ANTIBIOGRAM STUDY AND ANTIBIOTIC USE EVALUATION USING GYSSEN METHOD IN PATIENTS WITH DIABETIC FOOT

Foot infection is a common and serious problem in people with diabetes, which require proper management (diagnostic and therapeutic approaches) that can be cured. Empiric antibiotic regimen should be based on clinical data and bacteria pattern that are available, but definitive therapy should be based on the results of the infected tissue culture. The selection of initial antibiotic therapy was difficult and unwise use can lead to antibiotic-resistant. Evaluation is needed for using antibiotics to benefit wisely. The aim of this research is to analyzed the pattern of bacteria in diabetic foot and to its sensitivity test to antibiotics, analyze empiric antibiotics that can be recommended, and analyzed the use of antibiotics by Gyssen method. Data was analyzed with observational studies (descriptive non-experimental), retrospectively and prospectively in patients diabetic foot infection that met inclusion criteria. Retrospective data are used to analyzed bacteria pattern and its sensitivity test, while prospective data are used to evaluated the use of antibiotics based on bacteria pattern, during the period of late March-early August 2015 at Mardi Waluyo Hospital. Evaluation was conducted by Gyssen method. The results, retrospective data samples obtained 30 infection bacteria during August 2014-March 2015. The prevalence of gram-negative bacteria as 53.33% with most types of bacteria E.coli and Klebsiella oxytoca (13.33%), and gram-positive bacteria as 46.67% with the highest bacteria are Staphylococcus spp. and Streptococcus spp. From the prospective data in inclusion criteria, 13 patients with the highest prevalence of gram-negative bacteria are Klebsiella oxytoca (28.57%), and most gram-positive Staphylococcus auerus (35.71%). While the qualitative analysis of antibiotic use was conducted on 50 types of antibiotics. The results of the qualitative analysis using Gyssens method obtained category as 62%, 2%, 14%, 2B category as 26%, 3A category as 10%, 4A category 52%, 4B category as 6%, 4C category as 8% and there are no use of antibiotics in the category V and VI. Conclusions, Gyessen method can show that the use of antibiotics in diabetic foot patients in Mardi Waluyo hospital is dominated by inaccuracy in choice of antibiotic, and inaccuracies in the interval antibiotics

Retrospective Observational Study on Microbial Contamination of Ulcerative Foot Lesions in Diabetic Patients

According to recent studies, there are almost 435 million people worldwide with diabetes mellitus. It is estimated that of these 148 million will develop Diabetic foot ulcers (DFUs) during their lifetime, of which 35 to 50% will be infected. In this scenario, the presence and frequency of pathogenic microorganisms and their level of susceptibility to the most frequent classes of antibiotics used to treat this pathological condition from patients with DFUs admitted to the outpatient clinic of vascular surgery of the Federico II University Hospital of Naples from January 2019 to March 2021 were investigated. Furthermore, the diabetic population characteristics under study (i.e., general, clinical, and comorbidities) and the pathogenic bacteria isolated from lesions were also considered. Bacterial strains poorly susceptible to antibiotics were more frequent in polymicrobial infections than in monomicrobial infections. β-Lactams showed the highest levels of resistance, followed by fluoroquinolones, aminoglycosides, and finally macrolides. The main findings of the study demonstrated that the occurrence of resistant microorganisms is the dominant factor in ulcer healing; thus it is essential to investigate the antibiotics’ susceptibility before setting antibiotic therapy to avoid inappropriate prescriptions that would affect the treatment and increase the development and spread of antibiotic resistanc

Diabetic Foot Infections:The Diagnostic Challenges

Diabetic foot infections (DFIs) are severe complications of long-standing diabetes, and they represent a diagnostic challenge, since the differentiation between osteomyelitis (OM), soft tissue infection (STI), and Charcot's osteoarthropathy is very difficult to achieve. Nevertheless, such differential diagnosis is mandatory in order to plan the most appropriate treatment for the patient. The isolation of the pathogen from bone or soft tissues is still the gold standard for diagnosis; however, it would be desirable to have a non-invasive test that is able to detect, localize, and evaluate the extent of the infection with high accuracy. A multidisciplinary approach is the key for the correct management of diabetic patients dealing with infective complications, but at the moment, no definite diagnostic flow charts still exist. This review aims at providing an overview on multimodality imaging for the diagnosis of DFI and to address evidence-based answers to the clinicians when they appeal to radiologists or nuclear medicine (NM) physicians for studying their patients

