Identifying Local Signs at the Catheter Insertion Site With Artificial Intelligence

NCT05440396 | Unknown DMC FDA Device

• 1 other identifier: DeepCath
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 4

Brief Summary

Deepcath is the first step to the introduction of artificial intelligence in catheter care. A better use of visualisation of catheter exit site should be used not only by the HCWs but also by the patients and their family. 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.

Conditions

Catheter Infection

Keywords

catheter infection; deep learning; local signs

Outcome Measures

Primary Outcomes (1)

• The number of correct predictions for redness divided by the total number of predictions

  Overall classification accuracy of the learning model compared to the assessment of three independent medical experts on the detection of the presence of redness greater than or equal to 5 mm at the catheter insertion site.

  through study completion, an average of 1 year

Secondary Outcomes (3)

• The ratio of true positives and total positives predicted:

  through study completion, an average of 1 year

• The number of correct predictions for indurated venous cord divided by the total number of predictions

  through study completion, an average of 1 year

• The link between presence of local signs and infection

  through study completion, an average of 1 year

Study Arms (1)

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.

Diagnostic Test: Photographs collection phase

Interventions

Photographs collection phase DIAGNOSTIC_TEST

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.

Also known as: Photographs reviewing phase by medical experts, Development/Validation of artificial intelligence algorithm

Patient

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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.

You may qualify if:

• Patients over 18 years of age Patients with one or more implanted central venous, midline, piCCline, arterial, or peripheral catheters.
• 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

You may not qualify if:

• Patients presenting a peripheral identification sign close to the catheter insertion point cannot be masked when the photograph is taken. Thus, jewelry, clothing, tattoos, scars, and birthmarks are identifying features.
• Patients whose catheter insertion point is not visible.

Sponsors & Collaborators

• Outcome Rea: lead
• Assistance Publique - Hôpitaux de Paris: collaborator
• National Network Surveillance and Prevention of Infections Associated with Invasive Devices SPIADI: collaborator
• University Hospital, Clermont-Ferrand: collaborator
• University Hospital, Grenoble: collaborator
• UNICANCER: collaborator
• University Grenoble Alps: collaborator

Study Sites (4)

CHU Grenoble Alpes

Grenoble, Isère, 38000, France

RECRUITING

CHU Clermont-Ferrand

Clermont-Ferrand, Puy-de-Dôme, 63000, France

RECRUITING

Réseau RéPIAS SPIADI

Tours, France

RECRUITING

Hôpital Bichat - Claude-Bernard

Paris, Île-de-France Region, 75018, France

RECRUITING

Related Publications (6)

• 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.

  PMID: 19318651 RESULT

• Timsit JF. Diagnosis and prevention of catheter-related infections. Curr Opin Crit Care. 2007 Oct;13(5):563-71. doi: 10.1097/MCC.0b013e3282efa03f.

  PMID: 17762237 RESULT

• 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.

  PMID: 35624170 RESULT

• 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.

  PMID: 35249794 RESULT

• 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.

  PMID: 34920150 RESULT

• 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.

  PMID: 35659775 RESULT

MeSH Terms

Interventions

Growth and Development

Intervention Hierarchy (Ancestors)

Physiological Phenomena

Study Officials

• Jean-François TIMISIT, Pr

  Assistance Publique - Hôpitaux de Paris

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jean-François TIMSIT, Pr

CONTACT

+33140257703; jean-francois.timsit@aphp.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Month
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 21, 2022
• First Posted: June 30, 2022
• Study Start: September 1, 2022
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2023
• Last Updated: October 25, 2022

Record last verified: 2022-10

Data Sharing

• IPD Sharing: Will not share

Locations

FR (4)