Candida auris in Greek healthcare facilities: Active surveillance results on first cases and outbreaks from eleven hospitals within Attica region.

BACKGROUND: Candida auris was sporadically detected in Greece until 2019. Thereupon, there has been an increase in isolations among inpatients of healthcare facilities. AIM: We aim to report active surveillance data on MALDI-TOF confirmed Candida auris cases and outbreaks, from November 2019 to September 2021. METHODS: A retrospective study on hospital-based Candida auris data, over a 23-month period was conducted, involving 11 hospitals within Attica region. Antifungal susceptibility testing and genotyping were conducted. Case mortality and fatality rates were calculated and p-values less than 0.05 were considered statistically significant. Infection control measures were enforced and enhanced. RESULTS: Twenty cases with invasive infection and 25 colonized were identified (median age: 72 years), all admitted to hospitals for reasons other than fungal infections. Median hospitalisation time until diagnosis was 26 days. Common risk factors among cases were the presence of indwelling devices (91.1 %), concurrent bacterial infections during hospitalisation (60.0 %), multiple antimicrobial drug treatment courses prior to hospitalisation (57.8 %), and admission in the ICU (44.4 %). Overall mortality rate was 53 %, after a median of 41.5 hospitalisation days. Resistance to fluconazole and amphotericin B was identified in 100 % and 3 % of tested clinical isolates, respectively. All isolates belonged to South Asian clade I. Outbreaks were identified in six hospitals, while remaining hospitals detected sporadic C. auris cases. CONCLUSION: Candida auris has proven its ability to rapidly spread and persist among inpatients and environment of healthcare facilities. Surveillance focused on the presence of risk factors and local epidemiology, and implementation of strict infection control measures remain the most useful interventions.

Machine Learning for Antibiotic Resistance Prediction: A Prototype Using Off-the-Shelf Techniques and Entry-Level Data to Guide Empiric Antimicrobial Therapy.

OBJECTIVE: In the era of increasing antimicrobial resistance, the need for early identification and prompt treatment of multi-drug-resistant infections is crucial for achieving favorable outcomes in critically ill patients. As traditional microbiological susceptibility testing requires at least 24 hours, automated machine learning (AutoML) techniques could be used as clinical decision support tools to predict antimicrobial resistance and select appropriate empirical antibiotic treatment. METHODS: An antimicrobial susceptibility dataset of 11,496 instances from 499 patients admitted to the internal medicine wards of a public hospital in Greece was processed by using Microsoft Azure AutoML to evaluate antibiotic susceptibility predictions using patients' simple demographic characteristics, as well as previous antibiotic susceptibility testing, without any concomitant clinical data. Furthermore, the balanced dataset was also processed using the same procedure. The datasets contained the attributes of sex, age, sample type, Gram stain, 44 antimicrobial substances, and the antibiotic susceptibility results. RESULTS: The stack ensemble technique achieved the best results in the original and balanced dataset with an area under the curve-weighted metric of 0.822 and 0.850, respectively. CONCLUSIONS: Implementation of AutoML for antimicrobial susceptibility data can provide clinicians useful information regarding possible antibiotic resistance and aid them in selecting appropriate empirical antibiotic therapy by taking into consideration the local antimicrobial resistance ecosystem.

Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains.

Hospital-acquired infections, particularly in ICU, are becoming more frequent in recent years, with the most serious of them being Gram-negative bacterial infections. Among them, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa are considered the most resistant bacteria encountered in ICU and other wards. Given the fact that about 24 hours are usually required to perform common antibiotic resistance tests after the bacteria identification, the use of machine learning techniques could be an additional decision support tool in selecting empirical antibiotic treatment based on the sample type, bacteria, and patient's basic characteristics. In this article, five machine learning (ML) models were evaluated to predict antimicrobial resistance of Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. We suggest implementing ML techniques to forecast antibiotic resistance using data from the clinical microbiology laboratory, available in the Laboratory Information System (LIS).

Bacteriology, antibiotic resistance and risk stratification of patients with culture-positive, community-acquired pleural infection.

