This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.
Invasive mechanical ventilation is one of the most important and life-saving therapies in the intensive care unit (ICU). In most severe cases, extracorporeal lung support is initiated when mechanical ventilation is insufficient. However, mechanical ventilation is recognised as potentially harmful, because inappropriate mechanical ventilation settings in ICU patients are associated with organ damage, contributing to disease burden. Studies revealed that mechanical ventilation is often not provided adequately despite clear evidence and guidelines. Variables at the ventilator and extracorporeal lung support device can be set automatically using optimization functions and clinical recommendations, but the handling of experts may still deviate from those settings depending upon the clinical characteristics of individual patients. Artificial intelligence can be used to learn from those deviations as well as the patient's condition in an attempt to improve the combination of settings and accomplish lung support with reduced risk of damage.
Retrospective Use of Patient Treatment Data for the Evaluation and Further Development of an Artificial Intelligence-based Algorithm for Clinical Decision Support in Invasive Mechanical Ventilation of Intensive Care Patients
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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Sponsor: Technische Universität Dresden
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.