A diagnostic algorithm to optimize data collection and interpretation of Ripple Maps in atrial tachycardias. (15th November 2015)

• Record Type: Journal Article
• Authors: Koa-Wing, Michael; Nakagawa, Hiroshi; Luther, Vishal; Jamil-Copley, Shahnaz; Linton, Nick; Sandler, Belinda; Qureshi, Norman; Peters, Nicholas S.; Davies, D. Wyn; Francis, Darrel P.; Jackman, Warren; Kanagaratnam, Prapa
• Is Part Of: International journal of cardiology. Volume 199(2015)
• Page Start: 391
• Page End: 400
• Publication Date: 2015-11-15
• Subjects: Mapping -- Atrial tachycardia -- Ablation -- Ripple Mapping; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2015.07.017
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: Ripple Mapping (RM) is designed to overcome the limitations of existing isochronal 3D mapping systems by representing the intracardiac electrogram as a dynamic bar on a surface bipolar voltage map that changes in height according to the electrogram voltage–time relationship, relative to a fiduciary point. Objective: We tested the hypothesis that standard approaches to atrial tachycardia CARTO™ activation maps were inadequate for RM creation and interpretation. From the results, we aimed to develop an algorithm to optimize RMs for future prospective testing on a clinical RM platform. Methods: CARTO-XP™ activation maps from atrial tachycardia ablations were reviewed by two blinded assessors on an off-line RM workstation. Ripple Maps were graded according to a diagnostic confidence scale (Grade I — high confidence with clear pattern of activation through to Grade IV — non-diagnostic). The RM-based diagnoses were corroborated against the clinical diagnoses. Results: 43 RMs from 14 patients were classified as Grade I (5 [11.5%]); Grade II (17 [39.5%]); Grade III (9 [21%]) and Grade IV (12 [28%]). Causes of low gradings/errors included the following: insufficient chamber point density; window-of-interest < 100% of cycle length (CL); < 95% tachycardia CL mapped; variability of CL and/or unstable fiducial reference marker; and suboptimal bar height and scar settings. Conclusions: A data collection and map interpretation algorithm has been developed to optimize Ripple Maps in atrial tachycardias. This algorithm requires prospective testing on a real-time clinical platform.