{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:17:04Z","timestamp":1788913024703,"version":"build-2803163510"},"reference-count":33,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2018,5,1]],"date-time":"2018-05-01T00:00:00Z","timestamp":1525132800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vox Sanguinis"],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:sec><jats:title>Background and Objectives<\/jats:title><jats:p>Blood utilization during liver transplant has decreased, but remains highly variable due to many complex surgical and physiologic factors. Previous models attempted to predict utilization using preoperative variables to stratify cases into two usage groups, usually using entire blood units for measurement. We sought to develop a practical predictive model using specific transfusion volumes (in ml) to develop a data\u2010driven patient blood management strategy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>This is a retrospective evaluation of primary liver transplants at a single institution from 2013 to 2015. Multivariable analysis of preoperative recipient and donor factors was used to develop a model predictive of intraoperative red\u2010blood\u2010cell (<jats:styled-content style=\"fixed-case\">pRBC<\/jats:styled-content>) use.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 256 adult liver transplants, 207 patients had complete transfusion volume data for analysis. The median intraoperative allogeneic <jats:styled-content style=\"fixed-case\">pRBC<\/jats:styled-content> transfusion volume was 1250\u00a0ml, and the average was 1563\u00a0\u00b1\u00a01543\u00a0ml. Preoperative haemoglobin, spontaneous bacterial peritonitis, preoperative haemodialysis and preoperative international normalized ratio together yielded the strongest model predicting <jats:styled-content style=\"fixed-case\">pRBC<\/jats:styled-content> usage. When it predicted &lt;1250\u00a0ml of <jats:styled-content style=\"fixed-case\">pRBC<\/jats:styled-content>s, all cases with 0\u00a0ml transfused were captured and only 8\u00b76% of the time &gt;1250\u00a0ml were used. This prediction had a sensitivity of 0\u00b791 and a specificity of 0\u00b789. If predicted usage was &gt;2000\u00a0ml, 75% of the time blood loss exceeded 2000\u00a0ml.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Patients likely to require low or high <jats:styled-content style=\"fixed-case\">pRBC<\/jats:styled-content> transfusion volumes were identified with excellent accuracy using this predictive model at our institution. This model may help predict bleeding risk for each patient and facilitate optimized blood ordering.<\/jats:p><\/jats:sec>","DOI":"10.1111\/vox.12650","type":"journal-article","created":{"date-parts":[[2018,5,1]],"date-time":"2018-05-01T08:10:19Z","timestamp":1525162219000},"page":"421-429","source":"Crossref","is-referenced-by-count":9,"title":["A data\u2010driven patient blood management strategy in liver transplantation"],"prefix":"10.1111","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6074-7147","authenticated-orcid":false,"given":"R. A.","family":"Metcalf","sequence":"first","affiliation":[{"name":"Division of Clinical Pathology Department of Pathology University of Utah Salt Lake City UT USA"},{"name":"ARUP Laboratories Salt Lake City UT USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5183-6471","authenticated-orcid":false,"given":"M. B.","family":"Pagano","sequence":"additional","affiliation":[{"name":"Division of Transfusion Medicine Department of Laboratory Medicine University of Washington Seattle WA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. 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