Identifying Local Signs at the Catheter Insertion Site With Artificial Intelligence
NCT ID: NCT05440396
Last Updated: 2022-10-25
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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UNKNOWN
1000 participants
OBSERVATIONAL
2022-09-01
2023-12-31
Brief Summary
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A deep learning system able to detect visual abnormalities of the catheter exit site will be an helpful tools to develop a continuous follow-up of intravascular catheters.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Patient
Patients will be recruited independently in each of the centers participating in the study, according to the specific organization of each service (hospital and home). Patients will be selected based on the study inclusion/non-inclusion criteria. The included patients will receive oral information about the study. The informed consent will be obtained before any investigation.
Each photograph will focus on the intravascular catheter. The investigators will respect the main non-inclusion criterion: not to show any peripheral identification sign close to the insertion point of the catheter that cannot be masked when the photograph is taken. In this context, jewellery, clothing, tattoos, scars, and birthmarks represent identifying features. Individual access accounts with secure, randomly generated passwords will be provided to investigators.
The photographs will be anonymous, and any identification of the person concerned will be impossible.
Photographs collection phase
Three medical experts have been selected to review the photo collected. Each expert medical assesses the presence of local signs of infection on the photographs by annotating them directly via a dedicated software. They will annotate local signs: redness, perfusion extravasation, necrosis, hematoma, edema, non-purulent discharge, and purulent discharge.
A convolutional neural network model will determine the probability of local sign presence. Each picture will be annotated to determine the main characteristics of the catheter. A dataset preparation with photo cropping will be performed for modelling.
Interventions
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Photographs collection phase
Three medical experts have been selected to review the photo collected. Each expert medical assesses the presence of local signs of infection on the photographs by annotating them directly via a dedicated software. They will annotate local signs: redness, perfusion extravasation, necrosis, hematoma, edema, non-purulent discharge, and purulent discharge.
A convolutional neural network model will determine the probability of local sign presence. Each picture will be annotated to determine the main characteristics of the catheter. A dataset preparation with photo cropping will be performed for modelling.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
Patient and/or trusted person and/or family who have verbally stated their non-objection to the study Patient affiliated or beneficiary of a social security plan
Exclusion Criteria
Patients whose catheter insertion point is not visible.
18 Years
ALL
No
Sponsors
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Assistance Publique - Hôpitaux de Paris
OTHER
National Network Surveillance and Prevention of Infections Associated with Invasive Devices SPIADI
UNKNOWN
University Hospital, Clermont-Ferrand
OTHER
University Hospital, Grenoble
OTHER
UNICANCER
OTHER
University Grenoble Alps
OTHER
Outcome Rea
OTHER
Responsible Party
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Principal Investigators
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Jean-François TIMISIT, Pr
Role: PRINCIPAL_INVESTIGATOR
Assistance Publique - Hôpitaux de Paris
Locations
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CHU Grenoble Alpes
Grenoble, Isère, France
CHU Clermont-Ferrand
Clermont-Ferrand, Puy-de-Dôme, France
Réseau RéPIAS SPIADI
Tours, , France
Hôpital Bichat - Claude-Bernard
Paris, Île-de-France Region, France
Countries
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Central Contacts
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Facility Contacts
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References
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Timsit JF, Schwebel C, Bouadma L, Geffroy A, Garrouste-Orgeas M, Pease S, Herault MC, Haouache H, Calvino-Gunther S, Gestin B, Armand-Lefevre L, Leflon V, Chaplain C, Benali A, Francais A, Adrie C, Zahar JR, Thuong M, Arrault X, Croize J, Lucet JC; Dressing Study Group. Chlorhexidine-impregnated sponges and less frequent dressing changes for prevention of catheter-related infections in critically ill adults: a randomized controlled trial. JAMA. 2009 Mar 25;301(12):1231-41. doi: 10.1001/jama.2009.376.
Timsit JF. Diagnosis and prevention of catheter-related infections. Curr Opin Crit Care. 2007 Oct;13(5):563-71. doi: 10.1097/MCC.0b013e3282efa03f.
Buetti N, Rickard CM, Timsit JF. Catheter dressings. Intensive Care Med. 2022 Aug;48(8):1066-1068. doi: 10.1007/s00134-022-06734-w. Epub 2022 May 27. No abstract available.
Blot S, Ruppe E, Harbarth S, Asehnoune K, Poulakou G, Luyt CE, Rello J, Klompas M, Depuydt P, Eckmann C, Martin-Loeches I, Povoa P, Bouadma L, Timsit JF, Zahar JR. Healthcare-associated infections in adult intensive care unit patients: Changes in epidemiology, diagnosis, prevention and contributions of new technologies. Intensive Crit Care Nurs. 2022 Jun;70:103227. doi: 10.1016/j.iccn.2022.103227. Epub 2022 Mar 3.
Buetti N, Mermel LA, Timsit JF. Routine catheter-tip cultures for assessing catheter-related bloodstream infections in randomised-controlled trials. Anaesth Crit Care Pain Med. 2022 Feb;41(1):101006. doi: 10.1016/j.accpm.2021.101006. Epub 2021 Dec 14. No abstract available.
Buetti N, Abbas M, Pittet D, Chraiti MN, Sauvan V, De Kraker MEA, Boisson M, Teixeira D, Zingg W, Harbarth S. Lower risk of peripheral venous catheter-related bloodstream infection by hand insertion. Antimicrob Resist Infect Control. 2022 Jun 3;11(1):80. doi: 10.1186/s13756-022-01117-8.
Other Identifiers
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DeepCath
Identifier Type: -
Identifier Source: org_study_id
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