POCUS AI in Critically Ill Patients
PocusAI-Crit
POCUS Artificial Intelligence With Portable and Ultraportable Ultrasound Devices in Critically Ill Patients
1 other identifier
observational
99
1 country
1
Brief Summary
This is a prospective study that aims to assess the differences in point-of-care ultrasound assessment (POCUS) with portable and ultra-portable devices, using conventional vs artificial intelligence (AI) methodologies, performed by experienced vs inexperienced physicians, in critically ill patients.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P50-P75 for all trials
Started Jul 2021
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
July 27, 2021
CompletedFirst Submitted
Initial submission to the registry
March 26, 2022
CompletedFirst Posted
Study publicly available on registry
April 20, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 29, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
June 11, 2022
CompletedSeptember 16, 2022
September 1, 2022
10 months
March 26, 2022
September 15, 2022
Conditions
Outcome Measures
Primary Outcomes (1)
Assess artificial intelligence ultrasound software to assess various echocardiographic variables useful for the management of critically ill patients
Assess the accuracy of automatic measurements to determine ultrasound parameters using portable versus ultra-portable ultrasound scanners. \- Assess the accuracy of POCUS performed by inexperienced professionals using portable versus ultraportable ultrasounds.
up to 48 weeks
Eligibility Criteria
Patients admitted to the intensive care department which, according to the clinical assessment of the medical team, would benefit from an ultrasound assessment.
You may qualify if:
- Patients aged 18 years or over, admitted to the intensive care department which, according to the clinical assessment of the medical team, would benefit from an ultrasound assessment.
You may not qualify if:
- Patients with atrial fibrillation or other dysrhythmias;
- Inappropriate acoustic window for ultrasound assessment.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Hospital Garcia de Orta
Lisbon, Almada, 2801-267, Portugal
Related Publications (1)
Varudo R, Gonzalez FA, Leote J, Martins C, Bacariza J, Fernandes A, Michard F. Machine learning for the real-time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography. Crit Care. 2022 Dec 14;26(1):386. doi: 10.1186/s13054-022-04269-6.
PMID: 36517906DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Target Duration
- 1 Day
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Senior consultant of Internal Medicine and Intensive Medicine, PhD student
Study Record Dates
First Submitted
March 26, 2022
First Posted
April 20, 2022
Study Start
July 27, 2021
Primary Completion
May 29, 2022
Study Completion
June 11, 2022
Last Updated
September 16, 2022
Record last verified: 2022-09
Data Sharing
- IPD Sharing
- Will not share