Transfusion Medicine Reviews最新文献

英文中文

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2026-01-01 Epub Date: 2025-12-08 DOI: 10.1016/j.tmrv.2025.150950

Fleur Krommendijk , Amber Meulenbeld , Jan Karregat , Bryan R. Spencer , Fredrik Toss , Noriko Namba , Nelson Tsuno , Klara Greffin , Konstanze Aurich , Eva-Maria Merz , Mindy Goldman , Eamonn Ferguson , Trynke Hoekstra , Mikko Arvas , Katja van den Hurk , BEST Collaborative

Frequent blood donation can deplete iron stores, increasing the risk of iron deficiency anemia. Postdonation iron supplements may help preserve donor health, but willingness to take supplements likely depends on donor knowledge and confidence. We conducted a cross-sectional survey among 5691 whole blood donors from 6 countries (Netherlands, USA, Japan, Finland, Sweden, Germany). Knowledge was assessed using 16 true-or-false statements on 4 blood donation-related topics; confidence by asking if donors were "certain" or "guessing." Willingness to take supplements was assessed using 3 scenarios: to continue donating, when advised by a donor physician, and general iron supplementation rejection. Using logistic regressions, we assessed associations between knowledge, confidence, and willingness to take iron supplements for each scenario, adjusted for sex, age, country, prior supplement use, and trust in the blood service. Most donors exhibited medium to high knowledge and under confidence in that knowledge. Willingness to take supplements was high (80.6%-84.2% across scenarios). Knowledge and confidence were not consistently associated with willingness to take supplements. In contrast, trust in the blood service (odds ratio [OR] = 1.64, P < .001) and prior supplement use (OR = 1.87, P < .001) were strongly associated with willingness when supplements were required to continue donating, with similar effects across other scenarios. Willingness varied across countries, with higher willingness in Nordic countries. These findings suggest that trust-building approaches may be more promising than education-focused strategies, though causal relationships require further research. High acceptance rates suggest that postdonation iron supplementation may be a feasible strategy for donor iron management.

频繁献血会消耗铁储备，增加缺铁性贫血的风险。捐献后补充铁元素可能有助于保持捐赠者的健康，但是否愿意补充铁元素可能取决于捐赠者的知识和信心。我们对来自6个国家（荷兰、美国、日本、芬兰、瑞典和德国）的5691名全血献血者进行了横断面调查。对4项献血相关主题的16项真假陈述进行知识评估；通过询问捐赠者是“确定”还是“猜测”来提高信心。服用铁补充剂的意愿通过三种情况进行评估：在供体医生的建议下继续捐献，以及普遍的铁补充排斥。使用逻辑回归，我们评估了知识、信心和意愿在每种情况下服用铁补充剂之间的关系，并根据性别、年龄、国家、既往使用补充剂和对血液服务的信任进行了调整。大多数捐助者表现出中等到较高的知识，但对这些知识缺乏信心。服用补充剂的意愿很高（80.6%-84.2%）。知识和信心并不总是与服用补充剂的意愿相关。相比之下，对血液服务的信任（优势比[OR] = 1.64, P < .001）和之前使用过补品（OR = 1.87, P < .001）与需要补品时继续献血的意愿密切相关，在其他情况下也有类似的效果。各国的意愿各不相同，北欧国家的意愿更高。这些发现表明，建立信任的方法可能比以教育为重点的策略更有希望，尽管因果关系需要进一步研究。高接受率提示捐献后补铁可能是一种可行的捐献铁管理策略。

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引用次数: 0

Artificial Intelligence Implementation in Transfusion Medicine: Addressing the Challenges of Clinical Adoption 人工智能在输血医学中的应用：应对临床应用的挑战。

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2026-01-01 Epub Date: 2026-01-29 DOI: 10.1016/j.tmrv.2026.150961

Suzanne Maynard , Joseph Farrington , Sheharyar Raza , Simon J. Stanworth

Artificial intelligence (AI) and machine learning (ML) are increasingly promoted to enhance transfusion and patient blood management, yet real-world implementation remains rare. We reviewed recent exemplar studies reporting prospective deployment with workflow integration to examine translational features, barriers, and enablers of AI/ML integration. On June 18, 2025, we searched PubMed and Web of Science for articles from January 2022 onward. Of 1243 records screened and 31 full texts reviewed, 3 studies met inclusion criteria. The exemplars comprised: (1) a laboratory-embedded tool predicting low ferritin in anemic adults, which during a 21-day deployment identified additional iron deficiency relevant to pretransfusion optimization; (2) a patient-facing smartphone application estimating hemoglobin from fingernail images, adopted nationally by >200,000 users with potential implications for anemia screening; and (3) a clinician-facing smartphone decision support tool predicting resuscitation needs in trauma, piloted across 5 centers with acceptable feasibility and user satisfaction in a transfusion-intensive setting. Common enablers included alignment with clinical need, use of existing data infrastructure, interpretable tree-based models, and early stakeholder engagement. Persistent barriers were data quality and governance, limited generalizability, and absence of economic evaluation. Importantly, no study demonstrated improvement in clinical outcomes or cost. For clinical adoption, AI tools must integrate into routine workflows with clear safety, monitoring, and regulatory plans. Future research should apply implementation frameworks from the outset, evaluate downstream impact on transfusion practice and outcomes, and prioritize scalable approaches such as laboratory-embedded analytics, interoperable decision support, and patient-centered digital tools.

