Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician’s point of view.

The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17-month period, relative to the outcome, in order to build a prognostic model. We used the Google Vertex AI platform, on the one hand, to evaluate its performance in predicting ICU mortality, and on the other hand to show the ease with which even non-experts can make prognostic models. The model’s performance regarding the area under the receiver operating characteristic curve (AUC-ROC) was 0.955. The six highest-ranked predictors of mortality in the prognostic model were age, serum urea, platelets, C-reactive protein, hemoglobin, and SGOT.

Materials and Methods: Questionnaires were distributed via email to all anesthesiologists, registered in the HSA network. Participants had to provide information concerning their age, years of practice, whether they work in the public or private domain, in a secondary or tertiary care facility. They were also requested to declare their commitment to the use of protocols along with their opinion on its functionality, as well as accompanying drawbacks and advantages. Materials and Methods:Questionnaires were distributed via email to all anesthesiologists, registered in the HSA network. Participants had to provide information concerning their age, years of practice, whether they work in the public or private domain, in a secondary or tertiary care facility. They were also requested to declare their commitment to the use of protocols along with their opinion on its functionality, as well as accompanying drawbacks and advantages.Conclusions: Even though our research's sample size is not broad enough to portray the implementation of patient safety anesthesia protocols in Greek hospitals in full extent and detail, one can tell, by inference, that it has noted a rudimental emergence since the issue of the Helsinki Declaration. Nevertheless, progress still remains to be made in terms of compliance to protocols in a routine basis. As is more, this subject ought to be better addressed in the education of the forthcoming generations of anesthetists.

Central Venous Catheters insertion is a very common procedure performed at the operation room and the Intensive Care Unit. In the paediatric population they are frequently used to administer fluids, blood products, resuscitation drugs, parenteral nutrition and chemotherapy. One of the reported complications, though a less commonly described one, is the inappropriate position of the tip of the CVC in a vessel other than the superior vena cava. In this study, we initially present the anatomy of the superior vena cava system and that of the internal jugular vein, the optimal catheter tip position as well as the possible suboptimal catheter tip locations. Subsequently, paediatric chest X-rays of our hospital with catheter tip malpositioning are illustrated, after internal jugular vein catheterization. Following, we discuss possible mechanisms of central venous catheters malpositioning, signs and symptoms which could help us identify a wrong placement and also how to prevent as well as how to fix one. Finally, an interrelation between malpositioning, malfunction and the existence of infection or thrombosis is investigated. Our study concluded that the right internal jugular vein should be the first choice in all cases of vessel implantation, mainly based on our statistical analysis results, which suggested that this vessel was associated with the least possibility of erroneous catheter placement. Another important clue of our study is based on the fact that the inappropriate positioning of a central venous catheter over the long term could be a significant predisposing factor of malfunction, along with infection and thrombosis.