them found antibodies positive, 47.4% patients contained HLA antibodies, and 8.3% patients contained both HLA and HPA antibodies. In some cases for the immunodeficient patients graft-versus-host disease can be induced by blood products that contain live lymphocytes, it is imperative to monitor the immunological consequences of transfusion in order to deter the disadvantageous side effects (18).

One traditional immunohematological methods to reduce the transfusion reactions was the simplified sensitized erythrocyte plateleterology assay (SEPSA), in a study (19) manual Polybrene test and SEPSA test before platelet transfusion efficiently reduced immune transfusion reactions to 1.6%. Another meta analysis introduced 1,502 patients and identified ABO matching for platelet (PLT) transfusion could result in a higher PLT increment and reduce the PTR significantly (20). But these methods still could not completely avoid the occurrence of immune response, therefore, it is necessary to accurately identify platelets antigen type before PLT transfusion.

Platelets antigen type matching also called genotyping is an effective way of reducing the incidence of PTR by transfusing genotypically matched platelets to patients who produce antibodies (21). In this study, the merged effect results showed that the CCI value (1–4 h) after HLA gene matching was significantly higher than that of patients without matching (randomly selected), and the merged statistics value were MD =9.00, 95% CI: 7.69–10.32, Z=13.44, P<0.00001. The CCI (1–4 h) after HPA gene matching was also higher than that of the control group (MD =3.20, 95% CI: −1.40–7.80, Z=1.36, P=0.17), but the difference was not statistically significant. However, for the infusion response effective rate (without PTR), the results after gene matching compared with the randomly selected group were OR =4.90, 95% CI: 3.50–6.86, Z=9.23, P<0.00001, suggesting the former could improve the infusion response effective rate.

However, as compatible PLTs with the same genetic matching still cannot avoid PTR production due to antibodies during the next transfusion, the most fundamental solution is to transfuse platelets matched for ABO blood groups, HLA, and HPA to eliminate PTR caused by alloimmunization (22). In the report by Xia et al. (23), 23 patients were matched simultaneously with ABO blood groups, HLA-A and HLA-B, and HPA1-6/15, and the percentage of platelet transfusion recovery (PPR) of patients was significantly higher than that of the concurrent control group. However, due to different outcome measures, such studies were not included in this meta-analysis.

The situation of PTR for platelet transfusions patients benefits from human leukocyte antigen (HLA)-matched platelet transfusions, however, differences in ethnic background of patients and donors could hamper the availability of sufficient numbers of HLA-matched donors for all patients. In a study conducted by Kreuger et al. (24), for 10.3% patients who have African American background, fewer donors were available, which meant that more donors with the same ethnic ground were required to ensure the availability of HLA-matching transfusion.

HPAs are specific antigens carried by platelet glycoproteins, currently there are more than 22 kinds of alloantigens found, which called platelet antigen polymorphism. Some of them related to PTR. In a study by Zhou et al. (25), the HPA-1, -2, -3, -4, -5 and -15 polymorphisms were all associated with the platelet count. In the study (14), all the platelet antigen polymorphisms HPA 1-17 were engaged in matching before transfusion, which significantly reduced the antibodies and prevented PTR.

Among the controlled clinical studies included in this study, only one publication mentioned randomization (method unknown), but neither the allocation method nor the blind method was mentioned, and the overall quality was low, which may have caused the implementation bias in the analysis. As only 297 patients were included for further analysis, with few samples, the funnel plot showed asymmetric left-right distribution, suggesting there may be some risk of publication bias. Therefore, future studies on this topic still need to include more high-quality controlled clinical studies for analysis.

Conclusions

In PLT, the use of HLA or HPA gene matching can improve the increment after PLT and reduce the incidence of ineffective PLT. However, due to the small sample size and quality of included literature in this meta-analysis, more controlled clinical studies are needed for in-depth discussion.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://dx.doi.org/10.21037/apm-21-2603

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/apm-21-2603). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

