Validation of 5 models predicting transfusion, bleeding, and mortality in liver transplantation: an observational cohort study

Abstract

Background: Historically, orthotopic liver transplantation (OLT) has been associated with massive blood loss, blood transfusion and morbidity. In order to predict such outcomes five nomograms have been published relating to transfusions and morbidity associated with OLTs. These nomograms, developed on the basis of three cohorts of patients consisting of 406, 750, and 800 having undergone OLTs, aimed to predict a transfusion of ≥1 red blood cell unit (RBC), a transfusion of >2 RBC units, a blood loss of >900 ml, as well as one-month and one-year survival rates. The aim of this study was to validate these five nomograms in a contemporary, independent cohort of patients.

Methods: Five nomograms were previously developed based on 406, 750, and 800 OLTs. In this study we performed a temporal validation of these nomograms on contemporary patients that consisted of three cohorts of 800, 250, and 200 OLTs. Logistic regression coefficients from the historic development cohorts were applied to the three contemporary temporal validation cohorts.

Results: The most accurate nomogram was able to predict transfusion of ≥1 RBC units with an area under the curve (AUC) was 0.91. The second-best nomogram was able to predict bleeding of >900 ml with an AUC of 0.70. T he AUC of the third nomogram (transfusion of >2 RBC units) was 0.70. However, is temporal validation was suboptimal, due to a low prevalence of OLTs transfused with >2 RBC units. The last 2 nomograms exhibited clearly suboptimal AUC values of 0.54 and 0.61.

Conclusion: Two of the five nomograms predict blood transfusion and blood loss with excellent accuracy. Transfusion of ≥1 RBC unit and blood loss of >900 ml can be predicted on the basis of these nomograms. However, these nomograms are not accurate to predict one-month and one-year survival rates. These results should be further cross-validated, ideally prospectively, in additional external independent cohorts.