Anion gap, anion gap corrected for albumin, and base deficit fail to accurately diagnose clinically significant hyperlactatemia in critically ill patients.

Anion gap, anion gap corrected for serum albumin, and base deficit are often used as surrogates for measuring serum lactate. None of these surrogates is postulated to predict hyperlactatemia in the critically ill. We prospectively collected data from September 2004 through August 2005 for 1381 consecutive admissions. Patients with renal disease, ketoacidosis, or toxic ingestion were excluded. Anion gap, anion gap corrected for albumin, and base deficit were calculated for all patients. We identified 286 patients who met our inclusion or exclusion criteria. The receiver-operating characteristic area under the curve for the prediction of hyperlactatemia for anion gap, anion gap corrected for albumin, and base deficit were 0.55, 0.57, and 0.64, respectively. Anion gap, anion gap corrected for albumin, and base deficit do not predict the presence or absence of clinically significant hyperlactatemia. Serum lactate should be measured in all critically ill adults in whom hypoperfusion is suspected.

Pretransplant model to predict posttransplant survival in liver transplant patients.

OBJECTIVE: To develop a prognostic model that determines patient survival outcomes after orthotopic liver transplantation (OLT) using readily available pretransplant variables. SUMMARY BACKGROUND DATA: The current liver organ allocation system strongly favors organ distribution to critically ill recipients who exhibit poor survival outcomes following OLT. A severely limited organ resource, increasing waiting list deaths, and rising numbers of critically ill recipients mandate an organ allocation system that balances disease severity with survival outcomes. Such goals can be realized only through the development of prognostic models that predict survival following OLT. METHODS: Variables that may affect patient survival following OLT were analyzed in hepatitis C (HCV) recipients at the authors' center, since HCV is the most common indication for OLT. The resulting patient survival model was examined and refined in HCV and non-HCV patients in the United Network for Organ Sharing (UNOS) database. Kaplan-Meier methods, univariate comparisons, and multivariate Cox proportional hazard regression were employed for analyses. RESULTS: Variables identified by multivariate analysis as independent predictors for patient survival following primary transplantation of adult HCV recipients in the last 10 years at the authors' center were entered into a prognostic survival model to predict patient survival. Accordingly, mortality was predicted by 0.0293 (recipient age) + 1.085 (log10 recipient creatinine) + 0.289 (donor female gender) + 0.675 urgent UNOS - 1.612 (log10 recipient creatinine times urgent UNOS). The above variables, in addition to donor age, total bilirubin, prothrombin time (PT), retransplantation, and warm and cold ischemia times, were applied to the UNOS database. Of the 46,942 patients transplanted over the last 10 years, 25,772 patients had complete data sets. An eight-factor model that accurately predicted survival was derived. Accordingly, the mortality index posttransplantation = 0.0084 donor age + 0.019 recipient age + 0.816 log creatinine + 0.0044 warm ischemia (in minutes) + 0.659 (if second transplant) + 0.10 log bilirubin + 0.0087 PT + 0.01 cold ischemia (in hours). Thus, this model is applicable to first or second liver transplants. Patient survival rates based on model-predicted risk scores for death and observed posttransplant survival rates were similar. Additionally, the model accurately predicted survival outcomes for HCV and non-HCV patients. CONCLUSIONS: Posttransplant patient survival can be accurately predicted based on eight straightforward factors. The balanced application of a model for liver transplant survival estimate, in addition to disease severity, as estimated by the model for end-stage liver disease, would markedly improve survival outcomes and maximize patients' benefits following OLT.