Database research in transfusion medicine: The power of large numbers

How do we build a comprehensive Vein-to-Vein (V2V) database for conduct of observational studies in transfusion medicine? Demonstrated with the Recipient Epidemiology and Donor Evaluation Study-IV-Pediatric V2V database protocol

BACKGROUND

The Recipient Epidemiology and Donor Evaluation Study-IV-Pediatric (REDS-IV-P) is the fourth iteration of the National Heart, Lung, and Blood Institute's REDS program and includes a focus on pediatric populations. The REDS-IV-P Vein-to-Vein (V2V) database encompasses linked information from blood donors, blood components, and patients to facilitate studies in transfusion medicine.

STUDY DESIGN AND METHODS

The V2V database is an Observational Medical Outcomes Partnership Common Data Model database. The study period is April 1, 2019 through December 31, 2023. Data from all donors and donations at participating blood centers, all blood components derived from the donations, and all inpatient visits and selected outpatient visits at participating hospitals are included. The database captures all information within patient data domains not restricting data to a preselected subset of medical records.

RESULTS

The V2V database contains data from 7 blood centers and 22 hospitals. We project the database will have over 2 billion pieces of information from 1.3 million patients with 20.6 million healthcare encounters. The database will include data on approximately 1 million transfused units and 2.3 million donors with approximately 6.8 million donation visits.

CONCLUSION

The REDS-IV-P V2V database is a comprehensive database with data from millions of blood donors, blood components, and patients. A diverse set of data from the encounters are included in the database such that emerging questions can likely be addressed. The Observational Medical Outcomes Partnership Common Data Model is an efficient, flexible, and increasingly used common data model. The final de-identified database will be publicly available.

Transfusion practices in a large cohort of hospitalized children

BACKGROUND

While previous studies have described the use of blood components in subsets of children, such as the critically ill, little is known about transfusion practices in hospitalized children across all departments and diagnostic categories. We sought to describe the utilization of red blood cell, platelet, plasma, and cryoprecipitate transfusions across hospital settings and diagnostic categories in a large cohort of hospitalized children.

STUDY DESIGN AND METHODS

The public datasets from 11 US academic and community hospitals that participated in the National Heart Lung and Blood Institute Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) were accessed. All nonbirth inpatient encounters of children 0-18 years of age from 2013 to 2016 were included.

RESULTS

61,770 inpatient encounters from 41,943 unique patients were analyzed. Nine percent of encounters involved the transfusion of at least one blood component. RBC transfusions were most common (7.5%), followed by platelets (3.9%), plasma (2.5%), and cryoprecipitate (0.9%). Children undergoing cardiopulmonary bypass were most likely to be transfused. For the entire cohort, the median (interquartile range) pretransfusion laboratory values were as follows: hemoglobin, 7.9 g/dl (7.1-10.4 g/dl); platelet count, 27 × 10⁹ cells/L (14-54 × 10⁹ cells/L); and international normalized ratio was 1.6 (1.4-2.0). Recipient age differences were observed in the frequency of RBC irradiation (95% in infants, 67% in children, p < .001) and storage duration of RBC transfusions (median storage duration of 12 [8-17] days in infants and 20 [12-29] days in children, p < .001).

CONCLUSION

Based on a cohort of patients from 2013 to 2016, the transfusion of blood components is relatively common in the care of hospitalized children. The frequency of transfusion across all pediatric hospital settings, especially in children undergoing cardiopulmonary bypass, highlights the opportunities for the development of institutional transfusion guidelines and patient blood management initiatives.

Methodological considerations for linked blood donor-component-recipient analyses in transfusion medicine research

In recent years, there has been a concerted effort to improve our understanding of the quality and effectiveness of transfused blood components. The expanding use of large datasets built from electronic health records allows the investigation of potential benefits or adverse outcomes associated with transfusion therapy. Together with data collected on blood donors and components, these datasets permit an evaluation of associations between donor or blood component factors and transfusion recipient outcomes. Large linked donor-component recipient datasets provide the power to study exposures relevant to transfusion efficacy and safety, many of which would not otherwise be amenable to study for practical or sample size reasons. Analyses of these large blood banking-transfusion medicine datasets allow for characterization of the populations under study and provide an evidence base for future clinical studies. Knowledge generated from linked analyses have the potential to change the way donors are selected and how components are processed, stored and allocated. However, unrecognized confounding and biased statistical methods continue to be limitations in the study of transfusion exposures and patient outcomes. Results of observational studies of blood donor demographics, storage age, and transfusion practice have been conflicting. This review will summarize statistical and methodological challenges in the analysis of linked blood donor, component, and transfusion recipient outcomes.