人工智能（AI）和机器学习（ML）越来越多地被推广以加强输血和患者血液管理，但实际应用仍然很少。我们回顾了最近的范例研究，报告了工作流集成的预期部署，以检查AI/ML集成的翻译特性、障碍和推动因素。2025年6月18日，我们在PubMed和Web of Science上搜索了2022年1月以后的文章。在筛选的1243份记录和审查的31份全文中，有3项研究符合纳入标准。示例包括：(1)实验室嵌入式预测贫血成人低铁蛋白的工具，该工具在21天的部署期间确定了与输血前优化相关的额外铁缺乏；(2)一个面向患者的智能手机应用程序，从指甲图像中估计血红蛋白，在全国范围内被100万用户采用，对贫血筛查具有潜在意义；(3)面向临床医生的智能手机决策支持工具，预测创伤中复苏需求，在5个中心进行试点，在输血密集型环境中具有可接受的可行性和用户满意度。常见的促成因素包括符合临床需求、使用现有数据基础设施、可解释的基于树的模型以及早期涉众参与。持续存在的障碍是数据质量和治理、有限的通用性以及缺乏经济评估。重要的是，没有研究表明临床结果或成本有所改善。对于临床应用，人工智能工具必须整合到常规工作流程中，并制定明确的安全、监测和监管计划。未来的研究应该从一开始就应用实施框架，评估对输血实践和结果的下游影响，并优先考虑可扩展的方法，如实验室嵌入式分析、可互操作的决策支持和以患者为中心的数字工具。

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引用次数: 0

Journal Club 杂志俱乐部

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2026-01-01 Epub Date: 2026-01-05 DOI: 10.1016/j.tmrv.2025.150952

引用次数: 0

Recommended Papers of 2025 From the TMR Editorial Board TMR编委会2025年推荐论文

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2026-01-01 Epub Date: 2025-12-18 DOI: 10.1016/j.tmrv.2025.150957

Sunny Dzik , Zoe McQuilten , Jeannie Callum

引用次数: 0

Strengthening Translational Pathways: The Need for Readiness and Fairness Assessment in Transfusion AI Models 加强转化途径：输血人工智能模型中准备就绪和公平性评估的必要性。

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2026-01-01 Epub Date: 2025-12-15 DOI: 10.1016/j.tmrv.2025.150955

Riza Amalia , Ronal Surya Aditya , Muhammad Sandy Al Fath

引用次数: 0

Journal Club 杂志俱乐部

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2026-01-01 Epub Date: 2026-02-24 DOI: 10.1016/j.tmrv.2026.150964

引用次数: 0

The Science Behind Clinical Practice: What is a CAR-T Cell? 临床实践背后的科学：什么是CAR-T细胞？

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2026-01-01 Epub Date: 2025-10-29 DOI: 10.1016/j.tmrv.2025.150933

引用次数: 0

New Horizons in Transfusion Medicine 输血医学的新视野。

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2025-10-01 Epub Date: 2025-11-05 DOI: 10.1016/j.tmrv.2025.150934

Sunny Dzik , Jeannie Callum , Zoe McQuilten

引用次数: 0

Artificial Intelligence and Machine Learning in Transfusion Practice: An Analytical Assessment 输血实践中的人工智能和机器学习：分析性评估。

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2025-10-01 Epub Date: 2025-08-24 DOI: 10.1016/j.tmrv.2025.150926

Na Li , Ruchika Goel , Sheharyar Raza , Kiarash Riazi , Jie Pan , Huong Quynh Nguyen , Andrew W. Shih , Adam D’Souza , Rounak Dubey , Aaron A.R. Tobian , Donald M. Arnold

Transfusion medicine is vital to healthcare and affects clinical outcomes, patient safety, and system resilience while addressing challenges such as blood shortages, donor variability, and rising costs. The integration of artificial intelligence (AI) and machine learning (ML) presents new opportunities to improve clinical decision-making and operational effectiveness in this field. This structured narrative review identified and evaluated studies applying AI and ML in transfusion medicine. A search of PubMed and Scopus for articles published between January 2018 and April 2025 yielded 565 publications. Studies were included if they applied AI or ML techniques, focused on transfusion management or decision support, and were evaluated using electronic health records or expert review. Four exemplar studies were selected, each representing a distinct AI paradigm: supervised, unsupervised, reinforcement, and generative learning. These studies were critically appraised for methodological rigor, clinical relevance, and potential for implementation in practice. The reviewed studies reflected a clear shift from traditional analytic methods toward more advanced computational approaches to improve prediction accuracy, optimize resource allocation, and support clinical decision-making. Three overarching themes emerged: the need to balance model complexity with interpretability and clinical feasibility; the impact of data quality and preprocessing on model performance and fairness; and the barriers to broader applicability and cross-institutional deployment. As technological barriers continue to decline, future challenges will increasingly center on privacy regulations, infrastructure constraints, and aligning model complexity with practical utility. Thoughtful integration of these considerations through scalable, clinical-grade, and transparent solutions will be critical in realizing the full potential of AI and ML in transfusion medicine.

