Vox Sanguinis最新文献

Background and objectives: Liquid plasma (LQP) stands out as an alternative to thawed plasma (TP) for emergent transfusions due to its longer shelf-life. We aimed to measure fibrinogen, protein C (PC), protein S (PS), factor V (FV), factor VII (FVII) and factor VIII (FVIII) activity in LQP, quantify how these factors' levels change during storage and characterize how they compare in LQP and TP.

Materials and methods: Coagulation factor activities were measured on days 15, 26 and 27 for LQP (n = 26) and Day 5 for TP (n = 31). Bayesian statistics was used to compare coagulation factor activity and quantify changes in activity during storage.

Results: Fibrinogen and PC activity on Day 26 in LQP (LQP26) was comparable to that on Day 5 in TP (TP5) with posterior mean activity of 257 versus 246 mg/dL and 100.4% versus 108.7%, respectively. FV, FVII and FVIII had lower activity in LQP26 compared to TP5, with posterior mean activities of 42.6% versus 72.0%, 55.0% versus 59.7% and 48.8% versus 59.2%, respectively. PS in LQP26 was low, with posterior mean activity of 28.0%, which was less than half that of TP5 at 66.4%. From Day 15 to Day 26, FVII in LQP decreased at a rate of 3.49% per day, whereas fibrinogen, PC, PS, FV and FVIII activity in LQP remained relatively stable.

Conclusion: LQP26 has comparable activities of fibrinogen, PC and FVII as TP5, lower activities of FV and PS and slightly lower activity of FVIII. LQP is a viable alternative for use in emergency transfusions and massive transfusion protocols.

Within the digital transformation of medicine, transfusion medicine has quietly become a big-data discipline. The long-standing tradition of blood product standardization (e.g., ISBT-128) and large donor cohorts being followed over years-some of which are sampled in national biobank projects, build a favourable setting. In parallel, recent advances in artificial intelligence (AI) and data integration facilitate efficient data use for research and clinical care. Consequently, next-generation blood services might monitor donor phenotype data and match this information to AI-predicted recipient demands and their outcomes. Here, we attempt to provide a comprehensive introduction to the possibilities and challenges of big data and AI in transfusion medicine along with data integration opportunities related to the Fast Healthcare Interoperability Resources standard. We educate on the principles of AI and the digital transformation of transfusion medicine and analyse the evidence of blood establishments as digital platforms. We illustrate possible roadmaps for data integration and how federated learning initiatives and national networks may scale value while preserving donor and patient privacy. Finally, we exemplify the ongoing transformation with precision red blood cell (RBC) diagnostics using lab-on-a-chip and the digital crossmatch. The practice of transfusion medicine is undergoing transformation and experimentally appears to profit from synergies in precision diagnostics and AI. Its translation into routine practice remains a challenge for the current decade to leverage the full potential of blood establishments as 'big-data engines'.

Background and objectives: Bacterial contamination of blood components is an ongoing problem in transfusion medicine. We analysed the bacterial screening data of platelets from England, 2014-2023, and compared this with data on reported near-misses and transfusion-transmitted infections (TTIs).

Materials and methods: Anonymized data on bacterial screening of pooled and apheresis platelet donations were reviewed, including the number of donations collected yearly, results from bacterial screening and time from sampling to detection. The findings were compared with data on near-misses and TTIs reported during the same period.

Results: Screening of 1249,513 apheresis and 1,495,707 pooled platelet donations identified bacterial contamination in 2949 donations, including 78 bacterial species. Over four-fold higher frequency of confirmed bacterial contamination was observed in pooled platelets compared to apheresis donations (0.09% [1096/1,249,513] vs. 0.02% [362/1,495,707], p < 0.0001). Rates of bacterial contamination of pooled platelet doubled during the study period. Staphylococcus aureus was the most commonly detected highly pathogenic bacterial contaminant (29/147, 19.7%; 15/29, 52% in apheresis platelets). It was also implicated in 1 confirmed case of bacterial TTI and in 8 of 10 reported bacterial near-miss cases.

Conclusion: Increasing frequencies of bacterial contamination, mostly related to skin flora, were noted in pooled platelets. Furthermore, S. aureus was notably associated with near-miss events. Our findings demonstrate a limitation of bacterial screening, with evidence of bacterial growth after platelets were likely supplied for clinical use.

Transfusion medicine generates enormous volumes of data across the vein-to-vein continuum, spanning donor characteristics, laboratory testing, component manufacturing, logistics and recipient outcomes. The emergence of big data infrastructures, coupled with artificial intelligence (AI), offers a transformative opportunity to harness this information for safer, more efficient and better personalized transfusion practices. This narrative review outlines current and potential applications of AI and machine learning (ML) at each phase of the big data pipeline in transfusion medicine, including data collection, wrangling and harmonization, validation, feature engineering, analysis, publication and knowledge mobilization. We discuss how AI-enabled methods-such as natural language processing to extract variables, anomaly detection for product quality, supervised models to predict risks, federated analysis for collaboration, and forecasting algorithms to optimize inventory and logistics-may address longstanding challenges related to data fragmentation, unstructured documentation and labour-intensive manual validation. We emphasize critical risks and limitations of applying AI to big data analytics and discuss mitigation through robust governance, performance monitoring, fairness audits, cybersecurity measures and transparent human oversight. We end by offering key recommendations and future directions, highlighting that strategic, equitable and ethically sound implementation will be essential to realizing benefits and ensuring trust in an increasingly data-driven transfusion ecosystem.

Background and objectives: This study aimed to characterize a novel RHD variant and to predict its potential impact on RhD antigen expression by integrating serological testing, molecular genotyping and structural modelling.