BACKGROUND: Community-acquired pleural infection (CAPI) is a growing health problem worldwide. Although most CAPI patients recover with antibiotics and pleural drainage, 20% require surgical intervention. The use of inappropriate antibiotics is a common cause of treatment failure. Awareness of the common causative bacteria along with their patterns of antibiotic resistance is critical in the selection of antibiotics in CAPI-patients. This study aimed to define CAPI bacteriology from the positive pleural fluid cultures, determine effective antibiotic regimens and investigate for associations between clinical features and risk for death or antibiotic-resistance, in order to advocate with more invasive techniques in the optimal timing. METHODS: We examined 158 patients with culture positive, CAPI collected both retrospectively (2012-2013) and prospectively (2014-2018). Culture-positive, CAPI patients hospitalized in six tertiary hospitals in Greece were prospectively recruited (N=113). Bacteriological data from retrospectively detected patients were also used (N=45). Logistic regression analysis was performed to identify clinical features related to mortality, presence of certain bacteria and antibiotic resistance. RESULTS: Streptococci, especially the non-pneumococcal ones, were the most common bacteria among the isolates, which were mostly sensitive to commonly used antibiotic combinations. RAPID score (i.e., clinical score for the stratification of mortality risk in patients with pleural infection; parameters: renal, age, purulence, infection source, and dietary factors), diabetes and CRP were independent predictors of mortality while several patient co-morbidities (e.g., diabetes, malignancy, chronic renal failure, etc.) were related to the presence of certain bacteria or antibiotic resistance. CONCLUSIONS: The dominance of streptococci among pleural fluid isolates from culture-positive, CAPI patients was demonstrated. Common antibiotic regimens were found highly effective in CAPI treatment. The predictive strength of RAPID score for CAPI mortality was confirmed while additional risk factors for mortality and antibiotic resistance were detected.

Using Machine Learning Algorithms to Predict Antimicrobial Resistance and Assist Empirical Treatment.

Multi-drug-resistant (MDR) infections and their devastating consequences constitute a global problem and a constant threat to public health with immense costs for their treatment. Early identification of the pathogen and its antibiotic resistance profile is crucial for a favorable outcome. Given the fact that more than 24 hours are usually required to perform common antibiotic resistance tests after the sample collection, the implementation of machine learning methods could be of significant help in selecting empirical antibiotic treatment based only on the sample type, Gram stain, and patient's basic characteristics. In this paper, five machine learning (ML) algorithms have been tested to determine antibiotic susceptibility predictions using simple demographic data of the patients, as well as culture results and antibiotic susceptibility tests. Implementing ML algorithms to antimicrobial susceptibility data may offer insightful antibiotic susceptibility predictions to assist clinicians in decision-making regarding empirical treatment.

Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece.

Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample's Gram stain, site of infection, and patient demographics. In our single center study, we compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient's clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden.

Using Microbiological Data Analysis to Tackle Antibiotic Resistance of Klebsiella Pneumoniae.

Optimal antibiotic use for the treatment of nosocomial infections plays a central role in the effort to control the rapidly increasing prevalence of multidrug-resistant bacteria. Antibiotic selection should be based on accurate knowledge of local susceptibility rates. Traditional methods of resistance reporting, which are in routine use by microbiology laboratories could be enhanced by using statistically significant results. We present a method of reporting based on antibiotic susceptibility data analysis which offers an accurate tool that reduces clinician uncertainty and enables optimization of the antibiotic selection process.

A 2-Year Single-Centre Audit on Antibiotic Resistance of Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae Strains from an Intensive Care Unit and Other Wards in a General Public Hospital in Greece.

