Pseudomonas aeruginosa Community-Onset Bloodstream Infections: Characterization, Diagnostic Predictors, and Predictive Score Development—Results from the PRO-BAC Cohort

Pseudomonas aeruginosa; Infección del torrente sanguíneo; EpidemiologíaPseudomones aeruginosa; Infecció del torrent sanguini; EpidemiologiaPseudomonas aeruginosa; Bloodstream infection; EpidemiologyCommunity-onset bloodstream infections (CO-BSI) caused by gram-negative bacilli are common and associated with significant mortality; those caused by Pseudomonas aeruginosa are associated with worse prognosis and higher rates of inadequateempirical antibiotic treatment. The aims of this study were to describe the characteristics of patients with CO-BSI caused by P. aeruginosa, to identify predictors, and to develop a predictive score for P. aeruginosa CO-BSI. Materials/methods: PROBAC is a prospective cohort including patients >14 years with BSI from 26 Spanish hospitals between October 2016 and May 2017. Patients with monomicrobial P. aeruginosa CO-BSI and monomicrobial Enterobacterales CO-BSI were included. Variables of interest were collected. Independent predictors of Pseudomonas aeruginosa CO-BSI were identified by logistic regression and a prediction score was developed. Results: A total of 78patients with P. aeruginosa CO-BSI and 2572 with Enterobacterales CO-BSI were included. Patients with P. aeruginosa had a median age of 70 years (IQR 60-79), 68.8% were male, median Charlson score was 5 (IQR 3-7), and 30-daymortality was 18.5%. Multivariate analysis identified the following predictors of CO-BSI-PA [adjusted OR (95% CI)]: male gender [1.89 (1.14-3.12)], haematological malignancy [2.45 (1.20-4.99)], obstructive uropathy [2.86 (1.13-3.02)], source of infection other than urinary tract, biliary tract or intra-abdominal [6.69 (4.10-10.92)] and healthcare-associated BSI [1.85 (1.13-3.02)]. Anindex predictive of CO-BSI-PA was developed; scores ≥ 3.5 showed a negative predictive value of 89% and an area under the receiver operator curve (ROC) of 0.66. Conclusions: We did not find a good predictive score of P. aeruginosa CO-BSI due to its relatively low incidence in the overall population. Our model includes variables that are easy to collect in real clinical practice and could be useful to detect patients with very low risk of P. aeruginosa CO-BSI

Pseudomonas aeruginosa Community-Onset Bloodstream Infections: Characterization, Diagnostic Predictors, and Predictive Score Development-Results from the PRO-BAC Cohort

Abstract Community-onset bloodstream infections (CO-BSI) caused by gram-negative bacilli are common and associated with significant mortality; those caused by Pseudomonas aeruginosa are associated with worse prognosis and higher rates of inadequateempirical antibiotic treatment. The aims of this study were to describe the characteristics of patients with CO-BSI caused by P. aeruginosa, to identify predictors, and to develop a predictive score for P. aeruginosa CO-BSI. Materials/methods: PROBAC is a prospective cohort including patients >14 years with BSI from 26 Spanish hospitals between October 2016 and May 2017. Patients with monomicrobial P. aeruginosa CO-BSI and monomicrobial Enterobacterales CO-BSI were included. Variables of interest were collected. Independent predictors of Pseudomonas aeruginosa CO-BSI were identified by logistic regression and a prediction score was developed. Results: A total of 78patients with P. aeruginosa CO-BSI and 2572 with Enterobacterales CO-BSI were included. Patients with P. aeruginosa had a median age of 70 years (IQR 60-79), 68.8% were male, median Charlson score was 5 (IQR 3-7), and 30-daymortality was 18.5%. Multivariate analysis identified the following predictors of CO-BSI-PA [adjusted OR (95% CI)]: male gender [1.89 (1.14-3.12)], haematological malignancy [2.45 (1.20-4.99)], obstructive uropathy [2.86 (1.13-3.02)], source of infection other than urinary tract, biliary tract or intra-abdominal [6.69 (4.10-10.92)] and healthcare-associated BSI [1.85 (1.13-3.02)]. Anindex predictive of CO-BSI-PA was developed; scores ? 3.5 showed a negative predictive value of 89% and an area under the receiver operator curve (ROC) of 0.66. Conclusions: We did not find a good predictive score of P. aeruginosa CO-BSI due to its relatively low incidence in the overall population. Our model includes variables that are easy to collect in real clinical practice and could be useful to detect patients with very low risk of P. aeruginosa CO-BSI.This work was financed by grants from the Plan Nacional de I + D+i 2013–2016-Instituto de Salud Carlos III (Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Ciencia, Innovación y Universidades) [PI16/01432]; and Spanish Network for Research in Infectious Diseases (REIPI) [RD16/0016/0001; RD16/0016/0008]; co-financed by the European Development Regional Fund “A way to achieve Europe”, Operative program Intelligent Growth 2014–2020.Pseudomonas aeruginosaBacteraemiaBloodstream infectionCommunity-onsetEpidemiolog

