Yazer, Mark

Importance Patients undergoing cardiac surgery with cardiopulmonary bypass often require platelet transfusions for bleeding. Platelets are routinely stored at room temperature (20-24 °C) for up to 5 to 7 days; however, cold storage (1-6 °C) may allow for increased storage duration without loss of hemostatic function. Objective To determine the maximum cold-storage duration, up to 21 days, for which cold-stored platelets (CSPs) are noninferior or superior in hemostatic efficacy to room-temperature platelets (RTPs) when transfused in actively bleeding patients undergoing cardiac surgery with cardiopulmonary bypass. Design, Setting, and Participants A phase 3, multicenter, randomized, partially blinded, adaptive, noninferiority, storage duration–ranging trial conducted in pediatric and adult patients undergoing cardiac surgery with planned cardiopulmonary bypass at 27 sites in the US and Australia from December 2021 to March 2025. Statistical analysis was conducted from September 9 to October 3, 2025. Intervention Patients were randomized 2:1 to receive CSPs with a maximum of 21 days of storage vs RTPs with a maximum of 7 days of storage. Main Outcomes and Measures The primary outcome was a hemostatic efficacy score with values ranging from 1 to 5 and higher values indicating greater bleeding. The noninferiority margin was 1 point. The success criterion was met if at least 1 cold-storage duration of 7 days or more had a posterior probability of noninferiority of at least 97.5%. The secondary outcome was 24-hour chest tube output. Results Of the 1000 patients who underwent platelet transfusion, 989 were included in the primary analysis (650 CSPs and 339 RTPs). The cohort had a mean (SD) age of 42.1 (30.1) years and 67.8% were male. CSPs were noninferior to RTPs for the primary outcome, with a probability of greater than 99.9% for all cold-storage durations. Pooled across storage durations, CSPs had a mean difference from RTPs of 0.09 (95% credible interval, −0.06 to 0.23). Median (IQR) chest tube output at 24 hours (8.9 mL/kg [5.2-15.4] for CSPs vs 8.4 mL/kg [5.5-15.9] for RTPs; difference in medians, 0.4 [95% CI, −1.0 to 1.5]) was not statistically different between study groups. In a post hoc analysis, as all storage durations were noninferior, considering the CSPs as a single group, the hemostatic efficacy score was similar between both groups: mean (SD) of 3.08 (1.15) CSPs vs 2.99 (1.10) RTPs. There were no differences in venous or arterial thrombotic events, transfusion-associated adverse events, acute respiratory distress syndrome, kidney failure, septic shock, and mortality between the study groups, except for increased reexploration rates in the CSP group. Conclusions and Relevance For patients undergoing cardiac surgery, CSPs stored up to 21 days are noninferior to RTPs for the control of active surgical bleeding. Use of CSPs may allow for increased availability of platelets by reducing wastage and shortages and may allow for incorporation of platelets into inventory at locations where platelets cannot be maintained due to the 5- to 7-day shelf life of RTPs. Trial Registration ClinicalTrials.gov Identifier: NCT04834414

Abstract Hemorrhagic shock is a common emergency that accounts for more than 10% of global mortality and up to 40% of trauma-related mortality. The Advanced Trauma Life Support (ATLS) guidelines outline resuscitation strategies in patients with massive hemorrhage based on clinical trial and observational data. Early intervention with fluids and/or blood products is recommended during resuscitation as key to maintaining vascular patency, stabilizing a patient’s blood pressure, maintaining tissue oxygenation, and limiting shock. From a transfusion perspective, there is wide heterogeneity in the quality of blood products due to donor and manufacturing variation. The optimal transfusion strategy for massive hemorrhage remains unclear. In silico models of resuscitation may offer a means to evaluate the effectiveness and safety of various resuscitation protocols. Here we describe a stochastic multicompartment model of fluid balance and resuscitation that includes (i) cardiovascular hemodynamics, (ii) body fluid compartments and capillary solute exchange, (iii) the ability to alter hemorrhage, resuscitation, and hemostasis parameters, and (iv) tissue oxygenation and capillary-alveolar gas exchange. Building upon deterministic frameworks, this stochastic model more faithfully reflects the clinical heterogeneity of bleeding patients at a level I trauma center. This allows for a proof of concept in silico trial comparing crystalloids, conventional component therapy (CCT), i.e. red cell, platelet and plasma components, to cold-stored low titer group O whole blood (LTOWB). In an in silico cohort of ATLS class III (>30% and ≤40% blood volume lost) hemorrhage, LTOWB resuscitation reduced the time spent in the critical hemostatic window (platelet count <50x109/L, INR≥2, hemoglobin (Hgb) <8 g/dL and fibrinogen <150 mg/dL) compared with CCT (90.59 vs. 147.62 minutes; p=0.04), with no difference in predicted event-free survival (t = 240 minutes; cardiac < 1.5, fluid overload > 10% or SBP > 150% of the starting SBP, or Hgb < 3.0 g/dL). In a separate cohort of ATLS class IV (>40% blood volume lost) hemorrhage, LTOWB yielded a higher predicted event-free survival (74%) versus the CCT arm (69%, p < 0.01). We demonstrate how this in silico platform can function as a digital twin for hemorrhagic trauma enabling precision transfusion strategies, and in parallel, an operational twin to support blood banks to forecast blood product demand and inform massive transfusion protocols and clinical trial design.

Abstract Artificial intelligence (AI) and machine learning (ML) are beginning to enhance key workflows across blood banking (BB) and transfusion medicine (TM), including donor engagement, inventory forecasting, immunohaematology interpretation, haemovigilance and clinical decision support. These tools can extract signals from large, diverse datasets to improve efficiency, consistency and timeliness of care while enabling more proactive patient blood management. At the same time, TM is uniquely sensitive to safety and trust, so the field must pair innovation with disciplined validation and governance. This review surveys emerging applications and distils a balanced ethics and oversight framework tailored to TM and BB practice. Core elements include outcome‐focused validation and continuous monitoring; targeted fairness testing where biological or scientific plausibility suggests subgroup differences; layered transparency that distinguishes transparency, interpretability and explainability; and privacy by design with proportionate consent. We emphasize human oversight at every step: clinicians and laboratorians evaluate, monitor and troubleshoot models, perform tasks AI cannot, integrate multiple inputs to make decisions and interact with AI through guardrails such as intended‐use statements, confidence thresholds and clear override pathways. Accountability is shared across users, service directors, institutions and regulators. When implemented with these safeguards, AI can reduce preventable errors, improve resource allocation and support more equitable access to safe transfusion. The goal is principled pragmatism that protects patients and donors while enabling timely adoption of well‐validated tools.