  1. Estcourt L, Stanworth S, Doree C, et al. Prophylactic platelet transfusion for prevention of bleeding in patients with haematological disorders after chemotherapy and stem cell transplantation. Cochrane Database Syst Rev 2012;CD004269 [Crossref] [PubMed]
  2. Prodger CF, Rampotas A, Estcourt LJ, et al. Platelet transfusion: Alloimmunization and refractoriness. Semin Hematol 2020;57:92-9. [Crossref] [PubMed]
  3. Solves Alcaina P. Platelet Transfusion: And Update on Challenges and Outcomes. J Blood Med 2020;11:19-26. [Crossref] [PubMed]
  4. Cohn CS. Platelet transfusion refractoriness: how do I diagnose and manage? Hematology Am Soc Hematol Educ Program 2020;2020:527-32. [Crossref] [PubMed]
  5. Blandin L, Dougé A, Fayard A, et al. Platelet transfusion refractoriness and anti-HLA immunization. Transfusion 2021;61:1700-4. [Crossref] [PubMed]
  6. Sellers J, Thompson J, Guttridge MG, et al. Human platelet antigens: typing by PCR using sequence-specific primers and their distribution in blood donors resident in Wales. Eur J Immunogenet 1999;26:393-7. [Crossref] [PubMed]
  7. Cumpston M, Li T, Page MJ, et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Database Syst Rev 2019;10:ED000142 [Crossref] [PubMed]
  8. Seike K, Fujii N, Asano N, et al. Efficacy of HLA virtual cross-matched platelet transfusions for platelet transfusion refractoriness in hematopoietic stem cell transplantation. Transfusion 2020;60:473-8. [Crossref] [PubMed]
  9. Rioux-Massé B, Cohn C, Lindgren B, et al. Utilization of cross-matched or HLA-matched platelets for patients refractory to platelet transfusion. Transfusion 2014;54:3080-7. [Crossref] [PubMed]
  10. Gavva C, Barroso J, Gernsheimer T, et al. Response to random apheresis platelets versus HLA-selected platelets versus pooled platelets in HLA-sensitized patients. Transfusion 2019;59:2276-81. [Crossref] [PubMed]
  11. Salama OS, Aladl DA, El Ghannam DM, et al. Evaluation of platelet cross-matching in the management of patients refractory to platelet transfusions. Blood Transfus 2014;12:187-94. [PubMed]
  12. Hyun J, Lim YM, Park KD, et al. An evaluation of platelet transfusion response using HLA crossmatch-compatible donors in patients with platelet refractoriness. Korean J Lab Med 2009;29:481-9. [PubMed]
  13. Li S, Gui R, Wang CL. The genotype matched-type and clinical effect of platelet antigen gene. Modern Medicine & Health 2007:09.
  14. Liu BX, Liu YJ, Gao GP, et al. Applications of platelet antigen gene typing and matching in the platelet transfusion. Zhongguo Shi Yan Xue Ye Xue Za Zhi 2014;22:199-203. [PubMed]
  15. Meinke S, Karlström C, Höglund P. Complement as an Immune Barrier in Platelet Transfusion Refractoriness. Transfus Med Rev 2019;33:231-5. [Crossref] [PubMed]
  16. Basire A, Picard C. Platelet allo-antibodies identification strategies for preventing and managing platelet refractoriness. Transfus Clin Biol 2014;21:193-206. [Crossref] [PubMed]
  17. Liu D, Lu P, Wang J, et al. A study of platelet specific antibody (anti-Sib(a)). Chin Med J (Engl) 1996;109:615-7. [PubMed]
  18. Petrányi GG, Réti M, Harsányi V, et al. Immunologic consequences of blood transfusion and their clinical manifestations. Int Arch Allergy Immunol 1997;114:303-15. [Crossref] [PubMed]
  19. Liu DZ, Zhu J, Zhu ZY, et al. Analysis and prevention of transfusion reactions in Shanghai region China. Chinese Journal of Blood Transfusion 2002;159-61.
  20. Shehata N, Tinmouth A, Naglie G, et al. ABO-identical versus nonidentical platelet transfusion: a systematic review. Transfusion 2009;49:2442-53. [Crossref] [PubMed]
  21. Kekomäki S, Volin L, Koistinen P, et al. Successful treatment of platelet transfusion refractoriness: the use of platelet transfusions matched for both human leucocyte antigens (HLA) and human platelet alloantigens (HPA) in alloimmunized patients with leukaemia. Eur J Haematol 1998;60:112-8. [Crossref] [PubMed]
  22. Saris A, Pavenski K. Human Leukocyte Antigen Alloimmunization and Alloimmune Platelet Refractoriness. Transfus Med Rev 2020;34:250-7. [Crossref] [PubMed]
  23. Xia WJ, Ye X, Tian LW, et al. Establishment of platelet donor registry improves the treatment of platelet transfusion refractoriness in Guangzhou region of China. Transfus Med 2010;20:269-74. [Crossref] [PubMed]
  24. Kreuger AL, Haasnoot GW, Somers JAE, et al. Ensuring HLA-matched platelet support requires an ethnic diverse donor population. Transfusion 2020;60:940-6. [Crossref] [PubMed]
  25. Zhou S, Liang X, Wang N, et al. Association of human platelet antigen polymorphisms with platelet count and mean platelet volume. Hematology 2018;23:517-21. [Crossref] [PubMed]

(English Language Editor: B. Draper)

Cite this article as: Wang J, Zhang X, Li Y. Prevention of platelet transfusion refractoriness by platelet antigen gene matching: a systematic review and meta-analysis. Ann Palliat Med 2021;10(10):10946-10953. doi: 10.21037/apm-21-2603

Article Options

Download Citation

Share