Current and emerging drug treatment strategies to tackle invasive community-associated methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) infection: what are the challenges?

Community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) infections represent a leading cause of purulent skin and soft tissue infections in some geographical regions. Traditionally, ‘old antibiotics’ such as trimethoprim-sulfamethoxazole, tetracyclines, clindamycin, chloramphenicol,vancomycin, and teicoplanin have been used to treat these infections, but these were often associated with low efficacy and excessive side effects and toxicity, especially nephrotoxicity. Along with the development of new compounds, the last decade has seen substantial improvements in the management of CA-MRSA infections. In this review, the authors discuss the current and emerging drug treatment strategies to tackle invasive CA-MRSA infections. Articles reported in this review were selected from through literature searches using the PubMed database. The availability of new drugs showing a potent in vitro activity against CA-MRSA represents a unique opportunity to face the threat of resistance while potentially reducing toxicity. All these compounds represent promising options to enhance our antibiotic armamentarium. However, data regarding the use of these new drugs in real-life studies are limited and their best placement in therapy and in terms of optimization of medical resources and balance of cost-effectiveness requires further investigation.</p

Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project

Candidemia is associated with a heavy burden of morbidity and mortality in hospitalized patients. The availability of blood culture results could require up to 48–72 h after blood draw; thus, early treatment decisions are made in the absence of a definite diagnosis. In this retrospective study, we assessed the performance of different supervised machine learning algorithms for the early differential diagnosis of candidemia and bacteremia in adult patients on a large dataset automatically extracted within the AUTO-CAND project. Overall, 12,483 episodes of candidemia (1275; 10%) or bacteremia (11,208; 90%) were included in the analysis. A random forest classifier achieved the best diagnostic performance for candidemia, with sensitivity 0.98 and specificity 0.65 on the training set (true skill statistic [TSS] = 0.63) and sensitivity 0.74 and specificity 0.57 on the test set (TSS = 0.31). Then, the random classifier was trained in the subgroup of patients with available serum β-D-glucan (BDG) and procalcitonin (PCT) values by exploiting the feature ranking learned in the entire dataset. Although no statistically significant differences were observed from the performance measures obtained by employing BDG and PCT alone, the performance measures of the classifier that included the features selected in the entire dataset, plus BDG and PCT, were the highest in most cases. Random forest classifiers trained on large datasets of automatically extracted data have the potential to improve current diagnostic algorithms for candidemia. However, further development through implementation of automatically extracted clinical features may be necessary to achieve crucial improvements.</p