A diagnostic algorithm to optimize data collection and interpretation of Ripple Maps in atrial tachycardias

作者：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^*

来源：International Journal of Cardiology, 2015, 199: 391-400.

DOI：10.1016/j.ijcard.2015.07.017

• Mapping
• Atrial tachycardia
• Ablation
• Ripple Mapping

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 (TM) 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 (TM) 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.

• 出版日期2015-11-15