Using trends and outliers in managing delayed transfusions

Document Type

Journal Article

Publication Date

2-20-2023

Journal

Transfusion medicine (Oxford, England)

DOI

10.1111/tme.12960

Keywords

delayed transfusion; machine learning; outlier detection

Abstract

OBJECTIVES: To investigate if time to initiate a blood transfusion after an informative laboratory test could feasibly be used by the transfusion medicine service as a metric to monitor for transfusion delays. BACKGROUND: Delayed transfusions may result in patient morbidity and mortality, but no standards for timely transfusion have been developed. Information technology tools could be implemented to identify gaps in provision of blood and to recognise areas of improvement. MATERIALS AND METHODS: Data obtained from a children's hospital's data science platform and time from the release of laboratory results to the initiation of transfusions were calculated and weekly medians were used for trend analyses. Outlier events were obtained using locally estimated scatterplot smoothing and generalised extreme studentized deviate test. RESULTS: Overall, the number of outlier events on the timing of transfusions based on patients' haemoglobin level and platelet count were small (n = 1 and n = 0 for 139 weeks, respectively). Investigation of these events for adverse clinical outcomes was non-significant. CONCLUSIONS: Herein, we propose that the trends and outlier events could be further investigated and used to make decisions and implement protocols to improve patient care.

Department

Pathology

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