TY - JOUR
T1 - Eligibility for subcutaneous implantable cardioverter-defibrillator in congenital heart disease
AU - Wang, Linda
AU - Javadekar, Neeraj
AU - Rajagopalan, Ananya
AU - Rogovoy, Nichole M.
AU - Haq, Kazi T.
AU - Broberg, Craig S.
AU - Tereshchenko, Larisa
N1 - Funding Information:
This research was supported in part by National Institutes of Health Grant HL118277 to Dr Tereshchenko and the Boston-Scientific Center for the Advancement of Research to Dr Tereshchenko. This physician-initiated study (PI Tereshchenko) was partially supported by the Boston-Scientific Center for the Advancement of Research. The Boston Scientific company had no role in the design, execution, analyses, and interpretation of the data and results of this study. ClinicalTrials.gov Identifier: NCT03209726.
Funding Information:
This research was supported in part by National Institutes of Health Grant HL118277 to Dr Tereshchenko and the Boston-Scientific Center for the Advancement of Research to Dr Tereshchenko. This physician-initiated study (PI Tereshchenko) was partially supported by the Boston-Scientific Center for the Advancement of Research. The Boston Scientific company had no role in the design, execution, analyses, and interpretation of the data and results of this study. ClinicalTrials.gov Identifier: NCT03209726 .
Publisher Copyright:
© 2020 Heart Rhythm Society
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/5
Y1 - 2020/5
N2 - Background: Adult congenital heart disease (ACHD) patients can benefit from a subcutaneous implantable cardioverter-defibrillator (S-ICD). Objective: The purpose of this study was to assess left- and right-sided S-ICD eligibility in ACHD patients, use machine learning to predict S-ICD eligibility in ACHD patients, and transform 12-lead electrocardiogram (ECG) to S-ICD 3-lead ECG, and vice versa. Methods: ACHD outpatients (n = 101; age 42 ± 14 years; 52% female; 85% white; left ventricular ejection fraction [LVEF] 56% ± 9%) were enrolled in a prospective study. Supine and standing 12-lead ECG were recorded simultaneously with a right- and left-sided S-ICD 3-lead ECG. Peak-to-peak QRS and T amplitudes; RR, PR, QT, QTc, and QRS intervals; Tmax, and R/Tmax (31 predictor variables) were tested. Model selection, training, and testing were performed using supine ECG datasets. Validation was performed using standing ECG datasets and an out-of-sample non-ACHD population (n = 68; age 54 ± 16 years; 54% female; 94% white; LVEF 61% ± 8%). Results: Forty percent of participants were ineligible for S-ICD. Tetralogy of Fallot patients passed right-sided screening (57%) more often than left-sided screening (21%; McNemar χ2 P = .025). Female participants had greater odds of eligibility (adjusted odds ratio [OR] 5.9; 95% confidence interval [CI] 1.6–21.7; P = .008). Validation of the ridge models was satisfactory for standing left-sided (receiver operating characteristic area under the curve [ROC AUC] 0.687; 95% CI 0.582–0.791) and right-sided (ROC AUC 0.655; 95% CI 0.549–0.762) S-ICD eligibility prediction. Validation of transformation matrices showed satisfactory agreement (<0.1 mV difference). Conclusion: Nearly half of the contemporary ACHD population is ineligible for S-ICD. The odds of S-ICD eligibility are greater for female than for male ACHD patients. Machine learning prediction of S-ICD eligibility can be used for screening of S-ICD candidates.
AB - Background: Adult congenital heart disease (ACHD) patients can benefit from a subcutaneous implantable cardioverter-defibrillator (S-ICD). Objective: The purpose of this study was to assess left- and right-sided S-ICD eligibility in ACHD patients, use machine learning to predict S-ICD eligibility in ACHD patients, and transform 12-lead electrocardiogram (ECG) to S-ICD 3-lead ECG, and vice versa. Methods: ACHD outpatients (n = 101; age 42 ± 14 years; 52% female; 85% white; left ventricular ejection fraction [LVEF] 56% ± 9%) were enrolled in a prospective study. Supine and standing 12-lead ECG were recorded simultaneously with a right- and left-sided S-ICD 3-lead ECG. Peak-to-peak QRS and T amplitudes; RR, PR, QT, QTc, and QRS intervals; Tmax, and R/Tmax (31 predictor variables) were tested. Model selection, training, and testing were performed using supine ECG datasets. Validation was performed using standing ECG datasets and an out-of-sample non-ACHD population (n = 68; age 54 ± 16 years; 54% female; 94% white; LVEF 61% ± 8%). Results: Forty percent of participants were ineligible for S-ICD. Tetralogy of Fallot patients passed right-sided screening (57%) more often than left-sided screening (21%; McNemar χ2 P = .025). Female participants had greater odds of eligibility (adjusted odds ratio [OR] 5.9; 95% confidence interval [CI] 1.6–21.7; P = .008). Validation of the ridge models was satisfactory for standing left-sided (receiver operating characteristic area under the curve [ROC AUC] 0.687; 95% CI 0.582–0.791) and right-sided (ROC AUC 0.655; 95% CI 0.549–0.762) S-ICD eligibility prediction. Validation of transformation matrices showed satisfactory agreement (<0.1 mV difference). Conclusion: Nearly half of the contemporary ACHD population is ineligible for S-ICD. The odds of S-ICD eligibility are greater for female than for male ACHD patients. Machine learning prediction of S-ICD eligibility can be used for screening of S-ICD candidates.
KW - Adult congenital heart disease
KW - Electrocardiogram
KW - Eligibility
KW - Machine learning
KW - Subcutaneous implantable cardioverter-defibrillator
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U2 - 10.1016/j.hrthm.2020.01.016
DO - 10.1016/j.hrthm.2020.01.016
M3 - Article
C2 - 32354451
AN - SCOPUS:85083057564
VL - 17
SP - 860
EP - 869
JO - Heart Rhythm
JF - Heart Rhythm
SN - 1547-5271
IS - 5
ER -