Key result
Personalized mechanical modeling using adjoint-based data assimilation revealed that patients with left bundle branch block had significantly lower peak active strain and stress compared to healthy controls (p<0.001).
Observational (n=14)
p-value: p=<0.001
Adjoint-based data assimilation can rapidly and accurately personalize cardiac mechanical models to patient-specific echocardiographic data, revealing significant differences in contractility between LBBB patients and healthy controls.
May support contractility assessment in LBBB via personalization; leaves open clinical utility pending prospective validation.
Cardiac computational models, individually personalized, can provide clinicians with useful diagnostic information and aid in treatment planning. A major bottleneck in this process can be determining model parameters to fit created models to individual patient data. However, adjoint-based data assimilation techniques can now rapidly estimate high dimensional parameter sets. This method is used on a cohort of heart failure patients, capturing cardiac mechanical information and comparing it with a healthy control group. Excellent fit (R2 ≥ 0.95) to systolic strains is obtained, and analysis shows a significant difference in estimated contractility between the two groups.
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Finsberg et al. (2017) conducted an observational in Left bundle branch block (n=14). Left bundle branch block vs. Healthy volunteers was evaluated on Peak active strain and stress (contractility) (p=<0.001). Personalized mechanical modeling using adjoint-based data assimilation revealed that patients with left bundle branch block had significantly lower peak active strain and stress compared to healthy controls (p<0.001).
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