Sepsis Clinical Trials

The Non-Invasive Cardiac Output Monitor (NICOM) is a non-invasive monitor capable of measuring cardiac output (CO) and cardiac index (CI), and stroke volume (SV) and stroke volume index (SVI) based on heart rate. Conceptually NICOM is a technology that utilizes a dynamic response characteristic in assessing the need for fluid administration, whereby SVI is measured before and after a fluid challenge with more fluid given only if SVI increases significantly with administered fluid. Dynamic response technologies are intended to replace older, "static" measures such as central venous pressure (CVP) and pulmonary capillary wedge pressures (PCWP) which are single point measurements utilized to assess the need to administer fluid. The aim is to pilot and evaluate the effectiveness of using The Non-Invasive Cardiac Output Monitor (NICOM) technology for goal-directed fluid resuscitation in adult inpatients with sepsis associated acute hypotension and/or evidence of septic shock (Lactate >= 4.0). Read Less

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. Read Less

The primary purpose of this study is to evaluate the impact of an Emergency Medical Services (EMS) based sepsis screening and early warning protocol on the timing of early sepsis care in the Emergency Department (ED). Read Less

This study addresses critically ill sepsis patients' current literature reports of ongoing post-hospital discharge weakness and hospital readmissions. This study is aimed at capture and interpretation of a complex set of tests, administered during a subject's sepsis functional recovery trajectory, particularly capturing hospital readmission's effects on survivors' physical function recovery. Read Less

In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients. The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults. Read Less

Lipids and lipoproteins (cholesterol and lipid metabolites) are present in sepsis and are highly biologically active regulators of inflammation, but currently the changes in lipid and lipoprotein homeostasis during sepsis are not well understood. This project will investigate the changes in lipid and lipoprotein function, oxidation, metabolites, and changes in gene expression to further our understanding of dysregulated lipid and lipoprotein metabolism in sepsis. We will analyze a bank of samples and make associations with important clinical outcomes (early death, chronic critical illness and sepsis recidivism) as supported by our published work, and will confirm our findings in a small prospective cohort of sepsis patients. Read Less

The aim of this project is to test the utility of The Gene Z device (as of 2018 Gene Z no longer being used) and other rapid identification techniques that the investigators have developed in the lab on clinically obtained bodily fluid samples taken from patients with suspected infection or sepsis based on having three of four positive Systemic Inflammatory Response Syndrome markers, or having a known infection for which a specimen is being collected. Specimens will be collected by Sparrow Laboratories and McLaren Greater Lansing laboratories, processed and stored for analysis at a later date to determine if the microbial pathogens identified by current methods of culture, as well as pathogen susceptibility to antibiotics by culture results, can be identified by the GeneZ technology or other developed technology accurately, and more timely. It will not affect current patient care nor impact patient care, which will continue in the standard fashion today for sepsis. Results will be compared to standard culture results and antibiotic sensitivities. Read Less

The overall objective of this proposed research is the derivation of a biomarker-enhanced artificial intelligence (AI)-based pediatric sepsis screening tool (PSCT) (software) that can be used in combination with the hospital's electronic health record (EHR) system to monitor and assess real-time emergency department (ED) electronic health record (EHR) data towards the enhancement of early pediatric sepsis recognition and the initiation of timely, aggressive personalized sepsis therapy known to improve patient outcomes. It is hypothesized that the screening performance (e.g., positive predictive value) of the envisioned screening tool will be significantly enhanced by the inclusion of a biomarker panel test results (PERSEVERE) that have been shown to be effective in prediction of clinical deterioration in non-critically ill immunocompromised pediatric patients evaluated for infection. It is also hypothesized that enhanced phenotypes can be derived by clustering PERSEVERE biomarkers combined with routinely collected EHR data towards improved personalized medicine. Read Less

Observational study for monitoring of capillary refill time in sepsis Read Less

Machine learning is a powerful method to create clinical decision support (CDS) tools, when training labels reflect the desired alert behavior. In our Phase I work for this project, we developed HindSight, an encoding software that was designed to examine discharged patients' electronic health records (EHRs), identify clinicians' sepsis treatment decisions and patient outcomes, and pass those labeled outcomes and treatment decisions to an online algorithm for retraining of our machine-learning-based CDS tool for real-time sepsis alert notification, InSight. HindSight improved the performance of InSight sepsis alerts in retrospective work. In this study, we propose to assess the clinical utility of HindSight by conducting a multicenter prospective randomized controlled trial (RCT) for more accurate sepsis alerts. Read Less

This is a single-center prospective bio-specimen analysis and observational study aiming to define immune pathways disrupted in bacterial sepsis and to identify clinically useful biomarkers of immune status. Read Less

Randomized, unblinded clinical trial of 152 critically ill patients with sepsis admitted to the intensive care unit. The primary determine if using the Venous Excess Ultrasound Score (VExUS) to guide fluid deresuscitation in critically ill patients with sepsis reduces net fluid balance at 5 days as compared to usual care. Read Less