Kidney diseases (Basel, Switzerland)
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Maintenance hemodialysis (MHD) patients are highly vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Medical staff of dialysis facilities without sufficient biosecurity protection are susceptible once exposed to asymptomatic coronavirus disease 2019 (COVID-19) patients. This study evaluated the epidemiological characteristics of COVID-19 in all MHD patients and medical staff of dialysis facilities in Wuhan, China. ⋯ The SARS-CoV-2 infection rates in MHD patients and medical staff in dialysis facilities were both high in Wuhan. Frequent chest CT and SARS-CoV-2 nucleic acid detection were needed to screen COVID-19 patients in dialysis facilities. Through the lessons of this experience on the aggressive diagnosis and quarantine measures of COVID-19 patients, we hope medical staff avoid more infections in serious epidemic areas.
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Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. ⋯ Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.
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Intravenous fluids (IVF) are frequently utilized to restore intravascular volume in patients with distributive and hypovolemic shock. Although the benefits of the appropriate use of fluids in intensive care units (ICUs) and hospitals are well described, there is growing knowledge regarding the potential risks of volume overload and its impact on organ failure and mortality. To avoid volume overload and its associated complications, strategies to identify fluid responsiveness are developed and utilized more often among ICU patients. Apart from the amount of fluid utilized for resuscitation, the type of fluid used also impacts patient outcome. Colloids and crystalloids are two types of fluids that are utilized for resuscitation. The efficacy of each fluid type on the expansion of intravascular volume on one hand and the potential adverse effects of each individual fluid, on the other hand, need to be considered when choosing the type of fluid for resuscitation. The negative impact of hydroxyethyl starch on kidney function, of albumin on the mortality of head trauma patients and chloride-rich crystalloids on mortality and kidney function, are only examples of new developments in the field. ⋯ Avoiding fluid overload by choosing the appropriate amount of fluids in patients who are fluid-responsive on one hand, and treating IVF like other medications, on the other hand, are the major changes. Whenever clinicians decide to prescribe IVF, they need to weigh the risks and benefits of giving fluid and also the advantages and side effects of each fluid type in order to optimize patient outcomes.