Regional variation in cardiac implantable electronic device implants trends in New Zealand over the past decade (ANZACS-QI 54)

Transvenous Lead Extraction: Outcomes From a Single Centre Providing a National Service for New Zealand

BACKGROUND

With increasing demand for cardiac implantable electronic devices there is a parallel increase in the need for transvenous lead extraction (TLE). Due to its small population, all TLE procedures in New Zealand are currently performed in a single centre, Auckland City Hospital. We analysed the clinical characteristics and outcomes of those undergoing TLE since this service was established.

METHODS

We performed a retrospective, single-centre cohort study of all TLE procedures between October 2015 and December 2021. Definitions from the European Lead Extraction Controlled study, Heart Rhythm Society, European Heart Rhythm Association consensus documents were used.

RESULTS

A total of 247 patients had 480 leads extracted, averaging 40 TLE procedures annually. Patients had a median lead dwell time of 6 (interquartile range [IQR] 3-11) years, 60 (13%) of leads had been in-situ >15 years, median age 61 (IQR 48-70) years, 73 (30%) female, 28 (11%) Māori, 23 (9%) Pasifika. Lead dysfunction (115 patients, 47%) and infection (90 patients, 37%) were the most common indications for TLE. Complete clinical and radiological success was achieved for 96% and 95%, respectively. Procedure-related complications occurred in 16 (7%) patients. Major intra-procedure complications occurred in 5 patients (2%), including 2 (1%) deaths. Death within one year of TLE occurred in 13 (26%) with systemic infection, 5 (3%) with local infection, and 5 (3%) with non-infection indications for TLE, p <0.01.

CONCLUSIONS

TLE is associated with high radiographic and clinical success, low complication, and low mortality rate. At our single centre providing a national service, TLE outcomes are comparable with those achieved internationally.

Automatic 3D Surface Reconstruction of the Left Atrium From Clinically Mapped Point Clouds Using Convolutional Neural Networks

Point clouds are a widely used format for storing information in a memory-efficient and easily manipulatable representation. However, research in the application of point cloud mapping and subsequent organ reconstruction with deep learning, is limited. In particular, current methods for left atrium (LA) visualization using point clouds recorded from clinical mapping during cardiac ablation are proprietary and remain difficult to validate. Many clinics rely on additional imaging such as MRIs/CTs to improve the accuracy of LA mapping. In this study, for the first time, we proposed a novel deep learning framework for the automatic 3D surface reconstruction of the LA directly from point clouds acquired via widely used clinical mapping systems. The backbone of our framework consists of a 30-layer 3D fully convolutional neural network (CNN). The architecture contains skip connections that perform multi-resolution processing to maximize information extraction from the point clouds and ensure a high-resolution prediction by combining features at different receptive levels. We used large kernels with increased receptive fields to address the sparsity of the point clouds. Residual blocks and activation normalization were further implemented to improve the feature learning on sparse inputs. By utilizing a light-weight design with low-depth layers, our CNN took approximately 10 s per patient. Independent testing on two cross-modality clinical datasets showed excellent dice scores of 93% and surface-to-surface distances below 1 pixel. Overall, our study may provide a more efficient, cost-effective 3D LA reconstruction approach during ablation procedures, and potentially lead to improved treatment of cardiac diseases.