A clinical decision support system for preventing adverse reactions to blood transfusion

Dennis H. Murphree, Leanne Clifford, Yaxiong Lin, Nagesh Madde, Che Ngufor, Sudhindra Upadhyaya, Jyotishman Pathak, Daryl J Kor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

During 2011 approximately 21 million blood components were transfused in the United States, with roughly 1 in 414 resulting in complication. For Americans, the two leading causes of transfusion-related death are the respiratory complications Transfusion-related acute lung injury (TRALI) and Transfusion-associated circulatory overload (TACO). Each of these complications results in significantly longer ICU and hospital stays as well as significantly greater rates of mortality. We have developed a set of machine learning models for predicting the likelihood of these adverse reactions in surgical populations. Here we describe deploying these models into a perioperative critical care environment via a continuous monitoring and alerting clinical decision support system. The goal of this system, which directly integrates our suite of machine learning models running in the R statistical environment into a traditional health information system, is to improve transfusion-related outcomes in the perioperative environment. By identifying high-risk patients prior to transfusion, the clinical team may be able to choose a more appropriate therapy or therapeutic course. Identifying high-risk patients for increased observation after transfusion may also allow for a more timely intervention, thereby potentially improving care delivery and resulting patient outcome. An early prototype of this system is currently running in two Mayo Clinic perioperative environments.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages100-104
Number of pages5
ISBN (Print)9781467395489
DOIs
StatePublished - Dec 8 2015
Event3rd IEEE International Conference on Healthcare Informatics, ICHI 2015 - Dallas, United States
Duration: Oct 21 2015Oct 23 2015

Other

Other3rd IEEE International Conference on Healthcare Informatics, ICHI 2015
CountryUnited States
CityDallas
Period10/21/1510/23/15

Keywords

  • Clinical decision support
  • Continuous monitoring
  • Prediction
  • System integration
  • Transfusion

ASJC Scopus subject areas

  • Health Informatics

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  • Cite this

    Murphree, D. H., Clifford, L., Lin, Y., Madde, N., Ngufor, C., Upadhyaya, S., Pathak, J., & Kor, D. J. (2015). A clinical decision support system for preventing adverse reactions to blood transfusion. In Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015 (pp. 100-104). [7349680] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2015.19