Effect of donor, component, and recipient characteristics on hemoglobin increments following red blood cell transfusion

Significant research has focused individually on blood donors, product preparation and storage, and optimal transfusion practice. To better understand the interplay between these factors on measures of red blood cell (RBC) transfusion efficacy, we conducted a linked analysis of blood donor and component data with patients who received single-unit RBC transfusions between 2008 and 2016. Hemoglobin levels before and after RBC transfusions and at 24- and 48-hour intervals after transfusion were analyzed. Generalized estimating equation linear regression models were fit to examine hemoglobin increments after RBC transfusion adjusting for donor and recipient demographic characteristics, collection method, additive solution, gamma irradiation, and storage duration. We linked data on 23 194 transfusion recipients who received one or more single-unit RBC transfusions (n = 38 019 units) to donor demographic and component characteristics. Donor and recipient sex, Rh-D status, collection method, gamma irradiation, recipient age and body mass index, and pretransfusion hemoglobin levels were significant predictors of hemoglobin increments in univariate and multivariable analyses (P < .01). For hemoglobin increments 24 hours after transfusion, the coefficient of determination for the generalized estimating equation models was 0.25, with an estimated correlation between actual and predicted values of 0.5. Collectively, blood donor demographic characteristics, collection and processing methods, and recipient characteristics accounted for significant variation in hemoglobin increments related to RBC transfusion. Multivariable modeling allows the prediction of changes in hemoglobin using donor-, component-, and patient-level characteristics. Accounting for these factors will be critical for future analyses of donor and component factors, including genetic polymorphisms, on posttransfusion increments and other patient outcomes.

Critical developments of 2018: A review of the literature from selected topics in transfusion. A committee report from the AABB's Clinical Transfusion Medicine Committee

BACKGROUND

The AABB compiles an annual synopsis of the published literature covering important developments in the field of transfusion medicine. An abridged version of this work is being made available in TRANSFUSION, with the full-length report available as Appendix S1 (available as supporting information in the online version of this paper).

STUDY DESIGN AND METHODS

Papers published in late 2017 and 2018 are included, as well as earlier papers cited for background. Although this synopsis is comprehensive, it is not exhaustive, and some papers may have been excluded or missed.

RESULTS

The following topics are covered: "big data" and "omics" studies, emerging infections and testing, platelet transfusion and pathogen reduction, transfusion therapy and coagulation, transfusion approach to hemorrhagic shock and mass casualties, therapeutic apheresis, and chimeric antigen receptor T-cell therapy.

CONCLUSION

This synopsis may be a useful educational tool.

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.

Epidemiology of donors and recipients: lessons from the SCANDAT database

With the development of several 'vein-to-vein' databases, which capture data on the entire donor-recipient continuum and link this data to health outcomes, there has been an increasing number of studies investigating the health effects of all aspects of the practice of transfusion medicine. The Scandinavian Donations and Transfusions (SCANDAT) database is one of several such databases, which includes all electronically available data on blood donors, donations and transfusions since the late 1960s in Sweden and the early 1980s in Denmark. The SCANDAT database has been used to characterise disease occurrence among blood donors and transfused patients, as well as to investigate possible health effects of blood donations, aspects of transfusion care and possible transfusion transmission of disease. Recent publications include studies on recipient mortality associated with the storage lesion, studies on the effects of donor demographics on patient mortality and health effects of frequent blood donation. Although this research approach is clearly very powerful, the appropriate analysis of such real-world data is complex and requires close methodological attention. The purpose of this review is to present some of the research conducted within the SCANDAT collaboration. We hope more international collaboration may help improve our understanding of the important remaining questions about donor and recipient health.

