Clinical Research Directory

The Impact of Fast Antimicrobial Sensitivity Testing Tools on Stewardship Antibiotic and Clinical Outcome (ACT-FAST)

The ACT-FAST study aims to compare commercially available Rapid Antimicrobial Susceptibility Testing (R-AST) tools with the current standard of care for patients with Bloodstream Infections (BSI). The primary objective is to evaluate whether "early targeted" antibiotic prescriptions, guided by these rapid tests, can improve antimicrobial stewardship and patient clinical outcomes. To facilitate the evaluation of various diagnostic tools-including those currently on the market and those emerging in the near future-this study utilizes an adaptive clinical trial platform. This flexible design allows for the continuous assessment of different R-AST technologies within a single master protocol, ensuring that the most effective diagnostic strategies are identified efficiently.

Initial Feasibility Study of the CycloPE® Device

This is a single-center, prospective, single-arm clinical study to evaluate the feasibility, safety, and performance of the PATH EX CycloPE® device on suspected bacteremia-associated sepsis. All participants are adults diagnosed with suspected bacteremia-associated sepsis, admitted to the intensive care unit (ICU), and meet the inclusion criteria.

Monocyte Distribution Width (MDW) in the General Population of Emergency Department Patients With and Without Bacteremia

This project will evaluate the usefulness of Monocyte Distribution Width (MDW) for the diagnosis of blood culture positivity (BSI) in patients in the Emergency Department (ED) and reevaluate the usefulness of MDW in patients with BSI and sepsis. Consequently, if MDW indicate a high likelihood of bacteremia antibiotic management in patients with suspected bacterial infections will be changed and aid appropriate antibiotic administration.

Machine Learning Assisted Electrochemical Profiling to Provide Early Identification of Bloodstream Infections Pathogens

In the context of a bacteremia, although significant progress has been made in speeding up pathogen identification once a blood culture bottle turns positive, few cost-effective solutions have been proposed to improve the earlier stages of the process-specifically, from blood collection to bottle positivity. The investigators propose that transport time could be leveraged to grow and identify bacteria, enabling faster access to actionable results through innovative technologies. This project aims to develop a bacterial identification database by analyzing the electrochemical profile of bacteria growing within the blood culture bottle, using machine learning.