Catheter Infection Clinical Trial
— DeepCathOfficial 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.
Status | Recruiting |
Enrollment | 1000 |
Est. completion date | December 31, 2023 |
Est. primary completion date | December 31, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | 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. |
Country | Name | City | State |
---|---|---|---|
France | CHU Clermont-Ferrand | Clermont-Ferrand | Puy-de-Dôme |
France | CHU Grenoble Alpes | Grenoble | Isère |
France | Hôpital Bichat - Claude-Bernard | Paris | Ile De France |
France | Réseau RéPIAS SPIADI | Tours |
Lead Sponsor | Collaborator |
---|---|
Outcome Rea | Assistance Publique - Hôpitaux de Paris, National Network Surveillance and Prevention of Infections Associated with Invasive Devices SPIADI, UNICANCER, Université Grenoble-Alpes, University Hospital, Clermont-Ferrand, University Hospital, Grenoble |
France,
Blot S, Ruppé 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 epidemio — View Citation
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. — View Citation
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. — View Citation
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. — View Citation
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; Dressi — View Citation
Timsit JF. Diagnosis and prevention of catheter-related infections. Curr Opin Crit Care. 2007 Oct;13(5):563-71. Review. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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 | 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. | through study completion, an average of 1 year | |
Secondary | 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. | through study completion, an average of 1 year | |
Secondary | 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. | through study completion, an average of 1 year |
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