View clinical trials related to Respiratory Failure.
Filter by:This clinical trial aims to assess the efficacy of sedation protocol targeting optimal respiratory drive using P0.1 and arousal level compared with conventional sedation strategy (targeting arousal level alone) in patients requiring mechanical ventilation in the medical intensive care unit.
The goal of this pilot interventional no-profit study is to evaluate airway pressure, esophageal pressure and variations in lung volume distribution with EIT in mechanically ventilated patients admitted to our UTI with respiratory failure after the application of an abdominal weight and resulting increase of intra-abdominal pressure.
Acute Respiratory Distress Syndrome (ARDS), marked by acute hypoxemia and bilateral pulmonary infiltrates, has undergone multiple definitions over the years. Challenges persist regarding the ARDS definitions, leading to various revisions. Through the Delphi study, the investigators aims to gather global opinions on the conceptual framework of ARDS, assess the utility of current and past definitions, and explore the role of subphenotyping. The diverse panel's collective expertise will contribute valuable insights for refining future ARDS definitions and enhancing clinical management.
Observational and randomized trials have demonstrated the high effectiveness of non-invasive helmet ventilatory support, demonstrating a reduction in intubation rate mortality compared with high-flow and standard oxygen therapy. Some pilot physiological studies have shown physiological benefits of helmets compared to the oronasal mask for non-invasive ventilation. The purpose of the study is to compare markers of patient self-inflicted lung injury (P-SILI), patient's comfort, work of breathing, gas exchange, and hemodynamics in patients with acute hypoxemic respiratory failure (AHRF) during non-invasive ventilation (NIV) in continuous positive pressure (CPAP) mode during an oronasal mask ventilation or a combination of a helmet with high-flow oxygenation as an air flow generator.
The oximeter is used to monitor intensive care patients undergoing oxygen therapy. It indicates pulsed oxygen saturation (SpO2), a reflection of arterial oxygen saturation (SaO2) which enables detection of hypoxemia and hyperoxia, both deleterious state. Current SpO2 recommendations aim to reduce both risk of hypoxemia and hyperoxia. SpO2 is considered the 5th vital sign. Current recommendations for SpO2 targets do not consider the variability of oximeters used in clinical practice. This variability and lack of specification represent an obstacle to an optimal practice of oxygen therapy. Thus, this study aims to compare the SpO2 values of different oximeters (General Electric-GE, Medtronic, Masimo and Nonin) used in clinical practice with the SaO2 reference value obtained by an arterial gas in order to specify the precision and the systematic biases of the oximeters studied. This data will also make it possible to refine the recommendations concerning optimal oxygenation
This is a confirmatory study without any intervention. It is an uncontrolled, non-randomized and open-label study with measurements made with comparators, and it has a preset hypothesis for the primary endpoint. There are no similar devices to VitalThings Guardian M10 / M10 mobile on the market, consequently one or more different types of devices must be used as comparators.
There has been increasing use of venoarterial (VA) extracorporeal membrane oxygenation (ECMO) for infants with respiratory failure, up to 92% of neonatal respiratory support in 2021. This study seeks to leverage the increased use of VA ECMO in this cohort to enrich an evaluation of the differences in rate of intracranial hemorrhage and ischemic stroke between venovenous (VV) and VA ECMO among infants with respiratory failure where clinicians may choose either strategy. This project is a retrospective review of data in the ELSO registry.
The goal of this interventional study is to compare standard mechanical ventilation to a lung-stress oriented ventilation strategy in patients with Acute Respiratory Distress Syndrome (ARDS). Participants will be ventilated according to one of two different strategies. The main question the study hopes to answer is whether the personalized ventilation strategy helps improve survival.
This study targets adult patients treated with high flow nasal cannula (HFNC) at emergency department (ED) of Severance hospital, Yonsei university. Patients with acute hypoxic respiratory failure presenting to the ED receive conventional oxygen therapy as initial treatment unless immediate endotracheal intubation is required. Partial rebreathing oxygen masks are mainly applied at first. If the patient's condition does not improve despite such treatment, the patient receives HFNC or endotracheal intubation. However, possible treatment range have not been studied, especially in ED. Decisions are made based on the personal experience of the medical staff in charge. Applying HFNC to patients who eventually fail can lead to delayed intubation and increased mortality. Failure prediction models such as ROX index and HACOR score have been developed due to such reasons. However, such models are mostly based on intensive care unit studies and after application of HFNC. Therefore, failure prediction model at the time before application of HFNC and efficacy of existing models in ED are necessary. This study is a prospective observational study and follows the standard treatment guidelines applied to the patient and the judgment of the attending physician during the patient's treatment process. Immediately before applying HFNC, the patient's respiratory rate, pulse rate, blood pressure, SpO₂, PaO₂, PaCO₂, GCS score are determined, and FiO₂ is measured above upper lips using oxygen analyzer(MaxO2+AE, Maxtec, USA). From these data, ROX index (SF ratio/respiratory rate), ROX-HR (ROX index/pulse rate), POX index (PF ratio/respiratory rate), POX-HR (POX index/pulse rate), and HACOR score (Heart Rate, Acidosis, Consciousness, Oxygenation, Respiratory rate) are calculated. The settings (flow rate, FiO₂, temperature) at the time of HFNC application are also measured. The same indices and HFNC settings are checked 30 minutes, 1 hour, 2 hours, 4 hours, 6 hours, and 12 hours after applying HFNC. Modified Borg score and comfort scale using 5-point Likert scale are additionally determined at 30 minutes for patient's comfort. Primary outcome is HFNC failure at 28 days, defined by endotracheal intubation. Other outcomes include intubation in ED and mortality at 28 and 90 days collected through phone interview. The receiver operating curve for ROX index, HACOR score, ROX-HR, and POX-HR at baseline, 30 minutes, 1 hour, 2 hours, 4 hours, 6 hours, and 12 hours are drawn for the outcomes. The area under the curve of the above indices are compared and cutoff values are chosen with maximum value of index J by the Youden's Index. A binary variable is created based on the cutoff values and multivariable logistic regression analyses are performed. Cutoff values for maximum specificity are also invested suggesting the lower limit of the indicator to which HFNC can be applied.
The goal of this observational study is to establish an intelligent early warning system for acute and critical complications of the respiratory system such as pulmonary embolism and respiratory failure. Based on the electronic case database of the biomedical big data research center and the clinical real-world vital signs big data collected by wearable devices, the hybrid model architecture with multi-channel gated circulation unit neural network and deep neural network as the core is adopted, Mining the time series trends of multiple vital signs and their linkage change characteristics, integrating the structural nursing observation, laboratory examination and other multimodal clinical information to establish a prediction model, so as to improve patient safety, and lay the foundation for the later establishment of a higher-level and more comprehensive artificial intelligence clinical nursing decision support system. Issues addressed in this study 1. The big data of vital signs of patients collected in real-time by wearable devices were used to explore the internal relationship between the change trend of vital signs and postoperative complications (mainly including infection complications, respiratory failure, pulmonary embolism, cardiac arrest). Supplemented with necessary nursing observation, laboratory examination and other information, and use machine learning technology to build a prediction model of postoperative complications. 2. Develop the prediction model into software to provide auxiliary decision support for clinical medical staff, and lay the foundation for the later establishment of a higher-level and more comprehensive AI clinical decision support system.