Literature DB >> 35551558

Deep learning-based insights on T:R ratio behaviour during prolonged screening for S-ICD eligibility.

Mohamed ElRefai1,2, Mohamed Abouelasaad3, Benedict M Wiles4, Anthony J Dunn5, Stefano Coniglio5, Alain B Zemkoho5, Paul R Roberts3,6.   

Abstract

BACKGROUND: A major predictor of eligibility of subcutaneous implantable cardiac defibrillators (S-ICD) is the T:R ratio. The eligibility cut-off of the T:R ratio incorporates a safety margin to accommodate for fluctuations of ECG signal amplitudes. We introduce a deep learning-based tool that accurately measures the degree of T:R ratio fluctuations and explore its role in S-ICD screening.
METHODS: Patients were fitted with Holters for 24 h to record their S-ICD vectors. Our tool was used to assess the T:R ratio over the duration of the recordings. Multiple T:R ratio cut-off values were applied, identifying patients at high risk of T-wave oversensing (TWO) at each of the proposed values. The purpose of our study is to identify the ratio that recognises patients at high risk of TWO while not inappropriately excluding true S-ICD candidates.
RESULTS: Thirty-seven patients (age 54.5 + / - 21.3 years, 64.8% male) were recruited. Fourteen patients had heart-failure, 7 hypertrophic cardiomyopathy, 7 had normal hearts, 6 had congenital heart disease, and 3 had prior inappropriate S-ICD shocks due to TWO. 54% of patients passed the screening at a T: R of 1:3. All patients passed the screening at a T: R of 1:1. The only subgroup to wholly pass the screening utilising all the proposed ratios are the participants with normal hearts.
CONCLUSION: We propose adopting prolonged screening to select patients eligible for S-ICD with low probability of TWO and inappropriate shocks. The appropriate T:R ratio likely lies between 1:3 and 1:1. Further studies are required to identify the optimal screening thresholds.
© 2022. The Author(s).

Entities:  

Keywords:  Artificial intelligence; Cardiac devices; Deep learning methods; Subcutaneous implantable cardiac defibrillator

Year:  2022        PMID: 35551558     DOI: 10.1007/s10840-022-01245-6

Source DB:  PubMed          Journal:  J Interv Card Electrophysiol        ISSN: 1383-875X            Impact factor:   1.900


  11 in total

1.  Prevalence of subcutaneous implantable cardioverter-defibrillator candidacy based on template ECG screening in patients with hypertrophic cardiomyopathy.

Authors:  Niccolo' Maurizi; Iacopo Olivotto; Louise R A Olde Nordkamp; Katia Baldini; Carlo Fumagalli; Tom F Brouwer; Reinoud E Knops; Franco Cecchi
Journal:  Heart Rhythm       Date:  2015-09-08       Impact factor: 6.343

2.  Eligibility for subcutaneous implantable cardioverter-defibrillator in congenital heart disease.

Authors:  Linda Wang; Neeraj Javadekar; Ananya Rajagopalan; Nichole M Rogovoy; Kazi T Haq; Craig S Broberg; Larisa G Tereshchenko
Journal:  Heart Rhythm       Date:  2020-05       Impact factor: 6.343

3.  Eligibility for subcutaneous implantable cardioverter defibrillators in the adult congenital heart disease population.

Authors:  Hannah Garside; Francisco Leyva; Lucy Hudsmith; Howard Marshall; Joseph de Bono
Journal:  Pacing Clin Electrophysiol       Date:  2018-12-04       Impact factor: 1.976

4.  Head-to-head comparison of arrhythmia discrimination performance of subcutaneous and transvenous ICD arrhythmia detection algorithms: the START study.

Authors:  Michael R Gold; Dominic A Theuns; Bradley P Knight; J Lacy Sturdivant; Rick Sanghera; Kenneth A Ellenbogen; Mark A Wood; Martin C Burke
Journal:  J Cardiovasc Electrophysiol       Date:  2011-10-28

5.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks.

Authors:  Serkan Kiranyaz; Turker Ince; Moncef Gabbouj
Journal:  IEEE Trans Biomed Eng       Date:  2015-08-14       Impact factor: 4.538

Review 6.  The subcutaneous implantable cardioverter-defibrillator in review.

Authors:  Nicholas J Kamp; Sana M Al-Khatib
Journal:  Am Heart J       Date:  2019-08-17       Impact factor: 4.749

7.  Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection.

Authors:  Wenhan Liu; Mengxin Zhang; Yidan Zhang; Yuan Liao; Qijun Huang; Sheng Chang; Hao Wang; Jin He
Journal:  IEEE J Biomed Health Inform       Date:  2017-11-10       Impact factor: 5.772

8.  Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings.

Authors:  Xiaomao Fan; Qihang Yao; Yunpeng Cai; Fen Miao; Fangmin Sun; Ye Li
Journal:  IEEE J Biomed Health Inform       Date:  2018-08-07       Impact factor: 5.772

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  1 in total

1.  Dynamic screening for S-ICD eligibility: one frame does not capture the whole story.

Authors:  Auroa Badin
Journal:  J Interv Card Electrophysiol       Date:  2022-06-11       Impact factor: 1.900

  1 in total

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