Time to Result for Pathogen Identification and Antimicrobial Susceptibility Testing of Bronchoalveolar Lavage and Endotracheal Aspirate Specimens in U.S. Acute Care Hospitals.

Identification (ID) and antimicrobial susceptibility testing (AST) of respiratory pathogens are critical to the management of patients with pneumonia to facilitate optimal antibiotic therapy selection. Few studies have examined the time to results (TTR) for this critical specimen, and such data can be valuable for benchmarking the current paradigm of diagnostic approaches. TTR for bronchoalveolar lavage (BAL) and endotracheal aspirate (ETA) specimens from hospitalized patients was evaluated using the Premier Healthcare Database, a comprehensive database of 194 U.S. hospitals. Times from specimen collection to reporting of organism ID/AST were evaluated and compared by specimen types and characteristics. A total of 79,662 (43,129 BAL; 36,533 ETA) specimens were included, of which 19.3% harbored no growth, 47.1% contained normal respiratory flora alone (including yeast), and 0.6% contained mycobacteria/molds. Potential bacterial pathogens (PBP) were recovered from 33.0%. ETA specimens had a higher proportion of specimens with isolation of PBP (39.2% versus 27.7%) and with normal respiratory flora (52.0% versus 43.0%) and were less likely to be negative (8.2% versus 28.6%) than BAL specimens (all P < 0.0001). Staphylococcus aureus and Pseudomonas aeruginosa were isolated in 10.5 and 6.4% of the specimens, respectively, and were the most common organisms identified. Median (interquartile range) TTR were 37.0 h (21.8 to 51.7 h) and 60.5 h (46.6 to 72.4 h) for ID and AST, respectively. Median TTR for major respiratory pathogens by organism ranged from 29.2 to 43.9 h for ID and from 47.9 to 73.9 h for AST. Organism type, specimen collection time, and hospital teaching status influenced TTR. Mechanically vented patients and ETA specimens were more likely to recover PBP.

Diagnostics for insufficiencies of posterior calculations in Bayesian signal inference.

We present an error-diagnostic validation method for posterior distributions in Bayesian signal inference, an advancement of a previous work. It transfers deviations from the correct posterior into characteristic deviations from a uniform distribution of a quantity constructed for this purpose. We show that this method is able to reveal and discriminate several kinds of numerical and approximation errors, as well as their impact on the posterior distribution. For this we present four typical analytical examples of posteriors with incorrect variance, skewness, position of the maximum, or normalization. We show further how this test can be applied to multidimensional signals.

Improving stochastic estimates with inference methods: calculating matrix diagonals.

Estimating the diagonal entries of a matrix, that is not directly accessible but only available as a linear operator in the form of a computer routine, is a common necessity in many computational applications, especially in image reconstruction and statistical inference. Here, methods of statistical inference are used to improve the accuracy or the computational costs of matrix probing methods to estimate matrix diagonals. In particular, the generalized Wiener filter methodology, as developed within information field theory, is shown to significantly improve estimates based on only a few sampling probes, in cases in which some form of continuity of the solution can be assumed. The strength, length scale, and precise functional form of the exploited autocorrelation function of the matrix diagonal is determined from the probes themselves. The developed algorithm is successfully applied to mock and real world problems. These performance tests show that, in situations where a matrix diagonal has to be calculated from only a small number of computationally expensive probes, a speedup by a factor of 2 to 10 is possible with the proposed method.

Reconstructing signals from noisy data with unknown signal and noise covariance.

We derive a method to reconstruct Gaussian signals from linear measurements with Gaussian noise. This new algorithm is intended for applications in astrophysics and other sciences. The starting point of our considerations is the principle of minimum Gibbs free energy, which was previously used to derive a signal reconstruction algorithm handling uncertainties in the signal covariance. We extend this algorithm to simultaneously uncertain noise and signal covariances using the same principles in the derivation. The resulting equations are general enough to be applied in many different contexts. We demonstrate the performance of the algorithm by applying it to specific example situations and compare it to algorithms not allowing for uncertainties in the noise covariance. The results show that the method we suggest performs very well under a variety of circumstances and is indeed qualitatively superior to the other methods in cases where uncertainty in the noise covariance is present.