|Name||Loan van de Hoeven|
|Date defended||June 29, 2017|
|(Co-) Supervisors||Prof. Dr C.B. Roes, Dr ir. M.P. Janssen, Dr ir. H. Koffijberg|
|Title of thesis||Transfusion data: from collection to reflection|
Read the thesis online
Blood transfusion is an important medical treatment for many and diverse patients groups, saving lives but sometimes also causing adverse transfusion reactions in transfusion recipients. For this reason blood use should ideally be as low as possible. The fact that significant differences exist in the amount of blood used between countries, hospitals and even within hospitals, indicates that there is room for improvement. Moreover, there are likely to exist unrecognized risk factors in donors and blood products that might affect patient outcomes. In order to study these various aspects and the interplay between them, data on the complete transfusion chain are needed. Therefore the Dutch Transfusion Data warehouse (DTD) was set up, in which data from the national blood bank and a (growing) number of Dutch hospitals are linked. These data have a broad range of applications: identifying risk factors, predicting future blood use, benchmarking blood use, and optimizing process efficiency. A structured stepwise approach is applied to validate data quality, addressing external validity (e.g. concordance with external reports, previous studies and expert feedback) and internal validity (e.g. completeness, uniformity and plausibility). In addition, an algorithm is developed to identify –out of all diagnostic and procedural data available– the most likely indication (i.e. reason) for transfusion. The algorithm was evaluated against a gold standard based on expert review and adjusted accordingly. The final algorithm was able to predict the majority of cases correctly (about 75%). However, before implementation of the algorithm it should be optimized and externally validated in independent hospital datasets. New hospitals are included in the DTD continuously. Different strategies for selecting hospitals for inclusion in the DTD are simulated to compare their effect on representativeness for the Netherlands. The ‘maximum varation’ strategy, which is to include hospitals that differ from each other maximally (the smallest and largest hospitals), resulted in highest representativeness. Finally, analyses of donor and patient data show trends in blood use and in the donor population composition. Over the past 20 years, the use of red blood cell units (RBCs) decreased. Retrospective analysis of various patient revealed that RBC use changed from largely surgical to predominantly medical blood use, suggesting a more restrictive transfusion policy for surgical patients as well as an increase in medical indications for transfusion. A special group of donors provide antibdies that are required for ReshusD (RhD)-negative women pregnant with a RhD-positive child in order to prevent hemolytic disease of the newborn. Due to the success of the RhD prevention program, the number of naturally immunized women has decreased, thereby also reducing the number of potential donors. Various donor recruitment scenarios were compared by simulating donor population size and age using data on Dutch anti-RhD donors in 1994-2013. This relatively simple simulation model could sufficiently accurately describe and predict the size of the anti-RhD donor population and the impact of ageing. Recommendations for research topics and possible extensions of the DTD offer a perspective on future applications of the data following the process from collection to reflection.