Estimation of Sensitivity and Specificity of Bacteriology, Histopathology and PCR for the Confirmatory Diagnosis of Bovine Tuberculosis Using Latent Class Analysis

<div><p>Bacteriology and histopathology are the most commonly used tests used for official confirmatory diagnosis of bovine tuberculosis (bTB) in cattle in most countries. PCR is also being used increasingly because it allows a fast diagnosis. This test could be applied as a supplement to or replacement for current bTB confirmatory diagnostic tests but its characteristics have first to be evaluated. The aim of this study was to estimate and compare sensitivities and specificities of bacteriology, histopathology and PCR under French field conditions, in the absence of a gold standard using latent class analysis. The studied population consisted of 5,211 animals from which samples were subjected to bacteriology and PCR (LSI VetMAX™ <i>Mycobacterium tuberculosis</i> Complex PCR Kit, Life Technologies) as their herd of origin was either suspected or confirmed infected with bTB or because bTB-like lesions were detected during slaughterhouse inspection. Samples from 697 of these animals (all with bTB-like lesions) were subjected to histopathology. Bayesian models were developed, allowing for dependence between bacteriology and PCR, while assuming independence from histopathology. The sensitivity of PCR was higher than that of bacteriology (on average 87.7% [82.5–92.3%] versus 78.1% [72.9–82.8%]) while specificity of both tests was very good (on average 97.0% for PCR [94.3–99.0%] and 99.1% for bacteriology [97.1–100.0%]). Histopathology was at least as sensitive as PCR (on average 93.6% [89.9–96.9%]) but less specific than the two other tests (on average 83.3% [78.7–87.6%]). These results suggest that PCR has the potential to replace bacteriology to confirm bTB in samples submitted from suspect cattle.</p></div

Number of animals in analysis 2 according to (i) their test results for bacteriology (bac), histopathology and PCR and (ii) their population of origin.

<p>POS: positive/NEG: negative.</p

Posterior distributions for sensitivities and specificities of bacteriology and PCR.

<p>These estimations are based on the model assuming independence between bacteriology and PCR (model with the smallest DIC).</p

Mean estimates and 95% posterior credible intervals (CI) of the sensitivity (Se) and specificity (Sp) of bacteriology (bact) and PCR and population specific animal prevalences of the eight models assuming different proportions of maximum conditional covariances (analysis 1).

a<p>Proportion of maximum upper limit of conditional covariance.</p>b<p>Deviance information criterion.</p>c<p>For this model, a thin interval of 3 was defined in order to ensure that the MCMC chains are no longer autocorrelated.</p><p>Note: the results of the model with the lowest DIC are in bold.</p

Mean estimates and 95% posterior credible intervals (CI) of the sensitivity (Se) and specificity (Sp) of bacteriology, histopathology and PCR and population specific animal prevalences of the eight models assuming different proportions of maximum conditional covariances (analysis 2).

a<p>Proportion of maximum upper limit of conditional covariance.</p>b<p>Analysis 2 was carried out on a subgroup of animals (animals subjected to histopathology which all presented macroscopic bTB-like lesions), hence the differences in animal prevalence estimates between analysis 1 and analysis 2.</p>c<p>Deviance information criterion.</p><p>Note: the results of the model with the lowest DIC are in bold.</p

Additional file 1: Figure S1. of High unacylated ghrelin levels support the concept of anorexia in infants with prader-willi syndrome

Nonlinear regression (± SEM) of acylated (AG) (A), unacylated (UAG) ghrelin (B) levels and AG/UAG ratio (C) according to age in both groups. Black line: control; Red line: PWS infants. For Supplementary Figure1, we used nonlinear regressions by B-splines to draw the curves. Because the curves are compatible with linear regressions, we did not use nonlinear regressions for the statistical analysis. (DOCX 86 kb