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Platelet Transfusion Management using R and the Tidyverse
January 31, 2020
Development of a web-based clinical decision support application for platelet transfusion management using R and the Tidyverse Blood product transfusion is a high risk and costly medical procedure. Platelets (blood cells that initiate clotting) are a rare and expensive blood product with a short shelf life. Proper management of platelet transfusions is essential to clinical care, particularly for patients who have developed antibodies against specific platelet types due to pregnancy or past transfusions. By providing platelets that avoid a patient’s known antibodies, improved patient outcomes and better inventory management of a rare blood product are achieved. To address this need, we used R, Tidyverse, and several key packages (Shiny, shinydashboard, dplyr, purrr, httr, officer, flextables, futures) to develop a web-based application (PLTVXM) to help guide platelet inventory selection. PLTVXM queries information on available/pending platelet inventory (and eligible donors) from reports that run in our institutional reporting tool Tableau® via a Tableau Server REST API. Patient antibody and blood type information is securely retrieved from a clinical data lake via an in-house R package (“dart”) and a custom institutional API. The retrieved data is processed by a published algorithm implemented in R and incorporates user input to present sortable tables of patient-specific compatible platelet inventory (and donors) for consideration. The requisite documentation for platelet product reservation or donor recruitment is then autogenerated using institutional form templates. PLTVXM is deployed on an RStudio Connect server which allows seamless integration with our institution’s Active Directory identity management infrastructure. The pilot version of PLTVXM was created by physicians without formal computer programming training in two weeks. After successful demonstration, PLTVXM was approved for clinical validation and future use in our practice. Our experience highlights how R can facilitate creation of dynamic web-based applications for a wide range of business (or clinical) needs.
Justin Juskewitch received his MD, PhD in clinical & translational sciences and medical residency training in pathology & laboratory medicine from Mayo Clinic in Rochester, MN. He is currently completing a combined clinical informatics and transfusion medicine fellowship at Mayo Clinic.