TY - JOUR
T1 - Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia
AU - Prakosa, Adityo
AU - Arevalo, Hermenegild J.
AU - Deng, Dongdong
AU - Boyle, Patrick M.
AU - Nikolov, Plamen P.
AU - Ashikaga, Hiroshi
AU - Blauer, Joshua J.E.
AU - Ghafoori, Elyar
AU - Park, Carolyn J.
AU - Blake, Robert C.
AU - Han, Frederick T.
AU - MacLeod, Rob S.
AU - Halperin, Henry R.
AU - Callans, David J.
AU - Ranjan, Ravi
AU - Chrispin, Jonathan
AU - Nazarian, Saman
AU - Trayanova, Natalia A.
N1 - Funding Information:
This work was supported by the NIH Pioneer Award (DP1-HL123271) to N.A.T.
Publisher Copyright:
© 2018, The Author(s).
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radio-frequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here, we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (five swine) and human studies (21 patients), as well as in a prospective feasibility study (five patients). We first assessed, using retrospective studies (one of which included a proportion of clinical images with artefacts), the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart before the clinical procedure.
AB - Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radio-frequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here, we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (five swine) and human studies (21 patients), as well as in a prospective feasibility study (five patients). We first assessed, using retrospective studies (one of which included a proportion of clinical images with artefacts), the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart before the clinical procedure.
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U2 - 10.1038/s41551-018-0282-2
DO - 10.1038/s41551-018-0282-2
M3 - Article
C2 - 30847259
AN - SCOPUS:85053341969
SN - 2157-846X
VL - 2
SP - 732
EP - 740
JO - Nature biomedical engineering
JF - Nature biomedical engineering
IS - 10
ER -