An international multicenter retrospective study of Pseudomonas aeruginosa nosocomial pneumonia: Impact of multidrug resistance

Introduction: Pseudomonas aeruginosa nosocomial pneumonia (Pa-NP) is associated with considerable morbidity, prolonged hospitalization, increased costs, and mortality. Methods: We conducted a retrospective cohort study of adult patients with Pa-NP to determine 1) risk factors for multidrug-resistant (MDR) strains and 2) whether MDR increases the risk for hospital death. Twelve hospitals in 5 countries (United States, n = 3; France, n = 2; Germany, n = 2; Italy, n = 2; and Spain, n = 3) participated. We compared characteristics of patients who had MDR strains to those who did not and derived regression models to identify predictors of MDR and hospital mortality. Results: Of 740 patients with Pa-NP, 226 patients (30.5%) were infected with MDR strains. In multivariable analyses, independent predictors of multidrug-resistance included decreasing age (adjusted odds ratio [AOR] 0.91, 95% confidence interval [CI] 0.96-0.98), diabetes mellitus (AOR 1.90, 95% CI 1.21-3.00) and ICU admission (AOR 1.73, 95% CI 1.06-2.81). Multidrug-resistance, heart failure, increasing age, mechanical ventilation, and bacteremia were independently associated with in-hospital mortality in the Cox Proportional Hazards Model analysis. Conclusions: Among patients with Pa-NP the presence of infection with a MDR strain is associated with increased in-hospital mortality. Identification of patients at risk of MDR Pa-NP could facilitate appropriate empiric antibiotic decisions that in turn could lead to improved hospital survival

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe