Why the study?
Can patient-specific computational models based on clinically acquired S2 extrastimuli data accurately predict complex activation patterns following sequential S3 extrastimuli in patients with structurally normal hearts?
Observational (n=11)
No
Can patient-specific computational models based on clinically acquired S2 extrastimuli data accurately predict complex activation patterns following sequential S3 extrastimuli in patients with structurally normal hearts?
Effect estimate: R2 0.902 (95% CI 0.890-0.911)
p-value: p=<0.0001
Simulations based on clinically acquired electrophysiological data can successfully predict complex activation patterns following sequential extrastimuli, validating cable theory in humans.
Supports patient-specific modeling feasibility from clinical data; leaves open validation in diseased hearts and clinical translation.
INTRODUCTION: Computerized simulations of cardiac activity have significantly contributed to our understanding of cardiac electrophysiology, but techniques of simulations based on patient-acquired data remain in their infancy. We sought to integrate data acquired from human electrophysiological studies into patient-specific models, and validated this approach by testing whether electrophysiological responses to sequential premature stimuli could be predicted in a quantitatively accurate manner. METHODS: Eleven patients with structurally normal hearts underwent electrophysiological studies. Semi-automated analysis was used to reconstruct activation and repolarization dynamics for each electrode. This S 2 extrastimuli data was used to inform individualized models of cardiac conduction, including a novel derivation of conduction velocity restitution. Activation dynamics of multiple premature extrastimuli were then predicted from this model and compared against measured patient data as well as data derived from the ten-Tusscher cell-ionic model. RESULTS: Activation dynamics following a premature S 3 were significantly different from those after an S 2. Patient specific models demonstrated accurate prediction of the S 3 activation wave, (Pearson's R (2) = 0.90, median error 4%). Examination of the modeled conduction dynamics allowed inferences into the spatial dispersion of activation delay. Further validation was performed against data from the ten-Tusscher cell-ionic model, with our model accurately recapitulating predictions of repolarization times (R (2) = 0.99). CONCLUSIONS: Simulations based on clinically acquired data can be used to successfully predict complex activation patterns following sequential extrastimuli. Such modeling techniques may be useful as a method of incorporation of clinical data into predictive models.
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Finlay et al. (2013) conducted an observational in Structurally normal hearts with paroxysmal supraventricular tachycardia (n=11). Patient-specific one-dimensional conduction model vs. Measured in vivo patient data was evaluated on Prediction of activation time dynamics following a premature S3 extrastimulus (R2 0.902, 95% CI 0.890-0.911, p=<0.0001).
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