Abstract Background Vascular graft infection (VGI) remains a severe disease with high mortality and relapse rates. We performed a retrospective single-center cohort study to highlight factors associated with long-term all-cause mortality in patients with vascular graft infection. Methods All patients hospitalized in our facility over 10 years for VGI were included. VGI was defined by the presence of a vascular graft or an aortic stent graft (stent or fabric), associated with 2 criteria among clinical, biological, imaging, or microbiological elements in favor of VGI. The primary outcome was all-cause mortality. Empirical antibiotic therapy was considered as appropriate when all involved pathogens were susceptible in vitro to the antibiotics used. The surgical strategy was defined as nonoptimal when the graft was not removed in a late-onset surgery (>3 months) or no surgery was performed. Results One hundred forty-six patients were included. Empirical antibiotic therapy was administered in 98 (67%) patients and considered appropriate in 55 (56%) patients. Surgery was performed in 136 patients (96%) and considered as optimal in 106 (73%) patients. In multivariable analysis, appropriate empirical antibiotic therapy was associated with a lower probability of mortality (hazard ratio, 0.47 [95% confidence interval, .30–.79]; P = .002). Long-term survival did not differ according to whether the surgical strategy was considered optimal or not (log-rank = 0.66). Conclusions Appropriate empirical antibiotic therapy is a cornerstone of the management of VGI. Whenever possible, antibiotics must be associated with optimal surgical management. However, surgery could potentially be avoided in comorbid patients who are treated with appropriate antibiotics.

/pdf/long-term-prognosis-following-vascular-graft-infection-a-10-1sd46evg.pdf

Long-term Prognosis Following Vascular Graft Infection: A 10-Year Cohort Study

Acute kidney injury (AKI) has serious short- and long-term consequences. The objective of the present study of a cohort of hospitalized patients with AKI was to (i) evaluate the proportion of patients with hospital-acquired (HA) AKI and community-acquired (CA) AKI, the characteristics of these patients and the AKIs, and the short-term outcomes, and (ii) determine the performance of several ICD-10 codes for identifying AKI (both CA and HA) and drug-induced AKI. A cohort of hospitalized patients with AKI was constituted by screening hospital’s electronic medical records (EMRs) for cases of AKI. We distinguished between and compared CA–AKI and HA–AKI and evaluated the proportion of AKIs that were drug-induced. The EMR data were merged with hospital billing codes (according to the International Classification of Diseases, 10th Edition (ICD-10)) for each hospital stay. The ability of ICD-10 codes to identify AKIs (depending on the type of injury) was determined by calculating the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Lastly, we sought to validate specific ICD-10 codes for drug-induced AKI. Of the 2473 patients included, 1557 experienced an AKI (HA–AKI: 59.3%; CA–AKI: 40.7%). Patients with CA–AKI had a better short-term outcome and a lower death rate (7.6%, vs. 20% for HA–AKI). One AKI in three was drug-induced. The combination of AKI codes had a very high specificity (94.8%), a high PPV (94.9%), a moderate NPV (56.7%) and moderate sensitivity (57.4%). The sensitivity was higher for CA–AKI (72.2%, vs. 47.2% for HA–AKI), for more severe AKI (82.8% for grade 3 AKI vs. 43.7% for grade 1 AKI), and for patients with CKD. Use of a specific ICD-10 code for drug-induced AKI (N14x) alone gave a very low sensitivity (1.8%), whereas combining codes for adverse drug reactions with AKI-specific codes increased the sensitivity. Our results show that the combination of an EMR-based analysis with ICD-10-based hospital billing codes gives a comprehensive “real-life” picture of AKI in hospital settings. We expect that this approach will enable researchers to study AKI in more depth.

Use of a hospital administrative database to identify and characterize community-acquired, hospital-acquired and drug-induced acute kidney injury.

Purpose Severe bacterial infections are important causes of hospitalization and loss of health worldwide. In this study we aim to characterize the total burden, recurrence and severity of bacterial infections in the general population during a 22-year period. Methods We investigated hospitalizations due to bacterial infection from eight different foci in the prospective population-based Trøndelag Health Study (the HUNT Study), where all inhabitants aged ≥ 20 in a Norwegian county were invited to participate. Enrollment was between 1995 and 1997, and between 2006 and 2008, and follow-up ended in February 2017. All hospitalizations, positive blood cultures, emigrations and deaths in the follow-up period were captured through registry linkage. Results A total of 79,393 (69.5% and 54.1% of the invited population) people were included, of which 42,237 (53%) were women and mean age was 48.5 years. There were 37,298 hospitalizations due to infection, affecting 15,496 (22% of all included) individuals. The median time of follow-up was 20 years (25th percentile 9.5–75th percentile 20.8). Pneumonia and urinary tract infections were the two dominating foci with incidence rates of 639 and 550 per 100,000 per year, respectively, and with increasing incidence with age. The proportion of recurring admissions ranged from 10.0% (central nervous system) to 30.0% (pneumonia), whilst the proportion with a positive blood culture ranged from 4.7% (skin- and soft tissue infection) to 40.9% (central nervous system). The 30-day mortality varied between 3.2% (skin- and soft tissue infection) and 20.8% (endocarditis). Conclusions In this population-based cohort, we observed a great variation in the incidence, positive blood culture rate, recurrence and mortality between common infectious diseases. These results may help guide policy to reduce the infectious disease burden in the population.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0271263&type=printable

