| 1. |
- Athan, E., et al.
(författare)
-
Influence of vancomycin minimum inhibitory concentration on the outcome of methicillin-susceptible Staphylococcus aureus left-sided infective endocarditis treated with antistaphylococcal β-lactam antibiotics: a prospective cohort study by the International Collaboration on Endocarditis
- 2017
-
Ingår i: Clinical Microbiology and Infection. - : Elsevier BV. - 1198-743X .- 1469-0691. ; 23, s. 544-549
-
Tidskriftsartikel (refereegranskat)abstract
- © 2017 European Society of Clinical Microbiology and Infectious Diseases Objectives Left-sided methicillin-susceptible Staphylococcus aureus (MSSA) endocarditis treated with cloxacillin has a poorer prognosis when the vancomycin minimum inhibitory concentration (MIC) is ≥1.5 mg/L. We aimed to validate this using the International Collaboration on Endocarditis cohort and to analyse whether specific genetic characteristics were associated with a high vancomycin MIC (≥1.5 mg/L) phenotype. Methods All patients with left-sided MSSA infective endocarditis treated with antistaphylococcal β-lactam antibiotics between 2000 and 2006 with available isolates were included. Vancomycin MIC was determined by Etest as either high (≥1.5 mg/L) or low (<1.5 mg/L). Isolates underwent spa typing to infer clonal complexes and multiplex PCR for identifying virulence genes. Univariate analysis was performed to evaluate the association between in-hospital and 1-year mortality, and vancomycin MIC phenotype. Results Sixty-two cases met the inclusion criteria. Vancomycin MIC was low in 28 cases (45%) and high in 34 cases (55%). No significant differences in patient demographic data or characteristics of infection were observed between patients with infective endocarditis due to high and low vancomycin MIC isolates. Isolates with high and low vancomycin MIC had similar distributions of virulence genes and clonal lineages. In-hospital and 1-year mortality did not differ significantly between the two groups (32% (9/28) vs. 27% (9/34), p 0.780; and 43% (12/28) vs. 29% (10/34), p 0.298, for low and high vancomycin MIC respectively). Conclusions In this international cohort of patients with left-sided MSSA endocarditis treated with antistaphylococcal β-lactams, vancomycin MIC phenotype was not associated with patient demographics, clinical outcome or virulence gene repertoire.
|
|
| 2. |
|
|
| 3. |
- Bergman, Jakob, et al.
(författare)
-
Compositional Loess modeling
- 2011
-
Ingår i: Proceedings of the 4th International Workshop on Compositional Data Analysis. - 9788487867767
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Cleveland (1979) is usually credited with the introduction of the locally weighted regression, Loess. The concept was further developed by Cleveland and Devlin (1988). The general idea is that for an arbitrary number of explanatory data points xi the value of a dependent variable is estimated ŷi. The ŷi is the fitted value from a dth degree polynomial in xi. (In practice often d = 1.) The ŷi is fitted using weighted least squares, WLS, where the points xk (k = 1, ..., n) closest to xi are given the largest weights. We define a weighted least squares estimation for compositional data, C-WLS. In WLS the sum of the weighted squared Euclidean distances between the observed and the estimated values is minimized. In C-WLS we minimize the weighted sum of the squared simplicial distances (Aitchison, 1986, p. 193) between the observed compositions and their estimates. We then define a compositional locally weighted regression, C-Loess. Here a composition is assumed to be explained by a real valued (multivariate) variable. For an arbitrary number of data points xi we for each xi fit a dth degree polynomial in xi yielding an estimate ŷi of the composition yi. We use C-WLS to fit the polynomial giving the largest weights to the points xk (k = 1, ..., n) closest to xi. Finally the C-Loess is applied to Swedish opinion poll data to create a poll-of-polls time series. The results are compared to previous results not acknowledging the compositional structure of the data.
|
|
