Active clinical trials for "Respiratory Insufficiency"

This study aims to evaluate the use of POCUS to assess diaphragmatic function and its association with clinical outcomes in patients with respiratory failure who are admitted to the emergency department.

Determing optimal time of ventilator disconnection is a challenge for both acute and chronic neuromuscular disease. In one case it is helpful for weanning from ventilator and in the other to optimize daytime ventilation in the most severe patients. The investigators propose to validate a new non invasive tool for monitoring respiratroy parameters in neuromuscular patients in both acute and chronic conditions.

Patients with de novo respiratory failure undergoing non invasive ventilation (NIV) present failure rates of mechanical ventilation ranging from 4 to 50%. Causes for NIV treatment failure are various but the onset of septic shock and subsequent multi-organ failure (MOF) seem play a critical role. Recent data show that the 37% of patients admitted to intensive care unit for de novo respiratory failure without any other organ failure experience multiple organ failure within the first days from admission. Early identification of hypoxic patients at major risk for MOF seems critical. Physiological studies have demonstrated that the underlying mechanisms for organ damage preceding MOF are those involved in the oxygen consumption (VO2)/oxygen delivery (DO2) mismatch. Doppler ultrasound indices of renal arteria resistance are directly correlated tot he VO2/DO2 mismatch. With this study we aim at investigating the correlation between Doppler ultrasound indices of renal arteria resistance in patients with de novo respiratory failure admitted to ICU and the onset of MOF within the first 7 days form admission.

This study will use different indices for prediction of NIV failure in ARF patients Evaluation of HACOR score and ROX index for early prediction of NIV failure in patients with ARF. Study value of diaphragmatic dysfunction assessed by ultrasound as tool for prediction of success of NIV in ARF patients. Compare clinical significance of these scoring systems between hypoxemic and hypercapnic RF

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.

BACKGROUND: Endotracheal intubation and prolonged immobilization of patients receiving mechanical ventilation may reduce expectoration function. High frequency chest wall oscillation (HFCWO) may ameliorate airway secretion movement; however, the vigorous oscillation may influence ventilator settings and change instantaneous cardiopulmonary responses. The aim of this study was to investigate these issues. METHODS: Seventy-three patients aged >20 years who were intubated with mechanical ventilation for pneumonic respiratory failure were recruited and randomly classified into two groups (HFCWO group, n=36; and control group who received conventional chest physical therapy (CCPT), n=37). HFCWO was applied with a fixed protocol, while CCPT was conducted using standard protocols. Both groups received sputum suction after the procedure. Changes in ventilator settings and the subjects' responses were measured at pre-set intervals and compared within groups and between groups.

Purpose of the Study: (1) To classify an individual subject's ventilatory response in terms of respiratory depression to a bolus of remifentanil under normoxic and hyperoxic conditions. (2) Measurement of specific respiratory parameters to predict the opioid-induced respiratory depression (OIRD) response.

This study will enroll 75 mechanically vented adults in the ICU to compare self-reported ease of communication, patient satisfaction, and patient anxiety and frustration levels between a group using a communication application on an iPad and a group using standard methods of communication while mechanically vented.

Renal failure and resuscitation measures in critically ill patients can result in fluid overload. Fluid overload in renal failure patients can cause harmful effects like pulmonary edema, anasarca and congestive cardiac failure exacerbations among other complications. These have been associated with increased time on the ventilator, increased length of stay in the ICU, and higher overall mortality for patients requiring dialysis in the ICU. The current standard of care for adjusting fluid removal rates in patients on continuous renal replacement therapy relies on clinical judgement. Clinicians take into account factors like the patient's condition, vasopressor requirements, kidney function, total intake and outputs, vital signs, and physical examination findings when making daily changes to fluid removal rates on dialysis machines. Such assessment is highly subjective and can be imprecise/inaccurate leading to hypotension and hemodynamic instability in a critically ill patient. Use of conventional ultrasound by physicians to assess volume status using compressibility of the inferior vena cava has been shown to be a reliable predictor of volume status and can help guide therapy. Such use makes bedside volume assessment a non-invasive, rapid, repeatable point of care tool that can provide objective data to guide fluid removal determine velocity of fluid removal and help identify patients at risk of hypotension and hemodynamic instability during the process of fluid removal. Apart from rare possible local allergic reactions to ultrasound jelly and transient local discomfort, the disadvantages are minimal. Ultrasonography has been considered a safe imaging modality. This protocol will measure inferior vena cava compressibility using the General Electric VScan with Dual Probe, which has FDA approval for abdominal and vascular imaging in humans.

In the developed world critical illness is routinely treated in an intensive care unit (ICU) by highly specialized physicians, nurses and support staff. This model of intensive care is spreading rapidly to low and middle income countries and as it spreads, challenges and limitations to this model arise. In resource-poor settings, inadequate human resources, training, and equipment all present barriers to safe and effective use of life-saving procedures. The advances in medical informatics and human factors engineering have provided tremendous opportunity for novel and user-friendly clinical decision support (CDS) tools that can be applied in a complex and busy hospital setting. Real-time data feeds and standardized patient care tasks in a simulated acute care environment have been proven to have a significant advantage of a novel interface (compared to a conventional) in reducing provider cognitive load and errors. Currently researchers within the investigator's research group have developed and are pilot testing a simple electronic decision support tool: CERTAIN (Checklist for Early Recognition and Treatment of Acute Illness). This tool has been successfully tested and validated in simulated settings and is being implemented as pilot study in 18 countries. Worldwide infant and early childhood mortality continues to be very high partly due to the inability to recognize and respond aggressively to critical illnesses. Investigators expect that adaptation of the algorithms from CERTAIN has potential to be a powerful tool to improve on the medical care of children in developing countries. Investigators aim in this project is 1) to develop a pediatric adaptation of CERTAIN (CERTAINp) and 2) to implement it into clinical practice in resource-poor settings and evaluate the impact of the tool on the processes and patient outcomes.