Open versus Transcutaneous (Ultrasound-Guided and Based on Anatomic Landmarks) Tunneled Venous Access to the Right Internal Jugular Vein in Children: A Prospective Single-Center Study.

BACKGROUND: The purpose of this study was to compare the immediate and long-term complications that are associated with the utilized techniques for the insertion of indwelling central venous catheters, that is the open surgical technique, the ultrasound-guided technique, and the transcutaneous technique based on external anatomical landmarks in the right internal jugular vein, to a pediatric population. METHODS: This was a prospective randomized trial based on a pediatric patient population under 16 years of age of a tertiary pediatric-oncological hospital. The procedure was performed by a medical team with varying experience regarding the percutaneous and open insertion methods. We studied the outcome of our procedure, based on the immediate and delayed complication rate, as well as the needed time in order to complete the procedure and mean duration of line use. RESULTS: The patients that were inserted in our protocol were divided into three subgroups based on the selected technique for the insertion of the central venous catheter. A total number of 88 insertions (25.4%) (out of 346) were based on the technique that was using external anatomical landmarks, 121 insertions were based on the ultrasound-guided transcutaneous technique (34.9%), whereas in 137 cases (39.5%) the open surgical technique was preferred. All cases that were related to catheter re-insertion were excluded from our study. We performed a statistical analysis regarding the catheter dwell time between the three subgroups of patients and no significant difference was recorded. Moreover, the development of thrombosis was investigated, and we noted that a higher percentage of this complication was related to the transcutaneous external landmark and open surgical technique. Also, the incidence of infection was taken into consideration, which manifested an increased incidence when the transcutaneous technique based on external landmarks was used. CONCLUSIONS: Ultrasound-guided percutaneous insertion was considered to be a safe and effective technique for the insertion of central venous catheters. Our study also demonstrated a decrease in operating times when performed by operators with increasing expertise, increased preservation of the diameter of the venous lumen, and no increase in complication rates when the ultrasound-guided technique was selected.

Automated ML Techniques for Predicting COVID-19 Mortality in the ICU.

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.

Prediction of COVID-19 Mortality in the Intensive Care Unit Using Machine Learning.

Since its emergence, the COVID-19 pandemic still poses a major global health threat. In this setting, a number of useful machine learning applications have been explored to assist clinical decision-making, predict the severity of disease and admission to the intensive care unit, and also to estimate future demand for hospital beds, equipment, and staff. The present study examined demographic data, hematological and biochemical markers routinely measured in Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, in relation to the ICU outcome, during the second and third Covid-19 waves, from October 2020 until February 2022. In this dataset, we applied eight well-known classifiers of the caret package for machine learning of the R programming language, to evaluate their performance in forecasting ICU mortality. The best performance regarding area under the receiver operating characteristic curve (AUC-ROC) was observed with Random Forest (0.82), while k-nearest neighbors (k-NN) were the lowest performing machine learning algorithm (AUC-ROC: 0.59). However, in terms of sensitivity, XGB outperformed the other classifiers (max Sens: 0.7). The six most important predictors of mortality in the Random Forest model were serum urea, age, hemoglobin, C-reactive protein, platelets, and lymphocyte count.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review.

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.

Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans.

Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients. The aim of the present study was to evaluate the performance of an automated machine learning algorithm in the diagnosis of Covid-19 pneumonia using chest CT scans. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. The method's average precision was 0.932. We suggest that auto-ML platforms help users with limited ML expertise train image recognition models by only uploading the examined dataset and performing some basic settings. Such methods could deliver significant potential benefits for patients in the future by allowing for earlier disease detection and care.

Evaluation of quality of life outcomes following palliative radiotherapy in bone metastases: A literature review.

PURPOSE: To assess the quality of life (QoL) following palliative radiotherapy (RT) in patients with painful bone metastases. METHODS: A literature search limited to English-written publications was carried out, through the Cochrane Central Register of Controlled Trials (November 2018), OvidSP and PubMedCentral (1940-November 2018) databases. Subject headings and keywords included "quality of life"(QoL), "bone metastases", "palliative therapy", "pain" and "radiotherapy". Original articles, literature reviews, trials and meta-analyses revealing alterations in QoL post-RT using ratified measuring tools were examined. Studies referring to other types of metastases (e.g. brain metastases), or to other types of palliative therapy (e.g. the use of bisphosphonates alone), or focusing only on pain, or even reporting QoL only before or only after the use of RT were excluded. RESULTS: Twenty four articles were selected from a total of 1360 articles. Seven trials proceeded to patients' randomization. The most commonly used tool to evaluate QoL was EORTC, followed by Brief Pain Inventory (BPI) and Edmonton Symptom Assessment System (ESAS) questionnaires. All studies showed improvement in symptoms and functional interference scores after RT. The QoL between responders (Rs) and non-responders (NRs) has been juxtaposed in 10 studies. Rs had a significant benefit in QoL in comparison with the NRs. DISCUSSION: Palliative radiotherapy in painful bone metastases improves Rs' QoL.