Eckel‐Passow, Jeanette E.

ABSTRACT The role of combination antibiotic therapy with a beta-lactam and a fluoroquinolone for bacteremia caused by gram-negative bacilli, to our knowledge, has not been previously described. Much of the previous study of combination therapy has included beta-lactams and aminoglycosides. We conducted a large retrospective cohort study to evaluate 28-day all-cause mortality in patients with monomicrobial bacteremia due to aerobic gram-negative bacilli who received either a combination of beta-lactams and fluoroquinolones or beta-lactam monotherapy. We enrolled adult patients admitted to Mayo Clinic hospitals from 1 January 2001 to 31 October 2006 in the study. After stratification of patients by Pitt bacteremia scores, we used Cox regression models to estimate the hazard ratios (HR) for 28-day all-cause mortality after adjusting for the propensity to receive combination therapy. We identified 398 and 304 unique patients with bacteremia caused by gram-negative bacilli who received single and combination antibiotic therapy, respectively. In less severely ill patients with Pitt bacteremia scores of <4, combination therapy was associated with lower 28-day mortality than single therapy (4.2% [9 of 214] versus 8.8% [28 of 319]; adjusted HR, 0.44; 95% confidence interval [CI], 0.20 to 0.98; P = 0.044). In critically ill patients with Pitt bacteremia scores of ≥4, there was no difference in 28-day mortality between combination and single therapy (25.6% [23 of 90] versus 27.8% [22 of 79]; adjusted HR, 0.87; 95% CI, 0.47 to 1.62; P = 0.660). These findings were consistent for 14-day all-cause mortality. In this large cohort, we found for the first time that combination therapy with beta-lactams and fluoroquinolones was associated with a reduction in 28-day all-cause mortality among less severely ill patients with bacteremia caused by gram-negative bacilli.

Abstract Motivation: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for 16O/18O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of 16O/18O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies. Contact: eckel@mayo.edu