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2 clinical studies listed.
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Tundra lists 2 Pulmonary Edemas clinical trials. Each listing includes eligibility criteria, study locations, and direct links to research sites in the Tundra directory.
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NCT07875764
Predictive Role of Lung Ultrasound in Re-Expansion Pulmonary Edema During Thoracocentesis
This study evaluates the feasibility, safety, and predictive role of dynamic bedside Lung Ultrasound (LUS) during therapeutic thoracocentesis in patients with moderate-to-massive pleural effusions. Re-expansion pulmonary edema (RPE) is a rare but potentially life-threatening complication of rapid pleural fluid drainage. In this single-arm pilot study, participants undergo therapeutic thoracocentesis guided by serial LUS assessments performed at predefined drainage volume milestones (T0: baseline, T1: 500 mL, T2: 1000 mL, T\_3: 1500 mL, and T End). Rather than relying solely on predetermined total drainage limits, procedure modification or early termination is dynamically guided by real-time ultrasound findings (such as coalescent B-lines and subpleural consolidations) alongside clinical monitoring. The main goal is to assess whether serial LUS monitoring can reliably detect early sonographic features of evolving pulmonary edema and inform clinical decision-making during pleural fluid removal.
Gender: All
Ages: 18 Years - Any
Updated: 2026-10-15
1 state
NCT07814118
Multimodal Physiologic State Intelligence Study
The OMNIPHYS study will investigate whether multimodal ultrasound and other routinely collected clinical and physiologic data can be combined with artificial intelligence to characterize changes in a person's physiologic state over time. The study will examine cardiac, vascular, pulmonary, and systemic physiologic measurements and develop longitudinal models that describe baseline physiology, physiologic perturbation, compensation, deterioration, treatment response, and recovery. The study is observational and will not assign experimental treatments. The goal is to determine whether changes in multimodal physiologic patterns can be identified and characterized earlier and more reliably than conventional single-time-point assessment.
Gender: All
Ages: 18 Years - Any
Updated: 2026-09-10
1 state