Lead Extraction Imaging

Intracardiac Echocardiography During Transvenous Lead Extraction

Transvenous lead extraction is an invaluable procedure within the contemporary management of cardiac implantable electronic devices. Transvenous lead extraction has traditionally been guided by fluoroscopy. Complementary imaging with intracardiac echocardiography can provide valuable additional information, such as identification of complications, lead-adherent echodensities, and sites of lead-tissue adherence. As such, it can be used to aid in risk stratification before lead removal, help to choose tools or techniques, and provide visual monitoring throughout the procedure. Intracardiac echocardiography can be incorporated into the lead extraction workflow of the contemporary electrophysiologist and provide valuable information supporting safety and efficacy.

Assessment of cardiac implantable electric device lead perforation using a metal artifact reduction algorithm in cardiac computed tomography

PURPOSE

CT is considered the non-invasive gold standard for evaluating cardiac implantable electronic devices (CIEDs) lead perforation, but metal artifacts caused by the lead tip affect the image quality and make a definitive diagnosis challenging. We compared the performances of the metal artifact reduction (MAR) algorithm and the conventional algorithm for identification of the right ventricular (RV) lead tip position in cardiac CT studies of patients with CIEDs.

METHOD

Forty-seven consecutive patients (26 men; age 70.3 ± 15.4 years) with CIEDs underwent cardiac CT. Using the conventional and MAR algorithm, two image reconstructions were performed for each scan. We calculated the artifact index (AI) to assess the quantitative capability of the MAR algorithm for artifact reduction and visually assessed the RV lead tip position on both images as follows: non-perforation, perforation, and equivocal.

RESULTS

The mean AIs were significantly lower with the MAR algorithm than with the conventional algorithm (96.7 ± 40.1 HU vs. 284.6 ± 134.1 HU, P < 0.001). Thirteen (27.7 %) patients were diagnosed as equivocal using the conventional algorithm but were diagnosed with perforation (2 patients) and non-perforation (11 patients) using the MAR algorithm (equivocal rate: 27.7 % vs. 0%, P < 0.001). Using the MAR algorithm, all cases were diagnosed with perforation (6 patients, 12.8 %) or non-perforation (41 patients, 87.2 %).

CONCLUSIONS

The MAR algorithm effectively reduced metal artifacts and allowed us to diagnose the presence or absence of perforation in all cases, whereas definitive diagnosis was difficult with the use of conventional algorithm in 27.7 % of cases.

Algorithm for the analysis of pre-extraction computed tomographic images to evaluate implanted lead-lead interactions and lead-vascular attachments

BACKGROUND

The number of lead extractions is growing because of the greater population and increasing age of individuals with a cardiac implantable electronic device. Lead extraction procedures can be complex undertakings with risk of significant mortality, and vascular tears in the superior vena cava are of greatest concern.

OBJECTIVE

The purpose of this study was to study whether a novel algorithm that analyzes pre-extraction computed tomographic (CT) images can determine the likelihood and location of lead-lead interactions and lead-vessel attachment within patients' venous vasculatures. This information can be used to identify potential case challenges in the planning stages.

METHODS

We developed an algorithm to estimate the presence and position of lead-lead interactions and lead-vessel adherences by tracking distance between the leads and distance between the lead and superior vena cava in a sample of 12 patients referred to the United Heart and Vascular Clinic for lead extractions due to infection (n = 5), lead failure (n = 5), and tricuspid regurgitation (n = 2).

RESULTS

Preliminary results indicate that the developed algorithm successfully identified lead-lead and lead-vascular attachments compared to review of CT images by medical experts.

CONCLUSION

With future validation and clinical implementation, this algorithm could aid physician preparedness by minimizing intraprocedural emergencies and may improve patient outcomes.