Mortality, Appropriate and Inappropriate Shocks - Observations from an Institutional Implantable Cardioverter Defibrillator Registry
Europace : European pacing, arrhythmias, and cardiac electrophysiology(2021)SCI 2区
University Hospital Centre Zagreb | Special Hospital for Medical Rehabilitation Krapinske Toplice | University of Zagreb School of Medicine
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Introduction Implantable cardioverter defibrillator (ICD) is an effective therapy for primary (PP) and secondary prevention (SP) of sudden cardiac death (SCD). ICD adverse events include inappropriate shocks (IS), device infection and failure. Methods We analysed the data concerning all newly implanted ICDs in our institution from 2011 to 2017. Follow-up data was collected until the end of 2019. Results In total, 507 ICDs were implanted (85.4% male, 57.6 ± 14.0 years-old), 375 (74.0%) for PP and 132 (26.0%) for SP. The mean follow-up was 34.3 ± 23.8 months. ICD delivered therapy in 42.4% of SP and in 28.8% of PP patients (p = 0.15). In PP, shocks were delivered in 25.7% of non-ischaemic heart disease (NIHD) and in 17.6% ischaemic heart disease (IHD) patients (p = 0.81). IS were significantly more common in NIHD patients (13.8% vs 2.4% in IHD group, p < 0.0001). PP patients with NIHD also had a higher shock burden (average of 8.0 ± 17.4 shocks compared to 2.7 ± 3.0 in the IHD group). However, it failed to reach the level of statistical significance (p = 0.052). In SP, the rate of ICD activation and that of IS were similar in both groups (IHD and NIHD). In total, 32.6% of SP patients received appropriate shock (AS) and 5.3% of them received at least one IS (average number of AS and IS being 8.7 ± 11.5 and 1.1 ± 0.4 respectively). Mortality was significantly higher in SP than in PP (34.8% vs 13.9%, p < 0.001). In PP, significantly more deaths occurred among IHD than NIHD patients (18.8% vs 10.0%, p < 0.001). Conclusion The prevalence of AS and IS was relatively higher than reported elsewhere. Same was true for mortality. Interestingly, the rate of IS was somewhat higher in NIHD than in IHD, which was unexpected. ICD outcomes Primary prevention Secondary prevention Total IHD NIHD Total IHD NIHD Patients, n 375 165 210 132 88 44 Patients with ICD activation, n (%) 108 (28.8) 46 (27.9) 62 (29.5) 56 (42.4) 33 (37.5) 22 (50.0) Patientns with AS, n (%) 60 (16.0) 27 (16.4) 33 (15.7) 43 (32.6) 29 (33.0) 14 (31.8) Patientns with IS, n (%) 33 (8.8) 4 (2.4) 29 (13.8) 7 (5.3) 5 (5.7) 2 (4.5) AS delivered (mean ± SD) 5.6 ± 13.3 2.7 ± 3.0 8.0 ± 17.4 8.7 ± 11.5 9.9 ± 12.2 9.7 ± 17.6 IS delivered (mean ± SD) 3.2 ± 5.1 1.2 ± 0.5 3.5 ± 5.4 1.1 ± 0.4 1.0 ± 0 3.2 ± 5.2 Deaths, n (%) 52 (13.9) 31 (18.8) 21 (10.0) 46 (34.8) 32 (36.4) 14 (31.8) Time to death (months, mean ± SD) 20.3 ± 13.9 19.9 ± 12.6 21.1 ± 16.5 27.1 ± 25.7 28.9 ± 24.9 22.6 ± 28.1 ICD, implantable cardioverter defibrillator; IHD, ischemic heart disease; NIHD, non-ischemic heart disease; AS, appropriate shock; IS, inappropriate shock
MoreTranslated text
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest