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Gradient variability in hypertrophic cardiomyopathy: New insights from computer-assisted, high fidelity, rest and
Terry D Bauch1, Alexandra J Smith2, Joseph P Murgo3
1Geisinger Heart and Vascular Institute, Danville, Pennsylvania.
Insights
Computer analysis of hemodynamic variability in hypertrophic cardiomyopathy (HCM) can predict obstructive severity. Beat-to-beat variations and post-ectopic pressures offer insights into left ventricular outflow tract (LVOT) gradients.
Area of Science:
- Cardiology
- Hemodynamics
- Biomedical Engineering
Background:
- Hypertrophic cardiomyopathy (HCM) is characterized by left ventricular outflow tract (LVOT) obstruction, with significant intrapatient variability in gradient severity.
- Predictors for maximal LVOT gradients in HCM remain incompletely understood, necessitating further investigation into quantitative analysis methods.
Observation:
- High-fidelity left ventricular and aortic pressure waveforms were analyzed using computer-assisted methods.
- Data acquisition included resting state (659 beats) and supine exercise (379 beats) in a symptomatic patient with obstructive HCM.
- Analysis focused on variability in peak instantaneous LVOT gradients and aortic pulse pressures.
Findings:
- Consistent regression slopes characterizing energy loss were observed between LV and aorta at rest and during exercise.
- Resting beat-to-beat variability and post-ectopic pressures provided similar predictive information for maximal gradients in obstructive HCM.
- Computer-assisted analysis of hemodynamic variability may characterize obstruction severity in HCM.
Implications:
- Hemodynamic variability analysis offers a potential tool for assessing obstruction severity in hypertrophic cardiomyopathy.
- Further research is needed to confirm the reproducibility and clinical utility of these findings, particularly for exercise-induced gradients.
Objectives:
This study examines the intrapatient variability in peak instantaneous left ventricular outflow tract (LVOT) gradients and aortic pulse pressures during rest, exercise, and after ventricular ectopy.
Background:
Although the variability in LVOT gradients in patients with hypertrophic cardiomyopathy (HCM) is well known, the predictors of such variation are not. We hypothesized that quantitative invasive analysis of gradient variation could identify useful predictors of maximal gradients.
Methods:
Variability in continuously recorded, high-fidelity left ventricular and aortic pressure waveforms were evaluated by computer-assisted analysis in the resting state (N = 659 beats) and during supine exercise (N = 379 beats) in a symptomatic patient with a resting LVOT gradient >30 mmHg and frequent ventricular ectopy.
Results:
At rest, the peak left ventricular and aortic pressures at the time of the peak instantaneous LVOT gradient for all sinus and postectopic beats followed consistent regression slopes characterizing the potential energy loss between the LV cavity and aorta. During exercise, similar regression slopes were identified, and these converged with the resting slopes at the point of the maximal measured LVOT gradient. Component analysis of the LVOT gradient suggests that resting beat-to-beat variability provides information similar to post-ectopic pressures for predicting maximal gradients in obstructive-variant HCM.
Conclusions:
Our study suggests that computer-assisted analysis of hemodynamic variability in HCM may prove useful in characterizing the severity of obstruction. Further study is warranted to confirm the reproducibility and utility of this finding in a population with clinically significant exercise-induced gradients.
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