Demographic and epidemiologic characterization of transfusion recipients from four US regions: evidence from the REDS-III recipient database

BACKGROUND

Blood transfusion is one of the most common medical procedures during hospitalization in the United States. To understand the benefits of transfusion while mitigating potential risks, a multicenter database containing detailed information on transfusion incidence and recipient outcomes would facilitate research.

STUDY DESIGN AND METHODS

The Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) program has developed a comprehensive transfusion recipient database utilizing data from hospital electronic health records at 12 participating hospitals in four geographic regions. Inpatient and outpatient data on transfusion recipients from January 1, 2013 to December 31, 2014 included patient age, sex, ethnicity, primary diagnosis, type of blood product provided, issue location, pretransfusion and post-transfusion hemoglobin (Hgb), and hospital outcomes. Transfusion incidence per encounter was calculated by blood product and various patient characteristics.

RESULTS

During the 2-year study period, 80,362 (12.5%) inpatient encounters involved transfusion. Among inpatients, the most commonly transfused blood products were red blood cells (RBCs; 10.9% of encounters), followed by platelets (3.2%) and plasma (2.9%). Among patients who received transfusions, the median number of RBC units was one, the pretransfusion Hgb level was 7.6 g/dL, and the Hgb increment per unit was 1.4 g/dL. Encounter mortality increased with patient age, the number of units transfused, and the use of platelet or plasma products. The most commonly reported transfusion reaction was febrile nonhemolytic.

CONCLUSION

The database contains comprehensive data regarding transfusion use and patient outcomes. The current report describes an evaluation of the first 2 years of a planned, 4-year, linked blood donor-component-recipient database, which represents a critical new resource for transfusion medicine researchers.

Exploring donor and product factors and their impact on red cell post-transfusion outcomes

The impact of donor characteristics, red cell age, and red cell processing methods on recipient outcomes is an emerging area of research. Knowledge generated from exploring this transfusion continuum has the potential to change the way donors are selected and how donations are processed and stored with important clinical and operational impact. Recently, donor characteristics including age, gender, donation frequency, genetics, and ethnicity have been shown to affect product quality and possibly recipient outcomes. The structural, biochemical and immunological changes that occur with red cell storage appear to not cause harm to blood recipients after 14 randomized clinical trials. However, both in vitro and clinical data are now beginning to question the safety of blood stored for a shorter duration. Whole blood filtration, a method of blood processing, has been linked to inferior recipient outcomes when compared to red cell filtration. Collectively, this emerging body of literature suggests that pre-transfusion parameters impact product quality and recipient outcomes and that no 2 units of red cells are quite the same. This review will summarize both the pre-clinical and clinical studies evaluating these associations.

Validation of multisource electronic health record data: an application to blood transfusion data

Background

Although data from electronic health records (EHR) are often used for research purposes, systematic validation of these data prior to their use is not standard practice. Existing validation frameworks discuss validity concepts without translating these into practical implementation steps or addressing the potential influence of linking multiple sources. Therefore we developed a practical approach for validating routinely collected data from multiple sources and to apply it to a blood transfusion data warehouse to evaluate the usability in practice.

Methods

The approach consists of identifying existing validation frameworks for EHR data or linked data, selecting validity concepts from these frameworks and establishing quantifiable validity outcomes for each concept. The approach distinguishes external validation concepts (e.g. concordance with external reports, previous literature and expert feedback) and internal consistency concepts which use expected associations within the dataset itself (e.g. completeness, uniformity and plausibility). In an example case, the selected concepts were applied to a transfusion dataset and specified in more detail.

Results

Application of the approach to a transfusion dataset resulted in a structured overview of data validity aspects. This allowed improvement of these aspects through further processing of the data and in some cases adjustment of the data extraction. For example, the proportion of transfused products that could not be linked to the corresponding issued products initially was 2.2% but could be improved by adjusting data extraction criteria to 0.17%.

Conclusions

This stepwise approach for validating linked multisource data provides a basis for evaluating data quality and enhancing interpretation. When the process of data validation is adopted more broadly, this contributes to increased transparency and greater reliability of research based on routinely collected electronic health records.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0504-7) contains supplementary material, which is available to authorized users.