Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.
Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to find out novel analytical approaches to improve pre-implantation CRT response prediction. Among 31 pre-implantation features of clinical, laboratory, ele...
| Publicado en: | International Journal of Cardiovascular Imaging Vol. 35; no. 7; pp. 1221 - 1230 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137228659&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137228659 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15695794 1HHY jtl: International Journal of Cardiovascular Imaging issn: 15695794 maglogo: N pubinfo: dt: Jul2019 vid: 35 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137228659 137228659 NLM31104177 10.1007/s10554-019-01545-5 NLM31104177 137228659 ppf: 1221 ppct: 9 formats: tig: atl: Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography. aug: au: Lei, Juan Wang, Yi Grace Bhatta, Luna Ahmed, Jamal Fan, Dali Wang, Jingfeng Liu, Kan affil: Division of Cardiology, Department of Medicine, State University of New York, Upstate Medical University, 13202, Syracuse, NY, USA sug: subj: Echocardiography Methods Signal Processing, Computer Assisted Heart Failure Therapy Electrocardiography Methods Cardiac Resynchronization Therapy Heart Failure Image Interpretation, Computer Assisted Methods Heart Failure Physiopathology Treatment Outcomes Predictive Value of Tests Male Aged, 80 and Over Ventricular Remodeling Ventricular Function, Left Middle Age Aged Female Patient Selection Scales Aged, 80 & over Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to find out novel analytical approaches to improve pre-implantation CRT response prediction. Among 31 pre-implantation features of clinical, laboratory, electrocardiography (ECG), and echocardiography variables in a consecutive cohort of patients receiving a first-time CRT device (CRT-pacemaker or CRT-defibrillator), we developed a machine learning (ML) model with three classification algorithms (support vector machines (SVM), K nearest neighbors, and random subspaces) with the best features combination to predict CRT response. Three categorical variables, left bundle branch block (LBBB), nonischemic cardiomyopathy, and female gender, were independently associated with CRT responses. Among continuous variables, including septal wall thickness, posterior wall thickness, and relative wall thickness (RWT), could regularize ECG QRS duration (QRSd) and significantly enhance the correlation between QRSd and CRT response. The 3 ML algorithms in a total of 38 features combinations constantly recognized that the features combined with QRSd/RWT outperformed the combinations without it. For each of three algorithms, the triplet feature combination of QRSd/RWT, LBBB, and nonischemic cardiomyopathy repeatedly increased the classification rate more than 8%. The best performance for CRT response prediction occurred with SVM model, which proposed actual QRSd/RWT values that favored CRT responses in patients both with and without LBBB. Lower QRSd/RWT values were required for CRT responses in patients with ischemic cardiomyopathy compared to those with non-ischemic cardiomyopathy. ML from ventricular remodeling characteristics-regularized QRSd improves CRT response prediction. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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