Medical errors related to inappropriate genetic testing in liver transplant patients

Genetic testing of genes that encode proteins expressed by liver hepatocytes (clotting factors, alpha 1-antitrypsin, cytochrome P450 enzymes) is common in clinical practice. These tests use DNA extracted from peripheral blood lymphocytes (PBL) and are based on the assumption that PBL DNA can be used as a surrogate for hepatocyte DNA. However, in individuals who have undergone liver transplantation, hepatocyte DNA is that of the donor while PBL DNA remains that of the recipient. It follows that in liver transplant patients, genetic testing of the recipient's PBL DNA does not provide accurate results for proteins expressed by donor hepatocytes. Therefore, genetic testing of clotting factors, alpha 1-1-antitrypsin, cytochrome P450 enzymes, and other proteins expressed by hepatocytes is unreliable and inappropriate in liver transplant patients (inappropriate genetic testing). A review of the records of 215 consecutive liver transplant patients at our institution identified: one medical error and one near-miss medical error related to inappropriate genetic testing, 14 cases of inappropriate genetic testing, and 21 unnecessary duplicate genetic testing requests. We recommend laboratories performing genetic testing create systems to prevent inappropriate and duplicate genetic testing and that physicians be cognizant of the appropriate indications for genetic testing in liver transplant patients.     

Interobserver errors in anthropometry

To present basic information on the interobserver precision and accuracy of 32 selected anthropometric measurement items, six observers measured each of 37 subjects once in two days. The data were analyzed by using ANOVA, and mean absolute bias, standard deviation of bias, and mean absolute bias in standard deviation unit were used as measures of bias. By comparing the results of the two days, the effects of the practice on measurement errors were also investigated. Variance was overestimated by more than 10% in five measurements. Interobserver error variance and random error variance were highly correlated with each other. Measures of the bias were significantly correlated with interobserver and especially with random error variances. The interobserver errors were drastically reduced on the second day in the measurement items in which the causes of the interobserver errors could be specified. It was speculated that even when the definitions of the landmarks and measurement items were clear, the ambiguity in the practical procedures in locating landmarks, applying instruments, and so on, permitted each observer to develop his or her own measurement technique, and it in turn caused interobserver errors. To minimize interobserver and random errors, the standardization of measurement technique should be extended to the details of the practical procedures.     

Stochastic errors vs. modeling errors in distance based phylogenetic reconstructions

ABSTRACT: BACKGROUND: Distance-based phylogenetic reconstruction methods use evolutionary distances between species in order to reconstruct the phylogenetic tree spanning them. There are many different methods for estimating distances from sequence data. These methods assume different substitution models and have different statistical properties. Since the true substitution model is typically unknown, it is important to consider the effect of model misspecification on the performance of a distance estimation method. RESULTS: This paper continues the line of research which attempts to adjust to each given set of input sequences a distance function which maximizes the expected topological accuracy of the reconstructed tree. We focus here on the effect of systematic error caused by assuming an inadequate model, but consider also the stochastic error caused by using short sequences. We introduce a theoretical framework for analyzing both sources of error based on the notion of deviation from additivity, which quantifies the contribution of model misspecification to the estimation error. We demonstrate this framework by studying the behavior of the Jukes-Cantor distance function when applied to data generated according to Kimura's two-parameter model with a transition-transversion bias. We provide both a theoretical derivation for this case, and a detailed simulation study on quartet trees. CONCLUSIONS: We demonstrate both analytically and experimentally that by deliberately assuming an oversimplified evolutionary model, it is possible to increase the topological accuracy of reconstruction. Our theoretical framework provides new insights into the mechanisms that enables statistically inconsistent reconstruction methods to outperform consistent methods.     

Intrinsic errors in genome annotation

Genome sequencing is usually followed by routine annotation of protein function based on the assumption that similar sequences will have similar functions. Here, we introduce a simple calculation to estimate the magnitude of any possible annotation errors. We counted the number of discrepancies in the annotation of well-established sets of similar proteins and extrapolated these values to the pairs of similar sequences used for the annotation of different microbial genomes. We conclude that the number of potential errors in the prediction of detailed functions is higher than is usually believed.