View clinical trials related to Respiratory Distress Syndrome.
Filter by:Artificial intelligence (AI) shows promising in identifying abnormalities in clinical images. However, systematically biased AI models, where a model makes inaccurate predictions for entire subpopulations, can lead to errors and potential harms. When shown incorrect predictions from an AI model, clinician diagnostic accuracy can be harmed. This study aims to study the effectiveness of providing clinicians with image-based AI model explanations when provided AI model predictions to help clinicians better understand the logic of an AI model's prediction. It will evaluate whether providing clinicians with AI model explanations can improve diagnostic accuracy and help clinicians catch when models are making incorrect decisions. As a test case, the study will focus on the diagnosis of acute respiratory failure because determining the underlying causes of acute respiratory failure is critically important for guiding treatment decisions but can be clinically challenging. To determine if providing AI explanations can improve clinician diagnostic accuracy and alleviate the potential impact of showing clinicians a systematically biased AI model, a randomized clinical vignette survey study will be conducted. During the survey, study participants will be shown clinical vignettes of patients hospitalized with acute respiratory failure, including the patient's presenting symptoms, physical exam, laboratory results, and chest X-ray. Study participants will then be asked to assess the likelihood that heart failure, pneumonia and/or Chronic Obstructive Pulmonary Disease (COPD) is the underlying diagnosis. During specific vignettes in the survey, participants will also be shown standard or systematically biased AI models that provide an estimate the likelihood that heart failure, pneumonia and/or COPD is the underlying diagnosis. Clinicians will be randomized see AI predictions alone or AI predictions with explanations when shown AI models. This survey design will allow for testing the hypothesis that systematically biased models would harm clinician diagnostic accuracy, but commonly used image-based explanations would help clinicians partially recover their performance.
This is a single-blind randomized controlled trial done in a Level III neonatal intensive care unit. Preterm newborns with RDS were randomized to receive oxygen therapy through bubble CPAP vs ventilator-derived CPAP. Differences in arterial blood gases, oxygen saturation, number of surfactant and CPAP failure rate between study groups were analyzed.
The investigators are planning to perform a secondary analysis of an academic dataset of 1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in several published cohorts (NCT00736892, NCT022288949, NCT02836444, NCT03145974), aimed to characterize the best early scenario during the first three days of diagnosis to predict duration of mechanical ventilation in the intensive care unit (ICU) using supervised machine learning (ML) approaches.
This prospective, blinded observational clinical study was aimed to determine the effect of hyperhydration and muscle loss measured by Bioelectrical impedance vector analysis (BIVA) on mortality. The aim was to compare hydratation parameters measured by BIVA: OHY, Extracellular Water (ECW) / Total Body Wate (TBW) and quadrant, vector length, phase angle (PA) with cumulative fluid balance (CFB) recording (input-output) in their ability in predicting mortality as the abilities of the prognostic markers PA (BIVA), Acute Physiology and Chronic Health Evaluation II (APACHE II - score) and presepsin (serum Cluster of Differentiation (CD) 14-ST). The investigators also compared BIVA nutritional indicators (SMM, fat) with BMI and laboratory parameters (albumin, prealbumin and C-reactive protein (CRP) inflammation parameters) in the prediction of mortality. An important goal was to evaluate the usability of the BIVA method in critically ill patients on extracorporeal circulation, to compare the impedance data of the extracorporeal membrane oxygenation (ECMO) and non-ECMO groups.
PRactice of Ventilation and Adjunctive Therapies in COVID-19 Patients. An observational study of ventilation practice and adjunctive therapies in critically ill, invasively ventilated COVID-19 patients during the first and second surge of COVID-19 in the Netherlands.
The aim of this study is to identify existing definitions and therapeutic approaches for acute right ventricular injury (RVI) in patients receiving extracorporeal membrane oxygenation (ECMO) for respiratory support. The objective of the study is to generate expert consensus statements on the definition and management of acute RVI in this high-risk patient population, using a Delphi method. The standardised RVI definition during ECMO for respiratory support and a consensus-based management approach to RVI will facilitate systematic aggregation of data across clinical trials to harmonise patient selection and compare therapeutic interventions.
Research question: Are the ventilatory variables related to mechanical power associated with the outcome of subjects who received mechanical ventilation (MV) for Acute Respiratory Distress Syndrome (ARDS) secondary to pneumonia (NMN) due to COVID-19?
The goal of this observational study is to learn about the effect of steroid therapy in patients with COVID-19 ARDS. The main questions it aims to answer are: - Differences between patients with COVID-19 ARDS before and after steroid treatment in BALF single cell landscape, as well as patients with different prognosis. - Differences between COVID-19 and non COVID-19 ARDS patients in BALF single cell landscape. Participants will Choose whether to use or not to utilize steroid treatment based on conditions.
Hope to realize the recovery condition of ARDS survivors in Taiwan. It would be helpful not only to design the proper rehabilitation program but also to be a useful reference for the poor recovery patients to take hospice care if indicated.
Excessive respiratory effort may cause self-inflicted lung injury (SILI) and inspiratory muscle injuries , stimulate desynchronization between the patient and ventilator , and worsen the perfusion of extrapulmonary organs . Appropriate respiratory drive and effort should be maintained during the treatment of patients with respiratory failure . In contrast, respiratory drive and effort are commonly increased in patients with COVID-19 pneumonia , and this phenomenon may persist in critically ill patients with COVID-19, even after receiving venovenous ECMO (vv-ECMO) support, owing to low pulmonary compliance and a high systemic inflammatory state . To reduce respiratory effort and drive, ICU physicians often administer high doses of sedative drugs, analgesics, and muscle relaxants. The prolonged use of high doses of these drugs can cause loss of the spontaneous cough reflex, which in turn impairs sputum drainage and eventually worsens pulmonary consolidation and lung infections. As the partial pressure of carbon dioxide in arterial blood (PaCO2) could affect the respiratory drive from the respiratory center (1), it has been shown that altering different levels of extracorporeal carbon dioxide removal in patients undergoing ECMO recovering from acute respiratory distress syndrome (ARDS) could alter respiratory drive. We hope to find a more appropriate target for maintaining PaCO2 to control respiratory effort in patients with COVID-19 undergoing ECMO.