Preanalytical mistakes in samples from primary care patients.

BACKGROUND Preanalytical mistakes (PAMs) in samples usually led to rejection upon arrival to the clinical laboratory. However, PAMs might not always be detected and result in clinical problems. Thus, PAMs should be minimized. We detected PAMs in samples from Primary Health Care Centres (PHCC) served by our central laboratory. Thus, the goal of this study was to describe the number and types of PAMs, and to suggest some strategies for improvement. METHODS The presence of PAMs, as sample rejection criteria, in samples submitted from PHCC to our laboratory during October and November 2007 was retrospectively analysed. RESULTS Overall, 3885 PAMs (7.4%) were detected from 52,669 samples for blood analyses. This included missed samples (n=1763; 45.4% of all PAMs, 3.3% of all samples), haemolysed samples (n=1408; 36.2% and 2.7%, respectively), coagulated samples (n=391; 10% and 0.7%, respectively), incorrect sample volume (n=110; 2.8% and 0.2%, respectively), and others (n=213; 5.5% and 0.4%, respectively). For urine samples (n=18,852), 1567 of the samples were missing (8.3%). CONCLUSIONS We found the proportion of PAMs in blood and urine samples to be 3-fold higher than that reported in the literature. Therefore, strategies for improvement directed towards the staff involved, as well as an exhaustive audit of preanalytical process are needed. To attain this goal, we first implemented a continued education programme, financed by our Regional Health Service and focused in Primary Care Nurses.

Use of Hy's law and a new composite algorithm to predict acute liver failure in patients with drug-induced liver injury.

BACKGROUND & AIMS Hy's Law, which states that hepatocellular drug-induced liver injury (DILI) with jaundice indicates a serious reaction, is used widely to determine risk for acute liver failure (ALF). We aimed to optimize the definition of Hy's Law and to develop a model for predicting ALF in patients with DILI. METHODS We collected data from 771 patients with DILI (805 episodes) from the Spanish DILI registry, from April 1994 through August 2012. We analyzed data collected at DILI recognition and at the time of peak levels of alanine aminotransferase (ALT) and total bilirubin (TBL). RESULTS Of the 771 patients with DILI, 32 developed ALF. Hepatocellular injury, female sex, high levels of TBL, and a high ratio of aspartate aminotransferase (AST):ALT were independent risk factors for ALF. We compared 3 ways to use Hy's Law to predict which patients would develop ALF; all included TBL greater than 2-fold the upper limit of normal (×ULN) and either ALT level greater than 3 × ULN, a ratio (R) value (ALT × ULN/alkaline phosphatase × ULN) of 5 or greater, or a new ratio (nR) value (ALT or AST, whichever produced the highest ×ULN/ alkaline phosphatase × ULN value) of 5 or greater. At recognition of DILI, the R- and nR-based models identified patients who developed ALF with 67% and 63% specificity, respectively, whereas use of only ALT level identified them with 44% specificity. However, the level of ALT and the nR model each identified patients who developed ALF with 90% sensitivity, whereas the R criteria identified them with 83% sensitivity. An equal number of patients who did and did not develop ALF had alkaline phosphatase levels greater than 2 × ULN. An algorithm based on AST level greater than 17.3 × ULN, TBL greater than 6.6 × ULN, and AST:ALT greater than 1.5 identified patients who developed ALF with 82% specificity and 80% sensitivity. CONCLUSIONS When applied at DILI recognition, the nR criteria for Hy's Law provides the best balance of sensitivity and specificity whereas our new composite algorithm provides additional specificity in predicting the ultimate development of ALF.