Background Transfusion carries a risk of transfusion reaction that is often underdiagnosed due to reliance on passive reporting. The study investigated the utility of digital methods to identify potential transfusion reactions, thus allowing real‐time intervention for affected patients. Method The hemovigilance unit monitored 3856 patients receiving 43,515 transfusions under the hemovigilance program. Retrospective comparison data included 298,498 transfusions. Transfusion medicine physicians designed and validated algorithms in the electronic health record that analyze discrete data, such as vital sign changes, to assign a risk score during each transfusion. Dedicated hemovigilance nurses remotely monitor all patients and perform real‐time chart reviews prioritized by risk score. When a reaction is suspected, a hemovigilance trained licensed clinician responds to manage the patient and ensure data collection. Board‐certified transfusion medicine physicians reviewed data and classified transfusion reactions under various categories according to the Centers for Disease Control hemovigilance definitions. Results Transfusion medicine physicians diagnosed 564 transfusion reactions (1.3% of transfusions)—a 524% increase compared to the previous passive reporting. The rapid response provider reached the bedside on average at 12.4 min demonstrating logistic feasibility. While febrile reactions were most diagnosed, recognition of transfusion‐associated circulatory overload demonstrated the greatest relative increase. Auditing and education programs further enhanced transfusion reaction awareness. Discussion The model of digitally‐enabled expert real‐time review of clinical data that prompts rapid response improved recognition of transfusion reactions. This approach could be applied to other patient deterioration events such as early identification of sepsis.

Background: Active surveillance for transfusion reactions is critically important among pediatric patients undergoing chemotherapy. Among pediatric-adolescent-young-adult (AYA) hematology/oncology patients, who have been typically excluded from transfusion reaction studies, this profile remains poorly characterized. Methods: We assessed the incidence and clinical characteristics of transfusion reactions (n = 3246 transfusions) in this population (n = 201 patients) at our center. Findings: The incidence of adjudicated transfusion reactions was 2¢04%. The incidence was higher for platelet (2¢78%) compared to packed red blood cell transfusions (1¢49%) (p = 0¢0149). The majority (61¢4%) of all reactions were classified as febrile non-haemolytic transfusion, while 35¢7% were considered allergic, and 2¢9% were classified as transfusion-associated circulatory overload. The incidence of transfusion reactions in patients who were pre-medicated was higher (2¢51%) than in patients who were not (1¢52%) (p = 0¢0406). Subset analysis revealed a 3¢95% incidence of adjudicated transfusion reactions among recipients of immune effector cells (IECs) (n = 3), all of which occurred during the potential window for cytokine release syndrome; two-thirds of these reactions were severe/potentially life-threatening. Interpretation: The incidence of transfusion reactions among pediatric-AYA hematology/oncology patients may be lower than the general pediatric population. Patients with a prior history of transfusion reactions and those receiving platelet transfusions may be at higher risk for reaction. From our limited sample, IEC recipients may be at risk for severe transfusion reactions. Large multi-center prospective studies are needed to characterize transfusion reactions in this population. Appropriate characterization of reactions in this population may inform risk stratification and mitigate missed opportunities for prompt recognition and appropriate management.

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332 Background: Patient safety concerns that arose during COVID-19, related to blood shortages at a large oncological transfusion center, foregrounded the need for predictive modeling tools to optimize blood product inventory control. A maximum surgical blood ordering schedule (MSBOS), is a tool used to assist clinicians in predicting intraoperative blood usage based on retrospective historical data within an institution. Although MSBOS proves to be valuable, it is rudimentary in nature. Not only is data collection cumbersome but the data generated may not reflect current surgical practices and inter-patient variability may skew procedural averages. Predictive blood modeling is contingent generation of a digital health dashboard (DHB). DHB are electronically embedded in the electronic health record (EHR) to collect perioperative data. Coupling the generated informatics (patient demographics, diagnosis, laboratory results, procedural type, medications/supplements, surgeon) with machine learning allows for creation of patient-centered predictive blood modeling algorithms and better inventory control. Methods: To characterize blood use across various procedures at our institution, we engaged information technology specialists to create a Web Intelligence report by integrating data from both an EHR and a lab information system (LIS) into a single repository. Information obtained illuminated a master procedure list, blood product usage patterns, and characterized patient demographics during January - March, 2020. Data is continuously extracted to create a perpetually updated MSBOS while secondarily functioning to cultivate data for future predictive machine learning algorithms. Results: Data analysis demonstrated 5598 procedures were performed during the first quarter of 2020. Procedures not transfused with packed red blood cells (pRBCs) totaled to 4,156 and 1,442 had a greater than or equal to 10% probability of requiring pRBCs. Our current practices reflected our overall crossmatch to transfusion ratio ( C:T) was 5.4 to 1. Concerted collaboration, resulting in preparation of pre-surgical blood product orders according laboratory generated MSBOS schedule could decrease the C:T to 1.7 to 1. Additionally, high intraprocedural pRBCs variability was identified in current procedural subtypes. Conclusions: Traditionally generated MSBOS are functionally limited and may not be reflective of current surgical practices. Additionally, inter-patient variability may distort some procedural type guidance. Creating an integrated data report, eliminates some of the inherent limitations of traditional MSBOS. Moving forward, the cultivated data if coupled with machine learning has the potential to create transferable proprietary algorithms that proactively predict individual patient transfusion needs.