Materials and methods: The full-length RHD gene was analysed using PacBio single-molecule real-time (SMRT) long-read sequencing. The effect of the identified variant on protein stability was evaluated using DynaMut2. Amino acid interactions, including hydrogen bonds and hydrophobic contacts, were analysed with LigPlot to illustrate two-dimensional (2D) interaction changes, while three-dimensional (3D) visualization and structural interpretation were performed using PyMOL.

Results: Serological testing and sequencing revealed that the proband was heterozygous, with the reported RHD*15 allele and a novel allele carrying the c.727T>G (p.Tyr243Asp) variant present in trans. Structural prediction suggested that the p.Tyr243Asp substitution weakened hydrophobic interactions and reduced protein stability.

Conclusion: The coexistence of the reported RHD*15 allele and a novel missense variant, c.727T>G (p.Tyr243Asp), on different alleles may jointly contribute to reduced RhD antigen expression by affecting protein stability and epitope configuration. Integrating molecular genotyping and structural modelling with conventional serology provides complementary insights into the characterization of novel RHD variants and may provide additional clues for refining the classification of weak D and partial D phenotypes.

Background and objectives: Pre-donation deferral can negatively affect donor retention and blood supply sustainability. Comprehensive analysis of national deferral rates and reasons can inform evidence-based adjustments to donor eligibility criteria and improve the specificity of pre-donation screening.

Materials and methods: Data on deferral rates and the top three deferral causes were collected from 47 blood establishments (BEs) (23 provincial-level, 24 prefectural-level) across China between 2016 and 2024. Deferral reasons were classified into 16 standardized categories. Chi-square and Spearman correlation analyses were performed to examine differences between groups and associations with potential influencing factors.

Results: A total of 42.6 million whole blood (WB) and 5.9 million apheresis platelet (AP) donation attempts were analysed. Overall deferral rates were 11.4% for WB and 14.7% for AP donations, with provincial-level BEs exhibiting higher rates than prefectural-level BEs, for both donation types. The most frequently reported deferral reasons were elevated alanine aminotransferase (ALT) levels, low haemoglobin (Hb), lipaemia and abnormal blood pressure. Deferral rates were positively associated with the proportion of first-time donors and, for WB donors, with the detection rate of hepatitis B surface antigen (HBsAg).

Conclusion: Pre-donation deferral remains relatively high in China, with elevated ALT, low Hb, lipaemia and abnormal blood pressure identified as the most frequent causes. These findings highlight the need for evidence-based revisions of eligibility criteria, targeted donor education and strategies to promote repeat donation. Standardizing pre-donation screening practices across BEs may reduce unnecessary deferrals, enhance donor retention and support a more efficient and sustainable blood supply.

Background and objectives: Daratumumab (DARA), an anti-CD38 monoclonal antibody used in the treatment of multiple myeloma, frequently causes false-positive panreactivity in antibody screening tests. The conventional approach to resolve this interference involves treating reagent red blood cells (RBCs) with 0.2 M dithiothreitol (DTT). However, this method is labour-intensive, may fail to detect clinically significant antibodies and can yield inconsistent results. We aimed to assess the effectiveness of a commercial reagent containing anti-CD38 Fab fragments as an alternative for mitigating DARA-related interference.

Materials and methods: We retrospectively analysed 22 blood samples from 17 patients. Antibody screening was performed using both DTT-treated and DaraEx-treated reagent RBCs. Clinical and laboratory data were reviewed. Additionally, patient samples were spiked with antibodies to evaluate antibody detection.

Results: All samples showed false-positive panreactivity after DARA treatment. Nineteen samples were negative using both DTT- and DaraEx-treated cells. Three samples remained reactive with DTT-treated cells but turned negative with DaraEx. These cases were associated with lower serum albumin, impaired renal function and elevated monoclonal protein levels. DaraEx-treated cells retained reactivity with anti-E and anti-D antibodies.

Conclusion: DaraEx consistently and completely removed DARA-related interference, including in cases where DTT was ineffective. It represents a safe and efficient alternative to DTT for pre-transfusion antibody screening.

Background and objectives: Systematic evaluations of blood discard patterns remain limited. We analysed decade-long discard trends at a representative blood establishment to identify quality improvement opportunities.

Materials and methods: Discard data for blood products collected from 1 January 2015 to 31 December 2024 were obtained from the Beijing Red Cross Blood Center, classifying discards as test-related or non-test-related. Pareto charts identified major drivers of discard, and ${\chi }^2$ and Cochran-Armitage trend tests were used.

Results: The overall discard rate averaged 3.050%, peaking at 4.398% in 2021 before declining to 2.410% in 2024 (Z = -6.111, p < 0.0001). Non-test-related discards increased significantly from 0.720% in 2015 to 2.636% in 2021 (mean: 1.118%/year; Z = 26.478, p < 0.0001), before recovering to below pre-pandemic levels by 2024. Test-related discards decreased significantly from 2.254% in 2015 to 1.663% in 2020 and continued to decrease through 2024 (mean: 1.921%/year; Z = -27.454, p < 0.0001). Major discard drivers included lipaemic blood retest (LBR) in plasma, crossmatch incompatibility (CI) in red blood cell (RBC) products and alanine aminotransferase (ALT) retest failure. Plasma bag leakage (BL), platelet visual inspection abnormality (VIA), expired units and Treponema pallidum antibody (TP-Ab) and hepatitis C virus antibody (HCV-Ab) test-related discards, all showed significant downward trends (p < 0.001).

Conclusion: Divergent trends between test-related discards (declining from 2.254% to 1.663%) and non-test-related discards (increasing from 0.720% to 2.636%) indicate that although laboratory screening has improved substantially, greater attention is needed to optimize collection, processing and handling practices.