Literature DB >> 28718098

Massive hemorrhage and transfusion in the operating room.

Brian Muirhead1, Andrew D H Weiss2.   

Abstract

PURPOSE: In this Continuing Professional Development module, we review the pathophysiology and clinical manifestations associated with massive hemorrhage as well as laboratory investigations and appropriate therapeutic measures. In addition to reviewing the available blood/plasma products and adjunct therapy, we also explore the role of the anesthesiologist in a massive transfusion protocol scenario. PRINCIPAL
FINDINGS: Massive hemorrhage can be either anticipated or unexpected. The coinciding presence of acidosis, hypothermia, and hypotension contribute greatly to a poor outcome. Red blood cells not only increase oxygen carrying capacity, but they also play a role in providing hemostasis. While timely laboratory results, including point-of-care testing, are important, transfusion remains a clinical decision. Adjunct therapies other than blood components have contributed to improved outcomes. The pathophysiology of massive obstetric hemorrhage is unique when compared with the non-obstetric population. The approach to massive hemorrhage and its treatment vary considerably from institution to institution.
CONCLUSIONS: Massive hemorrhage is a multidisciplinary challenge that requires immediate response and communication between clinicians, nurses, other healthcare providers, laboratory testing, and blood banks. Basic knowledge and utilization of available products and therapies are inconsistent. A massive transfusion protocol can be used effectively to reduce chaos and ensure that correct treatments and proper dosing occur in a timely manner.

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Year:  2017        PMID: 28718098     DOI: 10.1007/s12630-017-0925-x

Source DB:  PubMed          Journal:  Can J Anaesth        ISSN: 0832-610X            Impact factor:   5.063


  2 in total

1.  Acute compartment syndrome due to extravasation of peripheral intravenous blood transfusion.

Authors:  Chanyang Park; Hyuckgoo Kim
Journal:  Saudi J Anaesth       Date:  2020-03-05

2.  Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study.

Authors:  Sai Chen; Le-Ping Liu; Yong-Jun Wang; Xiong-Hui Zhou; Hang Dong; Zi-Wei Chen; Jiang Wu; Rong Gui; Qin-Yu Zhao
Journal:  Front Neuroinform       Date:  2022-05-13       Impact factor: 3.739

  2 in total

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