An assessment of the risk, cost-effectiveness, and perceived benefits of anti-parvovirus B19 tested blood products.

BACKGROUND: Parvovirus B19 (B19V) can cause severe anemia, hydrops foetalis, and even death in vulnerable patients. To prevent transfusion-transmitted B19V infection of at-risk patients, B19V antibody screening of blood donors was implemented. The cost-effectiveness of this intervention is unclear, as the likelihood of transmission through blood and subsequent complications for recipients are unknown. This study estimates the cost-effectiveness of anti-B19V donor screening in the Netherlands. STUDY DESIGN AND METHODS: The estimates needed for the cost-effectiveness model were: the occurrence of B19V in Dutch blood donors, the number of anti-B19V tested products required by hospitals, the likelihood of morbidity and mortality given B19V infection, treatment costs, and screening costs. These estimates were obtained from literature and observational data. When data were unavailable, structured expert judgment elicitation and statistical modeling were applied. RESULTS: The costs of preventing one transfusion transmitted B19V infection are estimated at €68,942 (€42,045 - €102,080). On average, 1.25 cases of morbidity and 0.12 cases of mortality are prevented annually. Although the perceived risk of transfusion transmitted B19V infection was low, half of the treating physicians favored anti-B19V screening. CONCLUSION: The estimated mortality and morbidity caused by B19V infection was low in the risk groups. The cost-effectiveness ratio is similar to other blood safety screening measures. No guidance exists to evaluate the acceptability of this ratio. The explicit overview of costs and effects may further guide the discussion of the desirability of B19V safe blood products.

Aiming for a representative sample: Simulating random versus purposive strategies for hospital selection.

BACKGROUND: A ubiquitous issue in research is that of selecting a representative sample from the study population. While random sampling strategies are the gold standard, in practice, random sampling of participants is not always feasible nor necessarily the optimal choice. In our case, a selection must be made of 12 hospitals (out of 89 Dutch hospitals in total). With this selection of 12 hospitals, it should be possible to estimate blood use in the remaining hospitals as well. In this paper, we evaluate both random and purposive strategies for the case of estimating blood use in Dutch hospitals. METHODS: Available population-wide data on hospital blood use and number of hospital beds are used to simulate five sampling strategies: (1) select only the largest hospitals, (2) select the largest and the smallest hospitals ('maximum variation'), (3) select hospitals randomly, (4) select hospitals from as many different geographic regions as possible, (5) select hospitals from only two regions. Simulations of each strategy result in different selections of hospitals, that are each used to estimate blood use in the remaining hospitals. The estimates are compared to the actual population values; the subsequent prediction errors are used to indicate the quality of the sampling strategy. RESULTS: The strategy leading to the lowest prediction error in the case study was maximum variation sampling, followed by random, regional variation and two-region sampling, with sampling the largest hospitals resulting in the worst performance. Maximum variation sampling led to a hospital level prediction error of 15%, whereas random sampling led to a prediction error of 19% (95% CI 17%-26%). While lowering the sample size reduced the differences between maximum variation and the random strategies, increasing sample size to n = 18 did not change the ranking of the strategies and led to only slightly better predictions. CONCLUSIONS: The optimal strategy for estimating blood use was maximum variation sampling. When proxy data are available, it is possible to evaluate random and purposive sampling strategies using simulations before the start of the study. The results enable researchers to make a more educated choice of an appropriate sampling strategy.

Why was this transfusion given? Identifying clinical indications for blood transfusion in health care data.

BACKGROUND: To enhance the utility of transfusion data for research, ideally every transfusion should be linked to a primary clinical indication. In electronic patient records, many diagnostic and procedural codes are registered, but unfortunately, it is usually not specified which one is the reason for transfusion. Therefore, a method is needed to determine the most likely indication for transfusion in an automated way. STUDY DESIGN AND METHODS: An algorithm to identify the most likely transfusion indication was developed and evaluated against a gold standard based on the review of medical records for 234 cases by 2 experts. In a second step, information on misclassification was used to fine-tune the initial algorithm. The adapted algorithm predicts, out of all data available, the most likely indication for transfusion using information on medical specialism, surgical procedures, and diagnosis and procedure dates relative to the transfusion date. RESULTS: The adapted algorithm was able to predict 74.4% of indications in the sample correctly (extrapolated to the full data set 75.5%). A kappa score, which corrects for the number of options to choose from, was found of 0.63. This indicates that the algorithm performs substantially better than chance level. CONCLUSION: It is possible to use an automated algorithm to predict the indication for transfusion in terms of procedures and/or diagnoses. Before implementation of the algorithm in other data sets, the obtained results should be externally validated in an independent hospital data set.

Protocol for a national blood transfusion data warehouse from donor to recipient.

INTRODUCTION: Blood transfusion has health-related, economical and safety implications. In order to optimise the transfusion chain, comprehensive research data are needed. The Dutch Transfusion Data warehouse (DTD) project aims to establish a data warehouse where data from donors and transfusion recipients are linked. This paper describes the design of the data warehouse, challenges and illustrative applications. STUDY DESIGN AND METHODS: Quantitative data on blood donors (eg, age, blood group, antibodies) and products (type of product, processing, storage time) are obtained from the national blood bank. These are linked to data on the transfusion recipients (eg, transfusions administered, patient diagnosis, surgical procedures, laboratory parameters), which are extracted from hospital electronic health records. APPLICATIONS: Expected scientific contributions are illustrated for 4 applications: determine risk factors, predict blood use, benchmark blood use and optimise process efficiency. For each application, examples of research questions are given and analyses planned. CONCLUSIONS: The DTD project aims to build a national, continuously updated transfusion data warehouse. These data have a wide range of applications, on the donor/production side, recipient studies on blood usage and benchmarking and donor-recipient studies, which ultimately can contribute to the efficiency and safety of blood transfusion.