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Catheter InfectionDeepCathNCT05440396

Identifying Local Signs at the Catheter Insertion Site With Artificial Intelligence

An observational study of Catheter Infection, run by Outcome Rea at 4 sites in France.

Unknown status

Status not confirmed. The sponsor has not verified this record for over two years, so it may be out of date.

Official record
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Trial 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. Read more about this study

Who is it for?
Ages 18 years and older
Conditions the study is for
Catheter Infection
What is being tested?
Photographs collection phase
Type of treatment
Pill or oral medicine
How many people
1,000 participants
Where
4 sitesFrance
Study dates
1 Sep 2022to 31 Dec 2023
How long will I take part?
1 Month
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • No healthy volunteers
  • Adult, Older adult

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.

Who may be able to join

  • 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

Who cannot join

  • 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.

Trial locations and contacts

4 sites in 1 country.

Jean-François TIMSIT, Pr contact

+33140257703

jean-francois.timsit@aphp.fr

France4 sites, 4 recruiting
  • CHU Clermont-Ferrand

    Clermont-Ferrand, Puy-de-Dôme, 63000

    Bertrand Souweine, bsouweine@chu-clermontferrand.fr

    Bertrand Souweine

    Recruiting
  • CHU Grenoble Alpes

    Grenoble, Isère, 38000

    Carole Schwebel, (+33)476768779, cschwebel@chu-grenoble.fr

    Carole Schwebel

    Recruiting
  • Hôpital Bichat - Claude-Bernard

    Paris, Île-de-France Region, 75018

    Jean-François Timsit, jean-francois.timsit@bch.aphp.fr

    Jean-François Timsit

    Recruiting
  • Réseau RéPIAS SPIADI

    Tours

    Nathalie VAN DER MEE, Pr, n.vandermee@chu-tours.fr

    Recruiting

Other trials in France

About this study

Official title: Development of an Artificial Intelligence Model of Image Recognition Through Images of Intravascular Catheters From Inpatients and Outpatients to Identify the Presence of Local Signs Associated With Infection

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.

Being tested
Photographs collection phase
Keywords
catheter infection, deep learning, local signs

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Photographs collection phaseDiagnostic test

    Also called Photographs reviewing phase by medical experts, Development/Validation of artificial intelligence algorithm

    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.

Study groups in NCT05440396
Study groupRoleWhat they receive
PatientPatients 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.
Receives: Diagnostic Test: Photographs collection phase

What is being measured

The primary outcome is the main question the study is designed to answer.

  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.

    Measured: through study completion, an average of 1 year

Secondary outcomes (3)
  1. The ratio of true positives and total positives predicted:

    The precision metric focuses on Type-I errors(FP). A Type-I error occurs when we reject a true null Hypothesis.

    Measured: through study completion, an average of 1 year

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

    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.

    Measured: through study completion, an average of 1 year

  3. The link between presence of local signs and infection

    Measure the correlation between the appearance of the catheter puncture site and the presence of signs consistent with local and systemic infection.

    Measured: through study completion, an average of 1 year

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted30 Jun 2022
  2. Study start1 Sep 2022
  3. Primary completion31 Dec 2023 (estimated)
  4. Study completion31 Dec 2023 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
1,000 participants (estimated)
Target duration
1 Month
IDs, dates and publications
ClinicalTrials.gov ID
NCT05440396
Sponsor's protocol ID
DeepCath
Acronym
DeepCath
Status last verified
Oct 2022
First submitted
21 Jun 2022
Last updated
25 Oct 2022
Results posted
No
Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
Yes
Individual data sharing
No

Publications

  • 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.

    PubMed 19318651
  • Timsit JF. Diagnosis and prevention of catheter-related infections. Curr Opin Crit Care. 2007 Oct;13(5):563-71. doi: 10.1097/MCC.0b013e3282efa03f.

    PubMed 17762237
  • 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.

    PubMed 35624170
  • 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.

    PubMed 35249794
  • 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.

    PubMed 34920150
  • 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.

    PubMed 35659775

Questions about this trial

What is this clinical trial studying?

An observational study of Catheter Infection, run by Outcome Rea at 4 sites in France.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: status not confirmed, as of the sponsor's last update on 25 Oct 2022. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 4 sites in France. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Results are not available in the source record.

How can a doctor refer a patient or contact the research team?

Contact Jean-François TIMSIT, Pr (+33140257703, jean-francois.timsit@aphp.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

The number of correct predictions for redness divided by the total number of predictions (time frame: through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT05440396, which also lists 6 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Oct 2022.

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