Biofilms in diabetic foot ulcers: impact, risk factors and control strategies

Diabetic foot ulcers (DFUs) are a serious complication from diabetes mellitus, with a huge economic, social and psychological impact on the patients life. One of the main reasons why DFUs are so difficult to heal is related to the presence of biofilms. Biofilms promote wound inflammation and a remarkable lack of response to host defences/treatment options, which can lead to disease progression and chronicity. In fact, appropriate treatment for the elimination of these microbial communities can prevent the disease evolution and, in some cases, even avoid more serious outcomes, such as amputation or death. However, the detection of biofilm-associated DFUs is difficult due to the lack of methods for diagnostics in clinical settings. In this review, the current knowledge on the involvement of biofilms in DFUs is discussed, as well as how the surrounding environment influences biofilm formation and regulation, along with its clinical implications. A special focus is also given to biofilm-associated DFU diagnosis and therapeutic strategies. An overview on promising alternative therapeutics is provided and an algorithm considering biofilm detection and treatment is proposed.This work was supported by: Base Funding—UIDB/00511/2020 of the Laboratory for Process Engineering, Environment, Biotechnology and Energy—LEPABE—funded by national funds through the FCT/MCTES (PIDDAC); Project Biocide_for_Biofilm-PTDC/BII-BTI/30219/2017- POCI-01-0145-FEDER-030219, ABFISH–PTDC/ASP-PES/28397/2017-POCI-01-0145-FEDER- 028397 and Germirrad-POCI-01-0247-FEDER-072237, funded by FEDER funds through COMPETE2020— Programa Operacional Competitividade e Internacionalização (POCI) and by national funds (PID DAC) through FCT/MCTES. The authors also thank the CITAB (Centre for the Research and Technol ogy of Agro-Environmental and Biological Sciences) under the scope of the FCT funds with reference UIDB/AGR/04033/2020. Ana Afonso (2020.04773.BD) and Diana Oliveira (SFRH/BD/138217/2018) acknowledge the FCT grants. Anabela Borges thanks the FCT for the financial support of their work contract through the Scientific Employment Stimulus—Individual Call—[CEECIND/01261/2017].info:eu-repo/semantics/publishedVersio

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients

Virulence characterization and antimicrobial resistance of major bacterial genera from diabetic foot infections

Tese de Doutoramento em Ciências Veterinárias na especialidade de Ciências Biológicas e BiomédicasDiabetes mellitus is a major chronic disease that continues to increase significantly. One of the most important and costly complications of diabetes is the development of foot ulcers, colonized by pathogenic and antimicrobial resistant bacteria, which may be responsible for impairing its successful treatment. Diabetic foot ulcer (DFU) bacterial communities can be organized in polymicrobial biofilms, which may be responsible for its chronicity. The ability of these communities to produce biofilm was evaluated and was higher when compared to biofilm formation by individual species. Staphylococcus aureus is one of the most prevalent species in diabetic foot infections (DFI). Staphylococci isolated from DFU in patients from the Lisbon area were identified, genotyped and screened for virulence and antimicrobial resistance traits. The isolates showed high genomic diversity, were resistant to important clinically antibiotics and expressed relevant virulence determinants. As biofilm formation is one of the most important virulence traits of S. aureus, the antimicrobial susceptibility patterns of biofilm-producing S. aureus strains were also analysed. The minimum biofilm inhibitory and eradication concentrations were determined for ten antimicrobial compounds. Staphylococci biofilms were resistant to antibiotic concentrations ten to thousand times higher than those effective for planktonic cells. Furthermore, the enterococci frequently isolated from DFI, were also identified and characterized, showing high antimicrobial resistance and important virulence traits. Since DFI are often caused by resistant bacteria, it is necessary to find alternatives to antibiotic therapy, such as phage therapy. The inhibitory potential of five bacteriophages, previously characterized, was evaluated against established biofilms formed by S. aureus, P. aeruginosa and A. baumannii. A significant cell reduction after phage exposure was observed, mainly after multiple treatments. DFI are very complex and studies on this topic are scarce. It is necessary to intensify research in order to develop more adequate therapeutic protocols for this type of infection.RESUMO - Caracterização da virulência e resistência a antimicrobianos dos principais géneros bacterianos envolvidos em infeções de pé diabético - Diabetes mellitus é uma doença crónica com grande impacto em saúde pública e cuja incidência continua a aumentar significativamente em todo o mundo, atingindo atualmente mais de 400 milhões de pessoas. Uma das complicações mais importantes da diabetes e associada a gastos económicos significativos são as úlceras de pé diabético. Uma vez que a camada protetora de pele é danificada, os tecidos profundos ficam expostos à infeção bacteriana, a qual pode evoluir rapidamente. As infeções das úlceras de pé diabético são a causa mais comum de internamento hospitalar de pacientes diabéticos e uma importante causa de morbilidade, levando frequentemente à amputação dos membros inferiores. Estas infeções podem ser promovidas por bactérias potencialmente patogénicas e resistentes aos compostos antimicrobianos, prejudicando assim o sucesso do tratamento. As comunidades bacterianas presentes nas úlceras podem estar organizadas em biofilmes polimicrobianos, que contribuem para que as infeções se tornem crónicas e muito difíceis de resolver. Foi avaliada a capacidade de produção de biofilme por comunidades polimicrobianas de isolados bacterianos de pé diabético, utilizando um ensaio de microtitulação em placa com “Alamar Blue” (AB) e uma técnica de Hibridação In Situ Fluorescente Múltipla (MFISH). Esta avaliação foi realizada em três períodos de incubação distintos (24, 48 e 72 horas), depois da determinação da capacidade de formação de biofilme por 95 isolados de úlceras de pé diabético pertencentes a vários géneros bacterianos (Staphylococcus, Corynebacterium, Enterococcus, Pseudomonas e Acinetobacter). Todos os isolados apresentaram a capacidade de produzir biofilme às 24 horas, sendo que a quantidade de biofilme produzido aumentou com o tempo de incubação. Pseudomonas apresentou a capacidade mais elevada de produção de biofilme, seguida de Corynebacterium, Acinetobacter, Staphylococcus e por fim, Enterococcus. Foram encontradas diferenças estatisticamente significativas na capacidade de formação de biofilme entre os três períodos de incubação. As comunidades polimicrobianas produziram mais biofilme do que as espécies individualmente. As comunidades formadas por Pseudomonas + Enterococcus, Staphylococcus + Acinetobacter e Corynebacterium + Staphylococcus formaram mais biofilme do que as comunidades formadas por Enterococcus + Staphylococcus e por Enterococcus + Corynebacterium. O comportamento biológico das diferentes espécies bacterianas nos biofilmes polimicrobianos tem implicações clínicas muito importantes para o sucesso do tratamento deste tipo de infeções. A sinergia entre as bactérias presentes em biofilmes multiespécies foi descrita previamente, sendo que este trabalho representa o primeiro estudo sobre a evolução temporal da formação de biofilme por parte de comunidades polimicrobianas isoladas de úlceras de pé diabético, incluindo várias espécies. [...]Centro de Investigação Interdisciplinar em Sanidade Animal” (CIISA) of Faculty of Veterinary Medicine, University of Lisbon, PortugalN/

