Good Health & Wellbeing
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The International Journal of Transfusion Medicine
Risk-based blood matching cut expected immune reactions by about 75%
Extensive red blood cell matching considering patient alloimmunization risk
Publication Authors: Merel L. Wemelsfelder, Ronald H. G. van de Weem, Jessie S. Luken, Masja de Haas, René W. L. M. Niessen, C. Ellen van der Schoot, Han Hoogeveen, Folarin B. Oyebolu, Dick den Hertog, Mart P. Janssen
People who receive repeated blood transfusions can develop antibodies against proteins on donated red blood cells, making future transfusions more difficult. A collaboration across Sanquin (The Dutch Blood Bank), hospitals and universities developed a model that prioritises the closest blood matches for patient groups at higher risk while managing a limited supply of compatible units.
Summary
In the simulation, risk-based matching reduced expected harmful immune reactions by about 75% in higher-risk patient groups.
When hospitals shared scheduled transfusion needs with the central blood supply in advance, overall mismatches fell by about 30%.
The same advance information reduced simulated blood shortages by about 92%, showing how allocation and communication can affect availability as well as matching quality.
Why can repeated blood transfusions become harder over time?
Blood matching involves more than the familiar ABO and Rh(D) blood groups. Red blood cells carry many other proteins, called antigens. If a patient receives blood with an antigen their immune system recognises as foreign, they can develop antibodies against it.
This process is called alloimmunisation. It is especially relevant for people who receive repeated transfusions, including some people with sickle cell disease or thalassaemia, because new antibodies can make compatible blood harder to find and can increase the risk of transfusion reactions.
Why is this a prescriptive analytics problem?
Blood banks have a limited inventory and cannot provide the closest possible antigen match to every patient at the same time. The decision is how to allocate available units so that a mismatch is avoided where its expected clinical consequence is greatest, without creating avoidable shortages elsewhere.
The model therefore treats matching as an allocation problem. It combines information about available blood units, patient needs and the relative risk associated with different antigen mismatches.
How did the team change the matching model?
The research involved Sanquin (The Dutch Blood Bank), Amsterdam Business School, Leiden University Medical Centre, OLVG Laboratory, Utrecht University and the University of Cambridge.
The team extended the existing MINRAR model, which assigns blood units to patients while minimising mismatches on clinically important antigens. With input from transfusion-medicine experts, the revised model gave different priorities to patient groups based on the expected consequences of a mismatch.
The researchers tested the approach over a simulated year of daily transfusions based on real data from a Dutch university hospital and a regional hospital. They also tested what happens when scheduled transfusion needs are communicated to the central blood supply in advance.
What did the findings show?
Prioritising closer matches for higher-risk groups reduced expected harmful immune reactions by about 75% in the simulation, with a smaller increase in mismatches among groups for whom those mismatches were expected to have less serious consequences.
Advance information about scheduled transfusions produced another operational gain: overall mismatches fell by about 30%, and simulated shortages fell by about 92%.
These are modelled outcomes rather than observed clinical events. For people who depend on repeated transfusions, however, the results point to a way of using the existing blood inventory more deliberately to reduce exposure to mismatches while keeping compatible units available.
How can the findings inform other applications?
The project showed that allocation rules and advance demand information can change how effectively a scarce medical inventory is used. Similar principles can apply to other health services where products differ in compatibility, and some patient groups face greater consequences from a poor match.
Further research can test the approach prospectively in blood-bank operations, examine different patient populations and incorporate additional constraints such as rare blood types and regional inventory sharing.