Pseudomonas aeruginosa Community-Onset Bloodstream Infections: Characterization, Diagnostic Predictors, and Predictive Score Development—Results from the PRO-BAC Cohort

Community-onset bloodstream infections (CO-BSI) caused by gram-negative bacilli are common and associated with significant mortality; those caused by Pseudomonas aeruginosa are associated with worse prognosis and higher rates of inadequateempirical antibiotic treatment. The aims of this study were to describe the characteristics of patients with CO-BSI caused by P. aeruginosa, to identify predictors, and to develop a predictive score for P. aeruginosa CO-BSI. Materials/methods: PROBAC is a prospective cohort including patients >14 years with BSI from 26 Spanish hospitals between October 2016 and May 2017. Patients with monomicrobial P. aeruginosa CO-BSI and monomicrobial Enterobacterales CO-BSI were included. Variables of interest were collected. Independent predictors of Pseudomonas aeruginosa CO-BSI were identified by logistic regression and a prediction score was developed. Results: A total of 78patients with P. aeruginosa CO-BSI and 2572 with Enterobacterales CO-BSI were included. Patients with P. aeruginosa had a median age of 70 years (IQR 60–79), 68.8% were male, median Charlson score was 5 (IQR 3–7), and 30-daymortality was 18.5%. Multivariate analysis identified the following predictors of CO-BSI-PA [adjusted OR (95% CI)]: male gender [1.89 (1.14–3.12)], haematological malignancy [2.45 (1.20–4.99)], obstructive uropathy [2.86 (1.13–3.02)], source of infection other than urinary tract, biliary tract or intra-abdominal [6.69 (4.10–10.92)] and healthcare-associated BSI [1.85 (1.13–3.02)]. Anindex predictive of CO-BSI-PA was developed; scores ≥ 3.5 showed a negative predictive value of 89% and an area under the receiver operator curve (ROC) of 0.66. Conclusions: We did not find a good predictive score of P. aeruginosa CO-BSI due to its relatively low incidence in the overall population. Our model includes variables that are easy to collect in real clinical practice and could be useful to detect patients with very low risk of P. aeruginosa CO-BSI

Inflammation and Nutritional Science for Programs/Policies and Interpretation of Research Evidence (INSPIRE).

An increasing recognition has emerged of the complexities of the global health agenda-specifically, the collision of infections and noncommunicable diseases and the dual burden of over- and undernutrition. Of particular practical concern are both 1) the need for a better understanding of the bidirectional relations between nutritional status and the development and function of the immune and inflammatory response and 2) the specific impact of the inflammatory response on the selection, use, and interpretation of nutrient biomarkers. The goal of the Inflammation and Nutritional Science for Programs/Policies and Interpretation of Research Evidence (INSPIRE) is to provide guidance for those users represented by the global food and nutrition enterprise. These include researchers (bench and clinical), clinicians providing care/treatment, those developing and evaluating programs/interventions at scale, and those responsible for generating evidence-based policy. The INSPIRE process included convening 5 thematic working groups (WGs) charged with developing summary reports around the following issues: 1) basic overview of the interactions between nutrition, immune function, and the inflammatory response; 2) examination of the evidence regarding the impact of nutrition on immune function and inflammation; 3) evaluation of the impact of inflammation and clinical conditions (acute and chronic) on nutrition; 4) examination of existing and potential new approaches to account for the impact of inflammation on biomarker interpretation and use; and 5) the presentation of new approaches to the study of these relations. Each WG was tasked with synthesizing a summary of the evidence for each of these topics and delineating the remaining gaps in our knowledge. This review consists of a summary of the INSPIRE workshop and the WG deliberation