Identifying Local Signs at the Catheter Insertion Site With Artificial Intelligence

Recruiting

• Conditions: Catheter Infection

• Registration Number: NCT05440396
• Lead Sponsor: Outcome Rea

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2022-06-21
• Study First Submitted QC: 2022-06-27
• Study First Posted: 2022-06-30
• Study Start: 2022-09-01
• Status Verified: 2022-10
• Last Update Submitted: 2022-10-24
• Last Update Posted: 2022-10-25
• Primary Completion: 2023-12-31
• Study Completion: 2023-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

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

Exclusion Criteria

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The number of correct predictions for redness divided by the total number of predictions	through study completion, an average of 1 year	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.

Secondary Outcome Measures

Name	Time	Method
The number of correct predictions for indurated venous cord divided by the total number of predictions	through study completion, an average of 1 year	Evaluate, on the basis of images of peripheral catheter insertion sites, the reliability of the learning model on the assessment of the presence of indurated venous cord.
The link between presence of local signs and infection	through study completion, an average of 1 year	Measure the correlation between the appearance of the catheter puncture site and the presence of signs consistent with local and systemic infection.
The ratio of true positives and total positives predicted:	through study completion, an average of 1 year	The precision metric focuses on Type-I errors(FP). A Type-I error occurs when we reject a true null Hypothesis.

Trial Locations

Locations (4)

Hôpital Bichat - Claude-Bernard; 🇫🇷 Paris, Ile De France, France

CHU Grenoble Alpes; 🇫🇷 Grenoble, Isère, France

CHU Clermont-Ferrand; 🇫🇷 Clermont-Ferrand, Puy-de-Dôme, France

Réseau RéPIAS SPIADI; 🇫🇷 Tours, France