The EMALT Score: An Improved Model for Prediction of Early Mortality in Liver Transplant Recipients. (August 2020)

• Record Type: Journal Article
• Authors: Lindenmeyer, Christina C.; Kim, Ahyoung; Sanghi, Vedha; Lopez, Rocio; Niyazi, Fadi; Mehta, Neal A.; Flocco, Gianina; Kapoor, Aanchal; Carey, William D.; Romero-Marrero, Carlos
• Is Part Of: Journal of intensive care medicine. Volume 35:Number 8(2020)
• Page Start: 781
• Page End: 788
• Publication Date: 2020-08
• Subjects: liver transplantation -- in-hospital mortality -- survival -- Acute Physiology and Chronic Health Evaluation III -- Model for End-Stage Liver Disease; Critical care medicine -- Periodicals; Critical Care -- Periodicals; Soins intensifs -- Périodiques; Soins intensifs; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.02805
• DOI: 10.1177/0885066618784869
• Languages: English
• ISSNs: 0885-0666

Abstract

Purpose: Needs, risks, and outcomes of patients admitted to a post liver transplant intensive care unit (POLTICU) differ in important ways from those admitted to pretransplant intensive care units (ICUs). The aim of this study was to create the optimal model to risk stratify POLTICU patients. Methods: Consecutive patients who underwent first deceased donor liver transplantation (LT) at a large United States center between 2008 and 2014 were followed from admission to LT and to discharge or death. Receiver–operating characteristic analysis was performed to assess the value of various scores in predicting in-hospital mortality. A predictive model was developed using logistic regression analysis. Results: A total of 697 patients underwent LT, and 3.2% died without leaving the hospital. A model for in-hospital mortality was derived from variables available within 24 hours of admission to the POLTICU. Key variables best predicting survival were white blood cell count, 24-hour urine output, and serum glucose. A model using these variables performed with an area under the curve (AUC) of 0.88, compared to the Acute Physiology and Chronic Health Evaluation III and Model for End-Stage Liver Disease, which performed with AUCs of 0.74 and 0.60, respectively. Conclusion: An improved model, the early mortality after LT (EMALT) score, performs better than conventional models in predicting in-hospital mortality after LT.