Protocol for a National Blood Transfusion Data Warehouse from Donor to Recipient

Overview

Journal BMJ Open

Specialty General Medicine

Date 2016 Aug 6

PMID 27491665

Citations 4

Authors

Loan R van Hoeven

Babette H Hooftman

Mart P Janssen

Martine C de Bruijne

Karen M K de Vooght

Peter Kemper

Maria M W Koopman

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

The Swedish Scandinavian donations and transfusions database (SCANDAT3-S) - 50 years of donor and recipient follow-up.

Zhao J, Rostgaard K, Hjalgrim H, Edgren G Transfusion. 2020; 60(12):3019-3027.

PMID: 32827155 PMC: 7754339. DOI: 10.1111/trf.16027.

A Conceptual Framework for Optimizing Blood Matching Strategies: Balancing Patient Complications Against Total Costs Incurred.

van Sambeeck J, de Wit P, Luken J, Veldhuisen B, van den Hurk K, Van Dongen A Front Med (Lausanne). 2018; 5:199.

PMID: 30090809 PMC: 6069448. DOI: 10.3389/fmed.2018.00199.

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

van Hoeven L, Kreuger A, Roes K, Kemper P, Koffijberg H, Kranenburg F Clin Epidemiol. 2018; 10:353-362.

PMID: 29636633 PMC: 5881526. DOI: 10.2147/CLEP.S147142.

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

van Hoeven L, de Bruijne M, Kemper P, Koopman M, Rondeel J, Leyte A BMC Med Inform Decis Mak. 2017; 17(1):107.

PMID: 28709453 PMC: 5512751. DOI: 10.1186/s12911-017-0504-7.

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