Development and validation of a prognostic model for stump healing in major lower limb amputation

Introduction Stump healing is essential in patients with a lower limb amputation in order for them to mobilize again. Little research has been being done on factors affecting stump healing. The aim of this paper is to explore the effect of haematological makers as well as patient characteristics on stump healing after patients have undergone an amputation procedure. In addition, a practical model regarding factors that affect stump healing was developed. Methods Patients who underwent a major lower limb amputation (above knee and below knee) at the Royal Infirmary of Edinburgh from the period of 2006 to 2009 were included in this study. A prognostic model utilizing backward stepwise logistical regression was developed to measure the probability of lower limb stump healing. The relationship between the dependent and independent variables was identified using univariate and multivariate logistic regression. Hosmer and Lemeshow goodness of fit test and Receiver Operating Curve (ROC) was used in order to measure the effectiveness of the model. The model was validated with the prospective data of 100 patients that had undergone major lower limb amputation from the year 2010 and 2011 in Royal Infirmary of Edinburgh prospectively. Results In this study healing of the stump as defined was achieved in sixty three percent (63%) of patients. Univariate analysis found seven variables to be associated with lower limb stump healing (type of amputation, gender, hypertension, smoking, serum sodium, serum creatinine and serum High Density Lipid cholesterol (HDL)). A further four variables (age, diabetes xxv mellitus, white cell count and Prothrombin Time) were added to the model secondary to their strong clinical association with the stump healing. Three variables, namely serum sodium, serum creatinine and serum High Density Lipid cholesterol were identified which influenced stump healing. Patients with normal serum sodium were 75% more likely to have lower limb stump healing compared to that of patients with abnormal serum sodium (odds ratio [OR] 1.756; 95% confidence interval [CI] 1.048-2.942). Patients with normal serum creatinine were 66% more likely to have their stump healed (OR 1.664; 95% CI 0.94 to 2.946). The healing rate of patients with a normal level of serum High Density Lipid cholesterol was 75%, in contrast to patients with an aberrant level of serum High Density Lipids cholesterol (OR 1.753; 95% CI 1.061 to 2.895). The effectiveness of the retrospective stump-healing model was demonstrated by the area under the Receiver Operator Curve (0.612), which was supported by the Hosmer and Lemeshow goodness-of-fit test (p=0.879). In the prospective study, the model's discriminatory power was verified by the area under the Receiver Operator Curve (0.584) and Hosmer and Lemeshow goodness-of-fit test (p>0.05). Conclusion Serum sodium, serum High Density Lipid cholesterol and serum creatinine have a strong correlation with lower limb stump healing. However, serum sodium and serum High Density Lipid cholesterol secondary to multiple co-morbidities in this cohort group could be altered secondary to disease pathology itself. Further clinical research is necessary to evaluate the association of the risk factors with lower limb stump healing.sub_podsubmitted2845_ethesessubmitte