输血医学对医疗保健至关重要，影响临床结果、患者安全和系统弹性，同时应对血液短缺、献血者多样性和成本上升等挑战。人工智能（AI）和机器学习（ML）的融合为提高该领域的临床决策和操作效率提供了新的机会。这篇结构化的叙述性综述确定并评估了在输血医学中应用人工智能和ML的研究。在PubMed和Scopus上搜索2018年1月至2025年4月间发表的文章，得到565篇。如果研究应用了人工智能或机器学习技术，专注于输血管理或决策支持，并使用电子健康记录或专家审查进行评估，则纳入研究。选择了四个范例研究，每个研究都代表了一个不同的人工智能范式：监督学习、无监督学习、强化学习和生成学习。这些研究在方法的严谨性、临床相关性和在实践中实施的潜力方面得到了严格的评价。回顾的研究反映了从传统分析方法向更先进的计算方法的明显转变，以提高预测准确性，优化资源分配，并支持临床决策。出现了三个总体主题：需要平衡模型的复杂性与可解释性和临床可行性；数据质量和预处理对模型性能和公平性的影响；更广泛的适用性和跨机构部署的障碍。随着技术壁垒的不断下降，未来的挑战将越来越多地集中在隐私法规、基础设施约束以及将模型复杂性与实际效用相结合上。通过可扩展、临床级和透明的解决方案将这些考虑周到地整合在一起，对于实现人工智能和机器学习在输血医学中的全部潜力至关重要。

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引用次数: 0

Efficacy of Granulocyte Transfusions in Treating Neutropenic Infections: A Systematic Review and Meta-Analysis of Intervention Studies 粒细胞输注治疗中性粒细胞减少感染的疗效：干预研究的系统回顾和荟萃分析。

IF 2.5 2区医学 Q2 HEMATOLOGY

Transfusion Medicine Reviews

Pub Date : 2025-10-01 Epub Date: 2025-09-20 DOI: 10.1016/j.tmrv.2025.150929

Alejandra López-Arredondo , Saúl Karr de León , Anna-Maria Lampousi , Marion E.G. Brunck

The therapeutic value of granulocyte transfusions (GTX) remains debated. We conducted a systematic review and meta-analysis of intervention studies evaluating GTX efficacy in treating neutropenic infections. MEDLINE, EMBASE, and Cochrane Central were searched from inception to March 2025 to identify interventional studies evaluating the efficacy of GTX for neutropenic infections. Studies were qualitatively summarized. Summary risk ratios (RR) with 95% confidence intervals (CIs) were estimated for randomized controlled trials (RCTs), and non-randomized controlled trials (NRCTs) using random-effects models. Certainty of evidence was evaluated using GRADE. There were 110 studies meeting inclusion criteria: 16 RCTs, 14 NRCTs, and 80 uncontrolled trials. The most frequent underlying disease was leukemia, and the most frequently reported pathogen was Candida. In RCTs, GTX showed no significant all-cause mortality reduction over standard-of-care in pediatric/adult patients or neonates, both associations with low certainty of evidence. In contrast, prospective NRCTs including pediatric/adult patients showed that GTX led to lower all-cause mortality (RR 0.40; 95% CI: 0.23-0.68, I²: 64%), particularly among recipients of high-dose GTX (≥1 × 10¹⁰cells/transfusion), with very low-certainty evidence. Results support a dose-response relationship and highlight heterogeneity in patients, treatment settings, and infections. This work recommends carefully designed future RCTs, including strict patient stratification.

粒细胞输注（GTX）的治疗价值仍有争议。我们对评估GTX治疗中性粒细胞减少感染疗效的干预研究进行了系统回顾和荟萃分析。检索MEDLINE、EMBASE和Cochrane Central从成立到2025年3月，以确定评估GTX治疗中性粒细胞减少感染疗效的介入性研究。对研究进行定性总结。采用随机效应模型估计随机对照试验（rct）和非随机对照试验（NRCTs）的总风险比（RR）和95%置信区间（ci）。使用GRADE评价证据的确定性。有110项研究符合纳入标准：16项随机对照试验，14项非随机对照试验和80项非对照试验。最常见的基础疾病是白血病，最常见的病原体是念珠菌。在随机对照试验中，GTX显示儿科/成人患者或新生儿的全因死亡率与标准护理相比没有显著降低，这两种关联的证据确定性都较低。相比之下，包括儿童/成人患者在内的前瞻性nrct显示，GTX导致较低的全因死亡率（RR 0.40; 95% CI: 0.23-0.68, I2: 64%），特别是在高剂量GTX（≥1 × 1010个细胞/输血）的接受者中，证据的确定性非常低。结果支持剂量-反应关系，并强调患者、治疗环境和感染的异质性。这项工作建议仔细设计未来的随机对照试验，包括严格的患者分层。

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