Eligibility for subcutaneous implantable cardioverter-defibrillator in congenital heart disease

被引:20
作者
Wang, Linda [1 ]
Javadekar, Neeraj [1 ]
Rajagopalan, Ananya [1 ]
Rogovoy, Nichole M. [1 ]
Haq, Kazi T. [1 ]
Broberg, Craig S. [1 ]
Tereshchenko, Larisa G. [1 ]
机构
[1] Oregon Hlth & Sci Univ, Knight Cardiovasc Inst, 3181 SW Sam Jackson Pk Rd,UHN62, Portland, OR 97239 USA
基金
美国国家卫生研究院;
关键词
Adult congenital heart disease; Electrocardiogram; Eligibility; Machine learning; Subcutaneous implantable cardioverter-defibrillator; POTENTIAL BARRIERS; ADULTS; ICD;
D O I
10.1016/j.hrthm.2020.01.016
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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 chi(2) 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.
引用
收藏
页码:860 / 869
页数:10
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