Insulin Pump Therapy Efficacy and Key Factors Influencing Adherence in Pediatric Population-A Narrative Review.

Objective: we aimed to highlight the state of the art in terms of pediatric population adherence to insulin pumps. This study intends to underline the significance of identifying and minimizing, to the greatest extent feasible, the factors that adversely affect the juvenile population's adherence to insulin pump therapy. Materials and methods: articles from PubMed, Embase, and Science Direct databases were evaluated using the following search terms: adherence, pump insulin therapy, children, pediatric population, and type 1 diabetes, in combination with several synonyms such as compliance, treatment adherence, pump adherence, patient dropouts, and treatment refusal. Results: A better glycemic control is connected to a better adherence to diabetes management. We identify, enumerate, and discuss a number of variables which make it difficult to follow an insulin pump therapy regimen. Several key factors might improve adherence to insulin pump therapy: efficient communication between care provider and patients (including home-based video-visits), continuous diabetes education, family support and parental involvement, as well as informational, practical assistance, and emotional support from the society. Conclusions: every cause and obstacle that prevents young patients from adhering to insulin pumps optimally is an opportunity for intervention to improve glycemic control and, as a result, their quality of life.

Exploring Current Evidence on the Past, the Present, and the Future of the Heart Team: A Narrative Review.

INTRODUCTION: Including healthcare professionals dealing with cardiovascular diseases, Heart Team is a concept/structure designed for selecting diagnostic strategies, facilitating therapeutic decisions, and improving cardiovascular outcomes in patients with complex heart pathologies, requiring input from different subspecialties and the necessity of a multidisciplinary approach. The aim of this narrative review is to search for and to summarize current evidence regarding Heart Team and to underline the future directions for the development of this concept. METHODS: We searched the electronic database of PubMed, SCOPUS, and Cochrane CENTRAL for studies including Heart Team. Forty-eight studies were included, if reference was made to Heart Team structure and functionality. RESULTS: We depicted the structure and the timeline of Heart Team, along with actual evidence-based recommendations from European Guidelines. We underlined the importance of quality of knowledge-sharing and decision-making inside the Team, analyzing bad decisions which did not reflect members' true beliefs due to "uniformity pressure, closed mindedness, and illusion of invulnerability." The observation that Guidelines' indications regarding Heart Team carry a level C indication underlines the very future of this Team: randomized controlled trials proving solid benefits in an evidence-based world. CONCLUSIONS: Envisioned as a tool for optimizing the management of various complex cardiovascular pathologies, Heart Team should simplify and facilitate the activity in the cardiovascular ward. Finally, these facts should be translated into better cardiovascular outcomes and a lower psychological distress among Team participants. Despite all future changes, there must always be a constant part: the patient should remain at the very center of the Team.

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.

BACKGROUND: The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? METHODS: We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. RESULTS: AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. CONCLUSIONS: Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.