Therapeutic target of high fresh frozen plasma to red blood cell ratio in severe blunt trauma

Critical Care Fujiwara et al. Critical Care (2025) 29:436 https://doi.org/10.1186/s13054-025-05678-z Open Access RESEARCH Therapeutic target of high fresh frozen plasma to red blood cell ratio in severe blunt trauma Gaku Fujiwara1,2*, Kosuke Inoue3,4, Wataru Ishii5, Tadashi Echigo6, Shoji Yokobori7, Naoto Shiomi8, Naoya Hashimoto1, Shigeru Ohtsuru9 and Yohei Okada10,11 Abstract Background To assess heterogeneous treatment effects of high fresh frozen plasma (FFP) to red blood cell (RBC) transfusion ratios in patients with severe blunt trauma and to identify subgroups that derive the greatest survival benefit. Methods This multicenter retrospective cohort study used data from the Japan Trauma Data Bank (2019–2023). Adults with severe blunt trauma (Injury Severity Score ≥ 16) who received transfusions were included. Patients were categorized into high-FFP (FFP:RBC > 1) and low-FFP (FFP:RBC ≤ 1) groups. A causal forest machine learning model was applied to a derivation cohort (2019–2021) to estimate conditional average treatment effects (CATEs) and identify subgroups with the highest predicted benefit. Findings were validated in a separate cohort (2022–2023). Results Among 6,679 patients, in-hospital mortality was 23.3% in the derivation and 23.2% in the validation cohort. Causal forest analysis revealed lactate level and Glasgow Coma Scale (GCS) score as key effect modifiers. A therapeutic target subgroup—defined as lactate ≥ 4.5 mmol/L and GCS ≤ 12—comprised 20.7% of the validation cohort. This subgroup showed a substantially greater mortality reduction with high-FFP transfusion (risk difference –13.3%, 95% CI –22.4 to –4.2%; number needed to treat [NNT] 7.5), compared with the overall cohort (risk difference –3.3%, 95% CI –6.7 to 0.5%; NNT 32.1). Results were consistent across sensitivity analyses. Conclusions High FFP-to-RBC transfusion ratios may confer the greatest benefit in patients with impaired consciousness and metabolic acidosis. Identifying high-benefit subgroups using machine learning could support more individualized transfusion strategies in trauma care. Keywords Transfusion, Blood product ratio, Fresh frozen plasma, Multiple trauma, Blunt trauma, Massive transfusion, Coagulopathy, Causal forest, Machine learning, Heterogeneous treatment effect *Correspondence: Gaku Fujiwara 1 Department of Neurosurgery, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto, Japan 2 Department of Neurosurgery, Japanese Red Cross Society Kyoto Daini Hospital, Kyoto, Japan 3 Department of Social Epidemiology, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan 4 Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan 5 Department of Emergency Medicine and Critical Care, Japanese Red Cross Society Kyoto Daini Hospital, Kyoto, Japan 6 National Health Insurance Yamato Clinic, Kagoshima, Japan Department of Emergency and Critical Care Medicine, Nippon Medical School, Tokyo, Japan 8 Department of Critical and Intensive Care Medicine, Shiga University of Medical Science, Shiga, Japan 9 Department of Primary Care and Emergency Medicine, Kyoto University, Kyoto, Japan 10 Department of Preventive Services, School of Public Health, Kyoto University, Kyoto, Japan 11 Pre-hospital and Emergency Research Centre, Health Services Research and Population Health, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore 7 © The Author(s) 2025. 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Critical Care (2025) 29:436 Introduction Trauma remains one of the leading global health challenges, accounting for a significant burden of mortality and long-term disability. Trauma-induced coagulopathy represents a key complication of trauma, with transfusion strategies central to its management [1]. To effectively manage trauma-induced coagulopathy, it is necessary to follow a damage control resuscitation protocol, with an emphasis on stabilizing hemodynamic status and administering fresh frozen plasma (FFP) earlier than red blood cells (RBC) [2, 3]. Conventionally, an FFP-to-RBC ratio of 1 has been recommended [4], but recent studies have indicated that higher ratios may be effective for certain patient groups [5–8]. In previous research, we demonstrated that a high ratio of FFP to RBC transfusion (specifically, FFP-toRBC ratio > 1) was associated with favorable outcomes in patients with severe blunt trauma [5]. However, traumainduced coagulopathy in blunt trauma exhibits considerable heterogeneity, influenced by factors such as the degree of tissue hypoperfusion and the severity of injury [1]. In particular, traumatic brain injury is often accompanied by marked hyperfibrinolysis, further contributing to this heterogeneity [9, 10]. This complexity poses challenges in determining the optimal transfusion strategy across diverse patient presentations. These observations indicate the importance of developing more targeted approaches to identifying which patients are most likely to benefit from plasma-rich transfusion. Recent advances in machine learning have led to novel approaches to evaluating heterogeneous treatment effects [11]. Algorithms such as causal forest can quantitatively elucidate the varying effects of treatments and have shown promise in clinical contexts characterized by high heterogeneity, such as trauma [12]. By analyzing large datasets, causal forest can identify distinct “high-benefit” subgroups among blunt trauma patients for whom high FFP transfusion may be more or less beneficial. This approach allows for a more nuanced understanding of treatment effects and the potential to tailor interventions to specific patient characteristics [13]. Given the complexity of coagulopathy in blunt trauma, we hypothesized that distinct subgroups exist in which high-FFP transfusion has varying effectiveness. This study aimed to evaluate heterogenous treatment effects of high-FFP transfusion in blunt trauma. We expected that identifying more effective treatment targets could meaningfully benefit clinical practice and contribute to improved patient outcomes. Methods Study design and setting This stu (...truncated)