Using artificial intelligence and deep learning to optimise the selection of adult congenital heart disease patients in S-ICD screening
Using artificial intelligence and deep learning to optimise the selection of adult congenital heart disease patients in S-ICD screening
Introduction: The risk of complications associated with transvenous ICDs make the subcutaneous implantable cardiac defibrillator (S-ICD) a valuable alternative in patients with adult congenital heart disease (ACHD). However, higher S-ICD ineligibility and higher inappropriate shock rates-mostly caused by T wave oversensing (TWO)- are observed in this population. We report a novel application of deep learning methods to screen patients for S-ICD eligibility over a longer period than conventional screening. Methods: Adult patients with ACHD and a control group of normal subjects were fitted with a 24-h Holters to record their S-ICD vectors. Their T:R ratio was analysed utilising phase space reconstruction matrices and a deep learning-based model to provide an in-depth description of the T: R variation plot for each vector. T: R variation was compared statistically using t-test. Results: 13 patients (age 37.4 ± 7.89 years, 61.5 % male, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the two groups (p < 0.001). There was also a significant difference in the standard deviation of T: R between both groups (p = 0.04). Conclusions: T:R ratio, a main determinant for S-ICD eligibility, is significantly higher with more tendency to fluctuate in ACHD patients when compared to a population with normal hearts. We hypothesise that our novel model could be used to select S-ICD eligible patients by better characterisation of T:R ratio, reducing the risk of TWO and inappropriate shocks in the ACHD patient cohort.
Adult congenital heart disease, Cardiac implantable devices, Deep learning, S-ICD
192-199
ElRefai, Mohamed H.
c54af0dd-d0a1-478d-957a-cb485c19417c
Abouelasaad, Mohamed
62c5bd28-9c5f-4287-8b63-b25b2a2b7966
Conibear, Isobel
ea58a83b-d175-44dd-9669-2434d7358bac
Wiles, Benedict Mark
a42ba978-24c3-4533-8eca-498102004477
Dunn, Anthony James
161d9c8e-6813-4909-95ea-6c11bbbca287
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Zemkoho, Alain
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Morgan, John
7bd04ada-ca61-4a2c-b1cf-1750ffa9d89c
Roberts, Paul R.
193431e8-f9d5-48d6-8f62-ed9052b2571d
11 June 2024
ElRefai, Mohamed H.
c54af0dd-d0a1-478d-957a-cb485c19417c
Abouelasaad, Mohamed
62c5bd28-9c5f-4287-8b63-b25b2a2b7966
Conibear, Isobel
ea58a83b-d175-44dd-9669-2434d7358bac
Wiles, Benedict Mark
a42ba978-24c3-4533-8eca-498102004477
Dunn, Anthony James
161d9c8e-6813-4909-95ea-6c11bbbca287
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Zemkoho, Alain
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Morgan, John
7bd04ada-ca61-4a2c-b1cf-1750ffa9d89c
Roberts, Paul R.
193431e8-f9d5-48d6-8f62-ed9052b2571d
ElRefai, Mohamed H., Abouelasaad, Mohamed, Conibear, Isobel, Wiles, Benedict Mark, Dunn, Anthony James, Coniglio, Stefano, Zemkoho, Alain, Morgan, John and Roberts, Paul R.
(2024)
Using artificial intelligence and deep learning to optimise the selection of adult congenital heart disease patients in S-ICD screening.
Indian Pacing and Electrophysiology Journal, 24 (4), .
(doi:10.1016/j.ipej.2024.06.003).
Abstract
Introduction: The risk of complications associated with transvenous ICDs make the subcutaneous implantable cardiac defibrillator (S-ICD) a valuable alternative in patients with adult congenital heart disease (ACHD). However, higher S-ICD ineligibility and higher inappropriate shock rates-mostly caused by T wave oversensing (TWO)- are observed in this population. We report a novel application of deep learning methods to screen patients for S-ICD eligibility over a longer period than conventional screening. Methods: Adult patients with ACHD and a control group of normal subjects were fitted with a 24-h Holters to record their S-ICD vectors. Their T:R ratio was analysed utilising phase space reconstruction matrices and a deep learning-based model to provide an in-depth description of the T: R variation plot for each vector. T: R variation was compared statistically using t-test. Results: 13 patients (age 37.4 ± 7.89 years, 61.5 % male, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the two groups (p < 0.001). There was also a significant difference in the standard deviation of T: R between both groups (p = 0.04). Conclusions: T:R ratio, a main determinant for S-ICD eligibility, is significantly higher with more tendency to fluctuate in ACHD patients when compared to a population with normal hearts. We hypothesise that our novel model could be used to select S-ICD eligible patients by better characterisation of T:R ratio, reducing the risk of TWO and inappropriate shocks in the ACHD patient cohort.
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Accepted/In Press date: 10 June 2024
e-pub ahead of print date: 11 June 2024
Published date: 11 June 2024
Additional Information:
Publisher Copyright:
© 2024 Indian Heart Rhythm Society
Keywords:
Adult congenital heart disease, Cardiac implantable devices, Deep learning, S-ICD
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Local EPrints ID: 491172
URI: http://eprints.soton.ac.uk/id/eprint/491172
PURE UUID: edb43ff3-9aec-40f3-a6e9-7d38d74da253
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Date deposited: 13 Jun 2024 17:13
Last modified: 09 Aug 2024 01:50
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Contributors
Author:
Mohamed H. ElRefai
Author:
Mohamed Abouelasaad
Author:
Isobel Conibear
Author:
Benedict Mark Wiles
Author:
Anthony James Dunn
Author:
Paul R. Roberts
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