About this trial
Single center, open-label, phase IV clinical trial to study the application of radiomics in molecular imaging for diagnosis and follow-up of CVDIs. The study will include three populations:
* Retrospective cohort (2018-2025) * Prospective cohort (2026-2027) * Control group (prospective)
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Age ≥ 18 years.
Implanted cardiovascular device (prosthetic valve, TAVI, CIED, vascular graft/endograft).
For infected cohorts: patients studied by FDG-PET with definitive diagnosis of CVDI based on multidisciplinary evaluation.
For control group: no clinical suspicion of infection at time of inclusion.
Disqualifiers
Inability to undergo FDG-PET due to contraindications (e.g., allergies, claustrophobia).
Pregnancy or breastfeeding.
Incomplete clinical data or lack of access to imaging studies.
Refusal or withdrawal of informed consent (for control and prospective cohort).
Trial design
Single group
Treatments tested in this trial
PET/CT imaging
RadiationInclusion will occur after device implantation and informed consent signature. FDG-PET/CT imaging will be performed at approximately 1 month and 12 months post-implantation. These patients will not receive any specific treatment related to the study, beyond the procedures planned in the study schedule to record potential adverse events related to FDG administration and to facilitate the communication of imaging results. All medical management will otherwise follow standard clinical practice.
Treatment groups
Trial outcomes
Primary outcomes
To assess the diagnostic performance of radiomics applied to FDG-PET in patients with cardiovascular device infections (CVDIs), in comparison with standard visual and semiquantitative analysis.
Diagnostic accuracy of radiomics applied to FDG-PET for CVDIs, compared with conventional visual and semiquantitative interpretation.
Secondary outcomes
To evaluate the role of radiomics in monitoring suppressive antimicrobial therapy in patients with retained infected devices.
Performance of AI-based predictive models (combining radiomic and clinical features) for: * Distinguishing infection vs. non-infection. * Predicting resolution of infection and supporting discontinuation of suppressive antimicrobial therapy.
To identify normal 18F-FDG uptake patterns in cardiovascular devices in patients without suspected infection, stratified by sex and time from device implantation.
Characterization of physiological 18F-FDG uptake patterns in cardiovascular device carriers without infection, stratified by sex and time since implantation.
Sponsors and contacts
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