View clinical trials related to Severe Sepsis.
Filter by:In this clinical outcomes analysis, the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay, and 30-day readmission was evaluated.
This phase II trial studies how well early metabolic resuscitation therapy works in reducing multi-organ dysfunction in patients with septic shock. Early metabolic resuscitation is made of large doses of glucose, protein, and essential metabolic molecules that may help lower the effects of septic shock on the body. Giving patients early metabolic resuscitation in combination with standard of care may work better in reducing multi-organ dysfunction syndrome in patients with septic shock compared to standard of care alone.
The focus of this study will be to conduct a prospective, multi-center randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a machine-learning algorithm will be applied to EHR data for the detection of sepsis. For patients determined to have a high risk of sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, in-hospital SIRS-based mortality.
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a fluid treatment-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, reductions in in-hospital mortality.
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a Gram type infection-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, time to antibiotic administration. The secondary endpoint will be reduction in the administration of unnecessary antibiotics, which includes reductions in secondary antibiotics and reductions in total time on antibiotics.
The purpose of this study is to study the implementation and impact of an early warning system to detect and treat sepsis in the emergency room. We are observing the implementation of a Sepsis Machine Learning Model on all Adult patients. All data (observations field notes, interview recording & transcripts, and survey responses) will be stored on HIPAA-compliant Duke servers behind the Duke firewall, and requiring password-protected user authentication to access. The risk to patients is minimal. The two risks to interviewed clinical staff we have identified involve loss of work time and anonymity.
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a subpopulation-optimized algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, in-hospital SIRS-based mortality. The secondary endpoints will be in-hospital severe sepsis/shock-coded mortality, SIRS-based hospital length of stay, and severe sepsis/shock-coded hospital length of stay.
Sepsis and severe malaria together contribute to an estimated 13 million deaths annually, a great burden of which is in low-income countries. Optimal fluid management is critical yet remains one of the most challenging clinical care elements as volume overload precipitates pulmonary edema and volume restriction may exacerbate acute kidney injury. These complications of sepsis and severe malaria significantly increase mortality, particularly in resource-limited settings lacking mechanical ventilation and renal replacement therapy. Point-of-care ultrasound and passive leg raise testing are two easily implementable, safe and non-invasive clinical bedside fluid assessment tools that could be applied towards developing a fluid management algorithm in low resource settings. Similarly, simple tissue perfusion measures can facilitate understanding of precise indications or contraindication to fluid and vasopressor therapy. However, the performance of these tools has yet to be confirmed in these settings. Accurate assessment of pulmonary tolerance and fluid responsive patients could aid to tailor vasopressor and fluid therapy to the patient condition and disease phase, thus preventing or detecting iatrogenic pulmonary edema and other pulmonary complications. As there is currently limited evidence supporting fluid management recommendations for severe malaria and sepsis in low-resource settings, the potential application of these management tools could optimize supportive therapy and improve outcomes in these populations. The main activity proposed is a prospective, observational study of patients with sepsis and severe malaria to describe the relationship between fluid therapy and vasopressor therapy against measures of tissue perfusion and pulmonary congestion in adult patients with severe malaria or severe sepsis. In addition, the study will assess the performance of simple bedside clinical tools assessing fluid responsiveness, pulmonary congestion and peripheral tissue perfusion. The data from this observational study will facilitate the preparation of a follow-up study to test a clinical algorithm to guide individualized fluid and vasopressor administration.
The purpose of this study is to confirm the clinical validity and the performance of the Monocyte Width Distribution (MDW) parameter to detect the development of sepsis in a prospective study of Emergency Department (ED) adults who have blood draw including Complete Blood Count with differential (CBC-DIFF) ordered upon presentation in a Spanish & French hospital and to verify cut-off for Tri-potassium ethylenediaminetetraacetic acid (K3EDTA).
Severe sepsis leads a high morbidity and mortality by causing organ damage at distance. The treatment relies on early antibiotic therapy and hemodynamic resuscitation. Hypothesis: high flow nasal cannula (HFNC) could reduce work of breathing and improve the outcome of patients with severe sepsis and peripheral perfusion. Objective: the aim of this study is to evaluate the efficacy of HFNC for improving sixty-day survival in patients with severe sepsis. Design: multicenter parallel-group randomized clinical trial. Method: 592 adult patients with a diagnosis of severe sepsis in the first 12 hours of admission in the Emergency Room will be randomly assigned to an experimental or control group. In the experimental group, HFNC will be administered until the resolution of sepsis or until required mechanical ventilation, either invasive or non-invasive. In the Control group, conventional oxygen will be administered, if required. Sixty-day survival will be the primary outcome. The study is powered to demonstrate an improvement in survival from 70% in control group up to 80% in the HFNC group. The secondary outcomes will be reducing the need for vital support (mechanical ventilation, dialysis, vasoactive drugs) and physiological (acidosis, clearance of lactate, SvO2 and SOFA). Statistical analysis: Kaplan-Meier curves and Cox proportional hazard models will be calculated for all-cause sixty-day survival. If the results are conclusive, they will have immediate application in medical practice.