A Large Multicenter Prospective Study of Community-Onset Healthcare Associated Bacteremic Urinary Tract Infections in the Era of Multidrug Resistance: Even Worse than Hospital Acquired Infections?

Introduction: Healthcare-associated (HCA) infections represent a growing public health problem. The aim of this study was to compare community-onset healthcare associated (CO-HCA) bacteremic urinary tract infections (BUTI) and hospital-acquired (HA)-BUTI with special focus on multidrug resistances (MDR) and outcomes. Methods: ITUBRAS-project is a prospective multicenter cohort study of patients with HCA-BUTI. All consecutive hospitalized adult patients with CO-HCA-BUTI or HA-BUTI episode were included in the study. Exclusion criteria were: patients < 18 years old, non-hospitalized patients, bacteremia from another source or primary bacteremia, non-healthcare-related infections and infections caused by unusual pathogens of the urinary tract. The main outcome variable was 30-day all-cause mortality with day 1 as the first day of positive blood culture. Logistic regression was used to analyze factors associated with clinical cure at hospital discharge and with receiving inappropriate initial antibiotic treatment. Cox regression was used to evaluate 30-day all-cause mortality. Results: Four hundred forty-three episodes were included, 223 CO-HCA-BUTI. Patients with CO-HCA-BUTI were older (p < 0.001) and had more underlying diseases (p = 0.029) than those with HA-BUTI. The severity of the acute illness (Pitt score) was also higher in CO-HCA-BUTI (p = 0.026). Overall, a very high rate of MDR profiles (271/443, 61.2%) was observed, with no statistical differences between groups. In multivariable analysis, inadequate empirical treatment was associated with MDR profile (aOR 3.35; 95% CI 1.77–6.35), Pseudomonas aeruginosa (aOR 2.86; 95% CI 1.27–6.44) and Charlson index (aOR 1.11; 95% CI 1.01–1.23). Mortality was not associated with the site of acquisition of the infection or the presence of MDR profile. However, in the logistic regression analyses patients with CO-HCA-BUTI (aOR 0.61; 95% CI 0.40–0.93) were less likely to present clinical cure. Conclusion: The rate of MDR infections was worryingly high in our study. No differences in MDR rates were found between CO-HCA-BUTI and HA-BUTI, in the probability of receiving inappropriate empirical treatment or in 30-day mortality. However, CO-HCA-BUTIs were associated with worse clinical cure. © 2021, The Author(s)

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts