Non-invasive Detection of Driveline Infections in Patients with a Left Ventricular Assist Device

Status

Enrolling by invitation

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06867887

Acronym

DRIVE-ID

Enrollment

Registered

2025-03-10

Start date

2024-02-12

Completion date

2026-12-31

Last updated

2025-03-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

LVAD (Left Ventricular Assist Device) Driveline Infection

Keywords

Infection, LVAD, Detection, Monitoring, Driveline infection, Imaging, Non-invasive

Brief summary

The aim of this single center observational study is to determine the feasibility of using non-invasive imaging methods, including smartphone photography and infrared thermography, for detecting of DLIs in LVAD patients in terms of severity, extent and natural healing process.

Detailed description

Observational data of the driveline exit of LVAD patients will be collected during a follow-up period of 26 weeks. Two non-invasive imaging methods will be used. Smartphone photos will be taken weekly by the patient during routine wound care in the home environment. In case the patient is admitted for driveline infection, infrared thermographic (IRT) photography will be used to make thermographic photos of the driveline exit and the abdominal area of the subcutaneous driveline. Furthermore, existing smartphone images and diagnostic data regarding prior DLI status will be obtained from the electronic patient records. Imaging data will additionally be retrospectively analyzed using artificial intelligence (AI) and machine learning for the development of a predictive AI model.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

16 Years to 99 Years

Healthy volunteers

Inclusion criteria

Patients age 16 years or older, implanted with an LVAD, followed at Erasmus MC, with access to a smartphone with a built-in camera, who have signed an informed consent for data collection.

Exclusion criteria

Known cognitive problems, like dementia etc., non-cardiac disease or cardiac diseases resulting in a life expectancy less than 1 years, inability to read or sign the informed consent form.

Design outcomes

Primary

Measure	Time frame	Description
Extent and severity of driveline infections in LVAD patients using non-invasive imaging	26 weeks	Assess the extent, severity, and healing process of LVAD driveline infections in patients on LVAD support
Driveline exit healing process and risk of infection of the LVAD driveline	26	Assess the healing process of the driveline exit using non-invasive imaging (smartphone and thermographic)

Secondary

Measure	Time frame	Description
Sceptic complications	26 weeks	Occurance of systemic infection, positive blood cultures, and VAD-related infections.
Machine learning model for predicting DLIs.	26 weeks	Assess whether a machine learning model can be developed and validated based on smartphone photography and IRT to predict the occurrence of DLIs.

Countries

Netherlands

Outcome results