Clinical Research Directory

Evidence-Based Nursing to Reduce Adverse Events Regarding Ventilator Associated Pneumonia, Pressure Injuries and Central Line Bloodstream Infection in Intensive Care Unit in Bangladesh

Quality of care is crucial for preventing adverse events such as infection in the intensive care units (ICUs). The most common ICU-acquired infections include pneumonia such as Ventilator Associated Pneumonia (VAP), Pressure Ulcer (PU), Medical Device Related Pressure Ulcer (MDRPU), and Central Line-Associated Bloodstream Infection (CLABSI). VAP, PU, and MDRPU are significant indicators of the quality of care, while CLABSI is a critical marker of healthcare quality in ICUs, as it is largely preventable through strict adherence to infection control protocols. VAP is a lung infection that occurs in patients receiving mechanical ventilation for more than 48 hours. A PU is defined as localized damage to the skin or underlying tissue caused solely by pressure, while MDRPUs may also result from friction or pressure from medical devices or other objects. CLABSI is a severe infection caused by improperly inserted or maintained central venous catheters, which underscores the need for stringent catheter management protocols.

Comparing The Safety And Efficacy Of DEFENCATH® In Reducing Central-Line Bloodstream Infections (CLABSIs) In Adults Receiving Total Parenteral Nutrition Through A Central Venous Catheter (CVC)

This is a Phase 3, randomized, double-blind, controlled, adaptive, 2-arm, multicenter study to demonstrate the efficacy and safety of DefenCath in adult participants receiving home Total Parenteral Nutrition (TPN) via Central Venous Catheter (CVC) compared with heparin.

Machine Learning Prediction of Possible Central Line Associated Blood Stream Infections and Rate of Reduction

Prospective, multi-center, cluster-randomized trial of a hospital Infection Preventionist (IP)-led quality improvement study to provide clinical teams with just-in-time clinical education and reinforcement of existing best practices recommendations based on the output of a possible Central Line Associated Blood Stream Infection (CLABSI) Machine Learning (ML) prediction model. The objective is to determine whether providing this model to Infection Preventionists will decrease the CLABSI rates versus routine clinical practice.