Literature DB >> 33368321

Defining postoperative transfusion thresholds in liver transplant recipients: A novel retrospective approach.

Joseph P Connor1, David Aufhauser2, Bridget M Welch3, Glen Leverson2, David Al-Adra2.   

Abstract

BACKGROUND: The optimal transfusion threshold for most patient populations has been defined as hematocrit (HCT) <21%. However, some specific patient populations are known to benefit from higher transfusion thresholds. To date, the optimal postoperative transfusion threshold for patients undergoing liver transplant has not been determined. To define the ideal transfusion threshold for liver transplant patients, we designed a retrospective study of 496 liver transplant recipients.
METHODS: Using HCT prior to discharge as a surrogate marker for transfusion thresholds we grouped patients into three groups of transfusion thresholds (HCT <21%, <24%, and >30%). Transfusion rates (intra- and postoperative), graft and patient survival, and complications requiring readmission were compared between groups.
RESULTS: Ninety-two percent of patients were transfused during their hospital stay. Graft survival, patient survival, and rates of readmission within 30 days of discharge were no different between the three discharge HCT groups. Patients discharged with HCT >30% were less likely to be readmitted with infectious complications; however, this group also had the lowest model of end-stage liver (MELD) score at time of transplantation and were less likely to have received a transfusion during their hospital stay.
CONCLUSION: Transfusion thresholds of HCT <24%, and potentially as low as 21% are acceptable in postoperative liver transplant recipients. The conduct of a randomized clinical trial, as supported by these data, will be necessary to support the use of lower thresholds.
© 2020 AABB.

Entities:  

Keywords:  RBC transfusion; blood management; transfusion practices (surgical)

Mesh:

Year:  2020        PMID: 33368321     DOI: 10.1111/trf.16244

Source DB:  PubMed          Journal:  Transfusion        ISSN: 0041-1132            Impact factor:   3.157


  1 in total

1.  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

  1 in total

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