Clinical Research Directory

Biomarker Signature-Supported Antibiotic Treatment Decisions in ICU

The goal of this randomized clinical trial is to determine whether a biomarker-signature (BV) supported antibiotic treatment decision matrix can have a beneficial impact on antibiotic use and patient outcomes in an ICU population.

Impact of Information and Participation on Decision Time for Tracheostomy or PEG in ICU Families

Decisions regarding tracheostomy and percutaneous endoscopic gastrostomy (PEG) in the intensive care unit (ICU) are frequently associated with substantial uncertainty, decisional conflict, and psychosocial burden among patients' relatives. Inadequate or non-tailored information may negatively influence anxiety levels, decision satisfaction, and subsequent adaptation to care responsibilities. Differences in communication patterns and educational background may further affect how families perceive the risks, benefits, and long-term implications of these procedures. This prospective study aims to evaluate the impact of relatives' educational level and different information delivery methods on anxiety, decision satisfaction, and the overall decision-making process related to tracheostomy and PEG in the ICU setting. The findings are expected to contribute to the development of structured, education-level-tailored information strategies to improve shared decision-making and family-centered care in critical care practice.

The PRE-VAIL Study

During the prone position, there may be an increase in intragastric pressure. The investigators focus on a scientific question: Whether reducing enteral nutrition before the prone position will benefit patients with severe mechanically ventilated acute respiratory distress syndrome (ARDS). The experimental group had the enteral nutrition dose reduced before the prone position, while the control group did not have the enteral nutrition dose reduced

Assessing Intensive Care Unit (ICU) Indications: Human vs. ChatGPT-4o Predictions

This retrospective study evaluates the accuracy of ICU admission indications by comparing clinical decisions with predictions from ChatGPT-4. Patient data, including demographics, vital signs, laboratory results, imaging findings, and clinical decisions, will be retrospectively collected and documented systematically using Case Report Forms. The model will be trained using ICU admission guidelines and tasked to predict ICU needs based on collected patient data. This study aims to systematically assess the alignment between AI-based predictions and clinical decisions for ICU admissions.