Incidence, recurring admissions and mortality of severe bacterial infections and sepsis over a 22-year period in the population-based HUNT study

Aminoguanidines have been of interest in the last 50 years because of their biological and chemical properties. Derivatives of the aminoguanidines hydrazones represent a class of compounds which contain an amidine group (guanyl), attached to a hydrazone moiety. Moreover, present biological activities such as anti-diabetic, antimalarial, antileishmanial, antifungal, antibacterial and anti-HIV. Thus, the aim of this study is to evaluate the derivatives aminoguanidine hydrazones with potential antimicrobial activity and cell migration in vitro. It is an in vitro experimental research with quantitative and qualitative approach. Eighteen hydrazone aminoguanidine derivatives were tested and evaluated for antimicrobial activity against Gram positive and Gram negative bacteria. Cell viability was evaluated by Methyltetrazolium colorimetric method with cell lines of 3T3 fibroblasts at concentrations of 10 and 100 μM and test in vitro cell migration by the scratch assay technique used concentration of 10 μM. The antimicrobial activity of LQM 131 showed a MIC of 12,5 μg/mL for Shigella flexneri, and for LQM 199 the inhibition was at concentrations of 5 to 12,5 μg/mL against strains Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli, Pseudomonas aeruginosa, Enterobacter cloacae and Shigella flexneri. In relation to cell viability assay, it was observed that in the 10 μM concentration all the substances showed viability higher than 80%, however, in the highest concentration of 100 μM only four substances (LQM 96, 97, 101 and 131) remained viable with a percentage above 80%, the others presented relevant toxicity to the fibroblast lineages. In the evaluation of cellular migration, it was observed that LQM substances 101, 131, 134 and 195 presented a similar form when compared to positive control (55.17%), with percentages of migration of 55,01%, 56,98%, 58,13% and 57,95%, respectively, and LQM 132 showed decreased fibroblast migration with a percentage of 23,31%. However, it was concluded that the aminoguanidines hydrazones showed antimicrobial activity against the tested strains, being LQM substances 106, 131, 198 and 199 with inhibition against the six bacteria tested. The substances maintained cell viability greater than 80% at the 10 μM concentration and at the concentration of 100 μM only four molecules maintained viability greater than 80%. No cellular migration of 3T3 fibroblasts was observed when treated with the aminoguanidines hydrazones.

/pdf/avaliacao-de-derivados-aminoguanidinas-hidrazonas-com-3jp0404096.pdf

Avaliação de derivados aminoguanidinas hidrazonas com potencial atividade antimicrobiana e migração celular in vitro

Triaging is the categorization of patients attending the emergency departments (ED) into categories based on the patient's condition at arrival. In Australia, triage is a manual process, not guaranteed consistent and potentially error-prone. The problem with the manual process is that assigning incorrect triage categories to patients can result in delay of treatment for some patients. With the establishment of the Australian national health record system (MyHealth) and clinical data sharing standards such as HL7, it is possible to use patient history information as well as data about the patients' conditions at arrival in ED to quickly and accurately assign a triage category. The wide availability and application of machine learning (ML) methods, including medical applications using such methods, make these methods a possible solution to this problem. Before implementation of ML algorithms in triage, it is essential to understand the multiple dimensions of potential outcomes of health-services, including changes of clinical behaviors and workflows, "social-economical-technical", and ethical and legal debates. This research uses Context-Content-Process (CCP), SWOT and "Khoja--Durrani--Scott" (KDS) frameworks to provide an initial review of a technical "roadmap" of the classification of patients attending emergency departments (ED) using different stages of Naive Bayes (NB) and Neural Network (NN) machine learning (ML) methods. This is the first research looking at the potential to use ML methods to assist in triage of patients in the Australia context considering outcomes. This research could be used to evaluate automation of the triage process or to support the manual process. The research results suggest that it is necessary to understand these multiple outcomes before future implementations are actually conducted.

Technology Road Mapping of Two Machine Learning Methods for Triaging Emergency Department Patients in Australia

Objective A preliminary analysis of data consistency on different types of bacterial resistance by infection site and causative agents was conducted using the French hospital discharge database (French acronym PMSI) to assess the use of the database in a national cartography tool. Material and methods Hospital stays in medical, surgical, and obstetrical units were extracted from the 2014 PMSI database using the ICD-10 diagnosis codes. Bacterial infections, causative agents, and resistance corresponding to these stays were also identified. Results Data from 1258462 patients, corresponding to a total of 1617893 stays, was extracted. Among these stays, 46% were associated with a bacteria code and 7% with a resistance code. Lower respiratory tract infections were the most frequent infections (32% of stays; pneumonia in 95% of cases), followed by genitourinary infections (26%), intra-abdominal infections and diarrhoeas (24%), and skin and soft tissue infections (15%). Inconsistencies were observed between the types of infection and associated bacteria and between bacteria and associated resistance. These inconsistencies are likely due to initial coding errors. Conclusion The cartography of bacterial infections cannot be developed using the data of the current PMSI coding. These results underline the need to improve the coding of PMSI data for its use as a complementary tool of epidemiological surveillance of bacterial infections.

French hospital discharge database (PMSI) and bacterial resistance: Is coding adapted to hospital epidemiology?

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French hospital discharge database (PMSI) and bacterial resistance: Is coding adapted to hospital epidemiology?