Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques.

Use of data-driven methodologies in enhancing blood transfusion practices is rising, leveraging big data, machine learning, and optimization techniques to improve demand forecasting and supply chain management. This review used a narrative approach to identify, evaluate, and synthesize key studies that considered novel computational techniques for blood demand forecasting and inventory management through a search of PubMed and Web of Sciences databases for studies published from January 01, 2016, to March 30, 2023. The studies were analyzed for their utilization of various techniques, and their strengths, limitations, and areas for improvement. Seven key studies were identified. The studies focused on different blood components using various computational methods, such as regression, machine learning, hybrid models, and time series models, across different locations and time periods. Key variables used for demand forecasting were largely derived from electronic health record data, including clinical related predictors such as laboratory test results and hospital census by location. Each study offered unique strengths and valuable insights into the use of data-driven methods in blood bank management. Common limitations were unknown generalizability to other healthcare settings or blood components, need for field-specific performance measures, lack of ABO compatibility consideration, and ethical challenges in resource allocation. While data-driven research in blood demand forecasting and management has progressed, limitations persist and further exploration is needed. Understanding these innovative, interdisciplinary methods and their complexities can help refine inventory strategies and address healthcare challenges more effectively, leading to more robust, accurate models to enhance blood management across diverse healthcare scenarios.

Validation and cross-reactivity pattern assessment of monoclonal antibodies used for the screening of donor-specific IgG antibody subclasses in transplant recipients.

The screening for IgG subclass donor-specific antibodies (DSAs) in allograft recipients uses IgG1-4 subclass-specific monoclonal antibodies (mAbs) that should be mono-specific. The cross-reactivity discrepancies reported for IgG subclass-specific mAbs warranted a critical cross-reactivity pattern analysis of the IgG subclass-specific mAbs most commonly used to detect DSAs. We tested the reactivity of 2 anti-IgG1-, 3 anti-IgG2-, 1 anti-IgG3-, and 2 anti-IgG4-specific PE-conjugated mAbs against microbeads coated with IgG1-4 proteins separately. Each IgG subclass protein was coated at three densities on the beads (0.5, 1, and 2 µg of protein per 106 beads), and the PE-conjugated mAbs were titrated from 0.04 µg/mL to 5 µg/mL. The IgG subclass reactivity of the sample was acquired on the Luminex multiplex platform. Among the IgG subclass-specific mAbs, only the anti-IgG3 (clone: HP6050) mAb was mono-specific. All other mAbs tested were binding to IgG subclass proteins other than their respective immunogen, thereby being cross-reactive. IgG subclass cross-reactivity patterns were dependent on the concentration of both IgG subclass-specific mAbs and IgG1-4 protein targets coated onto the beads. With the current IgG subclass mAbs available, 3 of the 15 possible combinations of IgG1-4 subclass protein could be identified. While the remaining 12 unique combinations cannot be distinguished clearly, 6 groups that corresponded to two different unique combinations of IgG1-4 subclass protein could be identified. The dilution of serum samples and IgG subclass-specific mAbs, other than the anti-IgG3 (clone: HP6050), must be further optimized before their implementation in IgG subclass DSA screening in allograft recipients.

Big data modeling to predict platelet usage and minimize wastage in a tertiary care system.

Maintaining a robust blood product supply is an essential requirement to guarantee optimal patient care in modern health care systems. However, daily blood product use is difficult to anticipate. Platelet products are the most variable in daily usage, have short shelf lives, and are also the most expensive to produce, test, and store. Due to the combination of absolute need, uncertain daily demand, and short shelf life, platelet products are frequently wasted due to expiration. Our aim is to build and validate a statistical model to forecast future platelet demand and thereby reduce wastage. We have investigated platelet usage patterns at our institution, and specifically interrogated the relationship between platelet usage and aggregated hospital-wide patient data over a recent consecutive 29-mo period. Using a convex statistical formulation, we have found that platelet usage is highly dependent on weekday/weekend pattern, number of patients with various abnormal complete blood count measurements, and location-specific hospital census data. We incorporated these relationships in a mathematical model to guide collection and ordering strategy. This model minimizes waste due to expiration while avoiding shortages; the number of remaining platelet units at the end of any day stays above 10 in our model during the same period. Compared with historical expiration rates during the same period, our model reduces the expiration rate from 10.5 to 3.2%. Extrapolating our results to the ∼2 million units of platelets transfused annually within the United States, if implemented successfully, our model can potentially save ∼80 million dollars in health care costs.