Exciting to see part of Juliet Nagawa's PhD work published.

"Patient-Specific In Silico Prediction of Outcomes of Partial Continuous-Flow LVAD Treatment in Peripartum Cardiomyopathy" explores how computational "digital twin" models can help us better understand and predict the response of women with peripartum cardiomyopathy (PPCM) to mechanical circulatory support.

Using patient-specific biventricular finite element models derived from cardiac MRI data and coupled to a circulatory system model, we investigated the effects of partial continuous-flow LVAD support in six PPCM patients with varying disease severity.

Key findings include: 
✅ LVAD support reduced left ventricular myofiber stress and improved ejection fraction.
✅ The magnitude and pattern of improvement varied substantially between patients.
✅ Patients with more severe disease generally showed greater benefit from LVAD support.
✅ In the most severely affected patients, the LVAD became the primary source of stroke volume, highlighting the importance of individualized treatment strategies.

These findings reinforce the value of patient-specific computational modeling for understanding cardiac mechanics, optimizing device settings, and advancing precision medicine in cardiovascular care.

Many thanks to all co-authors and collaborators who made this interdisciplinary work possible.

🔗 Publication: https://onlinelibrary.wiley.com/doi/10.1002/cnm.70188