View clinical trials related to Toxemia.
Filter by:Sepsis-associated encephalopathy (SAE), is one of the most common organ dysfunction during the acute phase in sepsis and septic shock. Electroencephalogram (EEG) and auditory evoked potentials (AEPs), which reflect different aspects of brain function, are the most commonly used neurophysiological indices to detect acute brain dysfunction in critically ill patients including sepsis and septic shock. AEPs show the systemic responsiveness of the central nervous to auditory stimuli, so they can be considered a direct measure of brain responsiveness. Mismatch negativity (MMN) is a change-specific component of ERPs, which elicited by a deviant stimulus occurring in a sequence of repetitive stimuli. This component is thought to represent the automatic and unconscious detection of acoustic changes which requires good perceptual discriminative capacity and iconic memory. The peaks of MMN appear at 100 ~ 250 ms from deviant stimulus onset; with increasing magnitude of stimulus change, the peak latency of MMN was shortened and the amplitude increased. Since MMN can be elicited even in the absence of attention, subjects do not need to actively participate. The MMN has been extensively demonstrated to be used in the prediction of awakening in comatose patients for various reasons, and also has been reported to predict awakening in deeply sedated critically ill patients recently. However, it remains unclear whether SAE affects MMN in amplitude and latency that reflects cognitive processing of the auditory information. Patients with sepsis and septic shock who met the inclusion criteria were screened daily on the CAM-ICU scale, and those with positive CAM-ICU were diagnosed with SAE.All patients were tested for event-evoked potentials on Day 1 and Day 3 after inclusion and were followed up to Day 28 after discharge. The investigators intend to observe the dynamic change of MMN amplitude and latency between SAE and non-SAE groups. Logic regression analysis was used to determine whether the change of MMN was a predictor of SAE.
This study is a multi-center, randomized, partially double-blind, and placebo-controlled Phase Ib clinical trial of inhaled CO (iCO) for the treatment of sepsis-induced acute respiratory distress syndrome (ARDS). The purpose of this study is to evaluate the safety and accuracy of a Coburn-Forster-Kane (CFK) equation-based personalized iCO dosing algorithm to achieve a target carboxyhemoglobin (COHb) level of 6-8% in patients with sepsis-induced ARDS. We will also examine the biologic readouts of low dose iCO therapy in patients with sepsis-induced ARDS.
Sepsis is the leading cause of death in intensive care units and a major public health concern in the world. Heparin, a widely used anticoagulant medicine to prevent or treat thrombotic disorders, has been demonstrated to prevent organ damage and lethality in experimental sepsis models. However, the efficacy of heparin in the treatment of clinical sepsis is not consistent. Caspase-11, a cytosolic receptor of LPS, triggers lethal immune responses in sepsis. Recently, we have revealed that heparin prevents cytosolic delivery of LPS and caspase-11 activation in sepsis through inhibiting the heparanase-mediated glycocalyx degradation and the HMGB1- LPS interaction, which is independent of its anticoagulant properties. In our study, it is found that heparin treatment could prevent lethal responses in endotoxemia or Gram-negative sepsis, while caspase-11 deficiency or heparin treatment failed to confer protection against sepsis caused by Staphylococcus aureus, a type of Gram-positive bacterium. It is probably that other pathogens such as Gram-positive bacteria might cause death through mechanisms distinct from that of Gram-negative bacteria. Peptidoglycan, a cell-wall component of Gram-positive bacteria, can cause DIC and impair survival in primates by activating both extrinsic and intrinsic coagulation pathways, which might not be targeted by heparin. We speculate that the discrepancy between the previous clinical trials of heparin might be due to the difference in infected pathogens. Thus, stratification of patients based on the type of invading pathogens might improve the therapeutic efficiency of heparin in sepsis, and this merits future investigations.
The randomized control trial aims to determine the effect of twice daily application of a commonly used coconut oil to the skin of neonates in the neonatal intensive care setting on the rate of late onset sepsis versus a no treatment control.
An adaptive platform trial to compare effectiveness of different care models to prevent readmissions for patients hospitalized with sepsis or lower respiratory tract infection. The primary outcome is number of days spent at home within 90 days after hospital discharge.
Phase II study of Kukoamine B Mesilate in Sepsis Patients
Sepsis is a syndrome involving infection and an abnormal systemic inflammatory response in the infected organism, resulting in organ dysfunction and possibly death. It is a major cause of hospital mortality. A large proportion of sepsis diagnoses are made in emergency departments. Early diagnosis and appropriate treatment have been shown to reduce mortality from this disease. In a context of limited resources, it is therefore important to be able to quickly stratify patients presenting to the emergency department with a suspected infection into those who require rapid and intensive management because they are at risk of developing sepsis and septic shock and those who can be managed conventionally The objective of this study is to compare the clinical intuition of emergency room physicians and nurses with the qSOFA score to predict the clinical course of patients presenting to the emergency room with potential sepsis.
Sepsis is one of the leading causes of death in intensive care. About 50% of patients with septic shock die after 1 year; and 50% of survivors suffer from cognitive decline. The pathophysiological mechanisms of serious complications of sepsis are now well known. In fact, the systemic inflammation related to sepsis amplifies the release of pro-inflammatory cytokines and neurotoxic mediators, hence an increase in deleterious phenomena such as oxidative stress, mitochondrial dysfunction, endothelial activation, disruption of the blood-brain barrier, neuroinflammation (astrocytic and microglial activation) leading to multi-organ failure which compromises the patient's vital and functional prognosis. Although there has been progress in the understanding of its pathophysiology, the management of sepsis and septic shock in intensive care relies mainly on anti-infective treatments and the restoration of cardiovascular and respiratory functions. There is virtually no adjuvant therapy for the management of sepsis, apart from a few hormonal therapies such as insulin to maintain blood glucose levels below 180 mg / dL and low doses of corticosteroids and vasopressin. There is therefore a pressing need to develop innovative treatments targeting inflammatory and immunological processes in order to reduce the complications of sepsis and improve patient prognosis. Some recent work has shown that electrical vagus nerve stimulation (SNV), a technique used for the treatment of drug-resistant epilepsy, can modulate inflammatory and immune responses and control inflammation syndrome in animal models of sepsis, arthritis and rheumatism in humans. In this pilot study the investigators plan to evaluate the efficacy of transcutaneous (non-invasive) SNV as an adjuvant treatment in patients with sepsis in intensive care.
Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis. Using machine learning and natural language processing, we want to develop models to 1) identify all children with sepsis admitted to hospital and 2) stratify them to distinguish those who are at high risk of death b) How will you undertake your work? From Shanghai hospitals anf MIMIC III, we will develop a very large dataset of patient admissions for all medical conditions including sepsis from the electronic health record. This data will include both structured data such as age, gender, medications, laboratory values, co-morbidities as well as unstructured data such as discharge summaries and physician notes. Using the dataset, we will train a model through natural language processing and machine learning to be able to identify people admitted with sepsis and identify those patients who will be at high risk of death. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.
This is a multicenter, randomized, double-blind, placebo controlled trial, with parallel groups and reference group. The aim of the study was to evaluate the hypothesis that an immunonutritional strategy, based on use of Lactobacillus paracasei CBA L74-fermented formula, prevents or limits the development of late-onset-sepsis in preterm infants.