Evaluating the Clinical Effectiveness of "Endotracheal Tube Position Anomaly Alerting System"
NCT ID: NCT06857019
Last Updated: 2025-03-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
200 participants
INTERVENTIONAL
2025-02-24
2025-12-31
Brief Summary
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The main questions it aims to answer are:
* Does ETPAAS reduce the duration of endotracheal tube malposition?
* What are healthcare workers' perspectives on the usability and satisfaction of ETPAAS?
Participants will receive standard care, with or without ETPAAS, in the intensive care unit.
Researchers will evaluate the duration of endotracheal tube malposition. Healthcare workers will complete a questionnaire assessing the usability and satisfaction of ETPAAS.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
CROSSOVER
OTHER
NONE
Study Groups
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ETPAAS
Participants receive standard of care, with "Endotracheal Tube Position Anomaly Alerting System" (ETPAAS), in the intensive care unit.
"Endotracheal Tube Position Anomaly Alerting System"
The "Endotracheal Tube Position Anomaly Alerting System" (ETPAAS) is designed to assist healthcare providers in managing patients receiving mechanical ventilation via an endotracheal tube. This system integrates an artificial intelligence (AI) algorithm that automatically evaluates the appropriateness of endotracheal tube positioning whenever a patient's chest X-ray is uploaded. If the system detects that the distance between the endotracheal tube tip and the carina falls outside the optimal range, it promptly alerts healthcare providers, enabling timely intervention to correct the tube position and enhance patient safety.
Control
Participants receive standard of care, without ETPAAS, in the intensive care unit.
No interventions assigned to this group
Interventions
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"Endotracheal Tube Position Anomaly Alerting System"
The "Endotracheal Tube Position Anomaly Alerting System" (ETPAAS) is designed to assist healthcare providers in managing patients receiving mechanical ventilation via an endotracheal tube. This system integrates an artificial intelligence (AI) algorithm that automatically evaluates the appropriateness of endotracheal tube positioning whenever a patient's chest X-ray is uploaded. If the system detects that the distance between the endotracheal tube tip and the carina falls outside the optimal range, it promptly alerts healthcare providers, enabling timely intervention to correct the tube position and enhance patient safety.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* At least 18 years old
Exclusion Criteria
ALL
No
Sponsors
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Kaohsiung Medical University Chung-Ho Memorial Hospital
OTHER
Responsible Party
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Principal Investigators
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Ming-Ju Tsai, M.D., Ph.D.
Role: STUDY_DIRECTOR
Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung Medical University
Locations
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Kaohsiung Medical University Chung-Ho Memorial Hospital
Kaohsiung City, , Taiwan
Countries
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Central Contacts
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Facility Contacts
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Ming-Ju Tsai, M.D., Ph.D.
Role: backup
Wei-An Chang, M.D., Ph.D.
Role: backup
Chih-Hung Cheng, M.D.
Role: backup
Chau-Chyun Sheu, M.D., Ph.D.
Role: backup
Other Identifiers
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KMUHIRB-F(I)-20240327
Identifier Type: -
Identifier Source: org_study_id
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