Hospital-acquired infections, particularly in the critical care setting, are becoming increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality, with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. As treatment options become limited, antimicrobial stewardship programs aim to optimize the appropriate use of currently available antimicrobial agents and decrease hospital costs. Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae are the most common resistant bacteria encountered in intensive care units (ICUs) and other wards. To establish preventive measures, it is important to know the prevalence of Gram-negative isolated bacteria and antibiotic resistance profiles in each ward separately, compared with ICUs. In our single centre study, we compared the resistance levels per antibiotic of P. aeruginosa, A. baumannii and K.pneumoniae clinical strains between the ICU and other facilities during a 2-year period in one of the largest public tertiary hospitals in Greece. The analysis revealed a statistically significant higher antibiotic resistance of the three bacteria in the ICU isolates compared with those from other wards. ICU strains of P. aeruginosa presented the highest resistance rates to gentamycin (57.97%) and cefepime (56.67%), followed by fluoroquinolones (55.11%) and carbapenems (55.02%), while a sensitivity rate of 97.41% was reported to colistin. A high resistance rate of over 80% of A. baumannii isolates to most classes of antibiotics was identified in both the ICU environment and regular wards, with the lowest resistance rates reported to colistin (53.37% in ICU versus an average value of 31.40% in the wards). Statistically significant higher levels of resistance to most antibiotics were noted in ICU isolates of K. pneumoniae compared with non-ICU isolates, with the highest difference-up to 48.86%-reported to carbapenems. The maximum overall antibiotic resistance in our ICU was reported for Acinetobacter spp. (93.00%), followed by Klebsiella spp. (72.30%) and Pseudomonas spp. (49.03%).

Point-prevalence survey of healthcare facility-onset healthcare-associated Clostridium difficile infection in Greek hospitals outside the intensive care unit: The C. DEFINE study.

BACKGROUND: The correlation of Clostridium difficile infection (CDI) with in-hospital morbidity is important in hospital settings where broad-spectrum antimicrobial agents are routinely used, such as in Greece. The C. DEFINE study aimed to assess point-prevalence of CDI in Greece during two study periods in 2013. METHODS: There were two study periods consisting of a single day in March and another in October 2013. Stool samples from all patients hospitalized outside the ICU aged ≥18 years old with diarrhea on each day in 21 and 25 hospitals, respectively, were tested for CDI. Samples were tested for the presence of glutamate dehydrogenase antigen (GDH) and toxins A/B of C. difficile; samples positive for GDH and negative for toxins were further tested by culture and PCR for the presence of toxin genes. An analysis was performed to identify potential risk factors for CDI among patients with diarrhea. RESULTS: 5,536 and 6,523 patients were screened during the first and second study periods, respectively. The respective point-prevalence of CDI in all patients was 5.6 and 3.9 per 10,000 patient bed-days whereas the proportion of CDI among patients with diarrhea was 17% and 14.3%. Logistic regression analysis revealed that solid tumor malignancy [odds ratio (OR) 2.69, 95% confidence interval (CI): 1.18-6.15, p = 0.019] and antimicrobial administration (OR 3.61, 95% CI: 1.03-12.76, p = 0.045) were independent risk factors for CDI development. Charlson's Comorbidity Index (CCI) >6 was also found as a risk factor of marginal statistical significance (OR 2.24, 95% CI: 0.98-5.10). Median time to CDI from hospital admission was shorter with the presence of solid tumor malignancy (3 vs 5 days; p = 0.002) and of CCI >6 (4 vs 6 days, p = 0.009). CONCLUSIONS: The point-prevalence of CDI in Greek hospitals was consistent among cases of diarrhea over a 6-month period. Major risk factors were antimicrobial use, solid tumor malignancy and a CCI score >6.

Rare Case of Strongyloides stercoralis Hyperinfection in a Greek Patient with Chronic Eosinophilia.

Strongyloidiasis is a disease characterized by a diverse spectrum of unspecific manifestations that complicate its diagnosis. Although, the course of its chronic form is usually benign, in cases of immunosuppression, iatrogenic or not, it can evolve to a hyperifection syndrome with even fatal complications. Herein, we report a case of Strongyloides stercoralis hyperinfection in a Greek patient receiving corticosteroid treatment for chronic eosinophilia and angioedema. The case represents an extremely rare case of autochthonous strongyloidiasis in Greece and underlines the importance of the early diagnosis of the disease's uncomplicated forms in order to prevent its severe sequelae.