View clinical trials related to Vital Signs.
Filter by:Study hypothesis: Machine Learning algorithms and techniques previously developed for use in the robotics field can be applied to the field of medicine. These state-of-the-art, feature extraction and machine learning techniques can utilize patient vital sign data from bedside monitors to discover hidden relationships within the physiological waveforms and identify physiological trends or concerning conditions that are predictive of various clinical events. These algorithms could potentially provide preemptive alerts to clinicians of a developing patient problem, well before any human could detect a worrisome combination of events or trend in the data. Specific aims: 1. Collect physiological waveform and numeric trend data from patient vital signs monitors in ICUs at the University of Colorado Hospital and Children's Hospital Colorado. 2. Combine the physiological data from patient monitors with clinical data obtained from patient Electronic Medical Records including IV fluids, medications, ventilator settings, urine output, etc. for use in developing models of various clinical conditions. 3. Apply Machine Learning techniques to these models to identify physiological waveform features and trend information, which are characteristic and predictive of common clinical conditions including but not limited to: - Post-operative atrial fibrillation and other cardiac dysrhythmias - Post-operative cardiac tamponade - Tension pneumothorax - Optimal post-operative and post-resuscitation fluid needs - Intracranial hypertension and cerebral perfusion pressure
Title: The acute effect of water pipe smoking on exhaled nitric oxide (eNO) and exhaled breath condensate (EBC) pulmonary function tests in healthy volunteers Objectives: To evaluate the acute effect of one cession of water pipe smoking on airway inflammation as assessed by exhaled nitric oxide (eNO) and exhaled breath condensate (EBC) in healthy volunteers. Design: Prospective study evaluating these parameters before and after 30 minutes of water pipe smoking . The changes in inflammatory parameters pre and post smoking will be evaluated blindly. Sample size: 100 participants Participant selection: Adults subjects who regularly smoke water pipe . Intervention: Each subject will undergo evaluation including a respiratory questionnaire , pulmonary function tests , exhaled nitric oxide (eNO) and exhaled breath condensate (EBC) and carboxy- hemoglobin levels . All measurements will be evaluated before and after one cession of 30 minutes water pipe smoking Primary outcome parameter: Change in carboxy- hemoglobin Secondary outcome parameter:Change in peripheral eosinophils count, pulmonary function tests, change in FeNO, and in inflammatory parameters in EBC before and after water pipe smoking