Active clinical trials for "Acute Kidney Injury"

Open elective abdominal aortic surgery is a high risk procedure involving clamping of the aorta. Indications include abdominal aortic aneurysm (AAA) or aortic occlusive disease (AOD) causing lower limb ischaemia. These patients are often regarded as one entity in postoperative study settings. However, previous studies indicate that risk profiles, inflammatory activity, and haemodynamic capacity may differ between these groups. The first aim of this study was to evaluate postoperative ICU-requirements after open elective abdominal aortic surgery, hypothesising that AAA-patients had longer ICU-stays and needed more mechanical ventilation or acute dialysis than did patients with AOD. The investigators see a relatively high incidence of postoperative acute kidney injury (AKI) following aortic surgery. Neutrophil Gelatinase Associated Lipocalcin (NGAL) may be useful in the early diagnosis of postopeative AKI. However, NGAL is also known as a marker of inflammatory activation. The ischaemia-reperfusion injury and subsequent inflammatory response to aortic cross clamping may per se induce a rise in NGAL despite intact renal function. Therefore NGAL may not be a reliable marker of AKI after AAS. The second aim of this study is to describe the changes in NGAL after AAS in patients with and without postoperative dialysis-dependent AKI.

The purpose of this study is to be able to better describe the incidence, timing and severity of myocardial dysfunction and acute kidney injury (AKI) following a documented respiratory viral illness (RSV, adenovirus, metapneumovirus, parainfluenza, influenza, etc.) in pediatric patients.

The purpose of this study is to determine if intensive insulin therapy really reduces the incidence of acute kidney injury in critically ill patients, using for analysis the RIFLE criteria for definition of AKI.

To understand how AKI (Acute Kidney Injury) leads to chronic kidney disease so therapies can be found to alter the progression of events thereby significantly impacting the long-term outcomes of children who develop AKI.

Renal lithium clearance is hypothesized to be a useful indicator of renal tubular function. In this study lithium clearance will be monitored in patients with sepsis associated acute kidney injury and in healthy controls.

The goal of this observational study is to compare in ICU patients received CVVH treatment. The main questions it aims to answer are: Control of Filtration fraction by built-in calculator Reduced filter clotting and prolong filter lifespan Improve patient mortality Participants will divided into before group and after group based on the day when built-in calculator for calculating Filtration fraction was utilized. Researchers will compare before group to see if there were differences in clotting events, filter downtime, filter lifespan, mortality, etc.

Acute kidney injury (AKI) is one of the most frequent major complications in patients undergoing cardiac surgery. CSA-AKI is independently related to increased perioperative mortality, increased hospital and ICU length of stay as well as heath care expenditure. Identification of relevant biomarkers may lead to early diagnosis and improve patient outcomes and health care costs. The pathophysiology of CSA-AKI is complex and ischemia-reperfusion injury is one of the important factors. Recently, it has been shown that Protein kinase N1 (PKN1) is associated with ischemia-reperfusion injury. In this study, relationship between PKN1 with the risk of CSA-AKI was analyzed and the predictive value of elevated level of PKN1 for early prediction of CSA-AKI was further evaluated.

The study aims to evaluate the performance of the prediction algorithm NAVOY® AKI in a Swedish adult ICU patient population versus fulfilment of KDIGO diagnostic criteria.

Coronavirus disease 2019 is a novel viral disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus. The original cases occurred in Wuhan, China, in December 2019 and rapidly spread to other areas worldwide, constituting a pandemic with unimaginable health and economic consequences. the World Health Organization elevated the disease to the category of a pandemic on March 11, 2020. In children, the reported mortality rates were far below 1%, while in people above the age of 70 years it was above 5% or higher. So, in this retrospective study, the investigators describe the clinical features and outcomes of children with chronic kidney diseases who were diagnosed with Severe Acute Respiratory Syndrome Coronavirus 2 infection at pediatric centers in Doha from 1st March 2020 till January 20th, 2022. This review looks into the literature on pediatric patients with chronic kidney diseases to verify whether they were more prone to developing more severe symptoms when diagnosed with Coronavirus disease 2019 compared to children without chronic kidney diseases and adults with chronic kidney diseases, and the Prevalence of COVID-19 infection between patients with chronic kidney diseases, and the role of COVID-19 infection in increasing the relapses and deterioration of chronic kidney diseases.

<Development of synthetic medical data generation technology to predict postoperative complications> In order to develop a model for predicting the occurrence of complications after surgery, it is necessary to establish a cohort along with statistical indicators related to the occurrence of complications. This study aims to combine synthetic medical data based on actual clinical data and develop a predictive model based on synthetic medical data. This will allow researchers to conduct research only with synthetic data without dealing with actual medical data, allowing them to use and process data without legal constraints, and to create as much data as they want based on various preprocessed, standardized, and labeled raw data. Patients from three hospitals in Korea (Seoul National University Hospital, Seoul National University Bundang Hospital, Seoul Metropolitan City-Boramae Medical Center) were enrolled for the study. Medical data (both clinical and laboratory) from 410,000 patients who were conducted surgery between 2005 and 2020 were collected to evaluate the performance of the prediction model using AKI-based prediction model development and external verification. Based on the collected patient data, synthetic medical data were combined using the machine learning algorithm, and the anonymity and re-identification of the synthesized medical data were evaluated. Also, the development of AI-based prediction model using synthetic medical data and the actual medical data model were compared.