Abstract 4139637: Artificial Intelligence ECG Mapping of Biventricular Pacing QRS Morphology Predicts Response to Cardiac Resynchronization Therapy
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Le résumé fourni par la source
Background: Cardiac resynchronization therapy (CRT) is an important treatment modality for patients with heart failure and electrical dyssynchrony. A current limitation is that approximately 30% of patients do not respond, and identification of patients at greater risk for suboptimal outcome remains challenging. Hypothesis: We hypothesized that an artificial intelligence (AI) ECG mapping system would identify maladaptive ECG activation patterns in non-responders to CRT-defibrillator (CRT-D) therapy. Aims: We endeavored to analyze the biventricular-paced 12-lead ECG QRS complex using a novel AI tool and compare the projected segment of origin between responders and non-responders. Methods: We enrolled patients with heart failure 6 months or more after CRT-D implantation. ECG samples obtained during routine clinical follow-up during LV, RV, and BiV pacing were analyzed with an AI-based ECG mapping system. The projected segments of origin for each pacing setting were noted using a validated 30 segment ventricular model. CRT responders were defined as ≥5% increase in LVEF at 6 to 12 months post-implantation. Results: Of 49 study patients, 35 were responders (age 74±9 y, LVEF 28±9%, 31% female) and 14 were non-responders (age 68±12 y, LVEF 32±5%, 20% female). LVEF increased in responders by 15±7% and decreased in non-responders by 3±5% (p<0.001). Biventricular-paced ventricular activation patterns were significantly different between responders and non-responders: in non-responders, 8 of 14 patients (57.1%) had an activation pattern identified as mid to apical anterior or anteroseptal (Figure A) versus 6 of 35 responders (Figure B, 17.1%, p= 0.012). Left ventricular and right ventricular activation mapping results were not predictive. Conclusions: A maladaptive biventricular-paced electrical activation pattern, localized to the mid- or apical anteroseptal region of the LV, is associated with non-response to CRT. Studies of intraprocedural noninvasive LV activation mapping to improve target LV pacing site selection and response to CRT are underway.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Abstract 4139637: Artificial Intelligence ECG Mapping of Biventricular Pacing QRS Morphology Predicts Response to Cardiac Resynchronization Therapy
- Date Crossref
- 12/11/2024
- Éditeur
- Ovid Technologies (Wolters Kluwer Health)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of California San Diego pays non établi dans la noticeUniversité ou école supérieure
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University of California San Diego Medical Center pays non établi dans la noticeÉtablissement de santé
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Froedtert Hospital pays non établi dans la noticeÉtablissement de santé
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UCSD AND VA MEDICAL CENTER pays non établi dans la noticeÉtablissement de santé
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UC San Diego pays non établi dans la noticeInstitution
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Froedtert South Medical Center pays non établi dans la noticeÉtablissement de santé
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Vektor Medical pays non établi dans la noticeInstitution
University of California San Diego, University of California San Diego Medical Center et Froedtert Hospital, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.