View clinical trials related to Sepsis.
Filter by:Sepsis is defined as a dysregulated host response to infection . Despite ongoing efforts, both the incidence and mortality of sepsis have demonstrated limited reductions over the past years,There are several biomarkers that have already been studied for the early diagnosis of sepsis. Some of these markers can be used in risk prediction and monitoring the outcome of sepsis . Some of these markers as procalcitonin and CD14, are costly and not feasible options for low- and middle-income countries . While other biomarkers are feasible and accessible to be evaluated as Triglyceride\glucose index (TyG) , Relative Distributive Width of red blood corpuscles to albumin ratio (RAR), C-reactive protein,Neutrophile \Lympocyte ratio and serum lactate levels .
Extracorporeal membrane oxygenation (ECMO) is a form of cardiopulmonary life-support for critically ill patients where blood is extracted from the vascular system and circulated by a mechanical pump while it is oxygenated and reinfused into the patient's circulation. It is well known that critically ill patients may experience alterations in antibiotic pharmacokinetics, and as a result, dosing modifications are generally required. There is a need to understand how ECMO circuits affect the pharmacokinetics and disposition of drugs. This study is designed to assess the pharmacokinetics of the new broad-spectrum echinocandin, Rezafungin, in critically ill patients receiving ECMO
Patients with intracerebral hemorrhage (ICH) in the intensive care unit (ICU) are at heightened risk of developing sepsis, significantly increasing mortality and healthcare burden. Currently, there is a lack of effective tools for the early prediction of sepsis in ICH patients within the ICU. This study aims to develop a reliable predictive model using machine learning techniques to assist clinicians in the early identification of patients at high risk and to facilitate timely intervention. The Medical Information Mart for Intensive Care (MIMIC) IV database (version 2.2) is an international online repository for critical care expertise. This database contains patient-related information collected from the ICUs of Beth Israel Deaconess Medical Center between 2008 and 2019. It includes a vast dataset of 299,712 hospital admissions and 73,181 intensive care unit patients. The eICU Collaborative Research Database (eICU-CRD) comprises data from over 200,000 ICU admissions for 139,367 unique patients across 208 US hospitals between 2014 and 2015, providing a valuable resource for critical care research. This study aims to establish and validate multiple machine learning models to predict the onset of sepsis in ICU patients with ICH and to identify the model with the optimal predictive performance.
The RADAR-Canada trial is a pilot RCT undertaken to assess the acceptability of, compliance with, and biologic consequences of a deresuscitation protocol designed to expedite the removal of excess interstitial fluid in patients who remain in a positive fluid balance following admission to an intensive care unit (ICU).
Obesity has been shown to increase adverse outcomes in some critically ill patients e.g. those with COVID-19. For patients with sepsis this association is less clear cut but there is evidence that body fat distribution, resulting from impaired subcutaneous adipose tissue function, is associated with adverse clinical outcomes in critical care. The investigators aim to study subcutaneous adipose tissue function in lean and obese sepsis patients in critical care and compare that to healthy controls. First, the study will investigate differences in adipose tissue function (inflammation and mitochondrial function) related to obesity. Second, the investigators will examine whether lean critically ill patients with sepsis have enhanced adipose tissue inflammation and mitochondrial dysfunction compared to lean controls and whether this is further exacerbated by obesity. Patients will be either undergoing emergency abdominal surgery, or will have been admitted to a critical care unit with a diagnosis of sepsis. The investigators will collect blood and adipose tissue biopsies from the patients, and these will be analysed for markers of inflammation and of mitochondrial function. The aim is to better understand the relationship between obesity, inflammation, mitochondrial dysfunction and sepsis. The investigators hope that this may improve the understanding of the pathophysiology of sepsis and allow more targeted interventions for patients based on differences in their baseline metabolic state.
In this prospective observational study, patients hospitalized in mixed intensive care unit, aged between 18 and 80, and diagnosed with sepsis and septic shock according to sepsis-3 criteria will be included. To determine whether patients develop AKI during the first five days of ICU admission, creatinine and urine output will be monitored daily for the first five days of ICU admission according to KDIGO criteria. Clinical diagnosis and treatment of AKI will be made according to KDIGO. According to KDIGO, patients will be divided into two groups: those who develop AKI and those who do not. By comparing plasma NGAL and VEXUS scores between groups, the sensitivity and specificity of the VEXUS score in determining AKI will be determined.
Rudiger and Singer suggested strategies for refining adrenergic stress (decatecholaminization). They proposed the use of dexmedetomidine and vasopressin to reduce the catecholamine load during sepsis. The investigators will use vasopressin as the primary vasopressor and a heart rate-calibrated dexmedetomidine infusion in septic shock patients. The investigators of the current study will use DEXPRESSIN in septic shock patients to investigate the effects of decatecholaminization on in-hospital mortality.
Thyroid and cortisol hormone response to sepsis
In order to clarify the clinical efficacy of electroacupuncture on inhibiting systemic inflammatory response, improving respiratory mechanics parameters and prognosis in patients with sepsis-related ARDS.
The goal of this study is to create a computer simulation of patients with bloodstream infection to understand how changes in healthcare policies and resources affect patient treatment. This simulation will help doctors and health-care decision makers make better choices in treating these patients and avoid overusing antibiotics that can lead to antibiotic resistance. Antibiotic resistance is when bacteria can't be killed by antibiotics anymore. Participants will not receive treatments as this is an observational study, but the study will involve: - Interviews with healthcare staff to understand patient care pathways. - Analysis of historical data on bacteria causing infections and antibiotic treatments. - A 30-day observational study to observe patient treatment for bloodstream infections.