A Patient-specific Computational Model for Neonates and Infants with Borderline Left Ventricles

Borderline left ventricle (BLV) presents a dilemma between pursuing a biventricular repair (BiVR) and a Stage 1 palliation (S1P) because a discordant pursuit of BiVR increases mortality risk. We aim to develop and validate a personalized computational model to assist surgical decision-making by predicting virtual surgery hemodynamics in BLV patients.

We developed a novel multi-block lumped parameter network (LPN) model of a BLV circulatory system. Patient-specific model parameters were estimated using a semi-automatic tuning framework to fit clinical data in ten retrospectively identified BLV patients. Virtual surgeries (BiVR and S1P) were performed on each patient to quantify post-operative hemodynamics.

In patients who clinically received S1P (Group I, N=5), a virtual BiVR predicted significantly elevated mean pulmonary artery pressure (PAP_mean: 38.00±10.0 vs. 17.50±2.7 mmHg, p<0.01), mean left atrial pressure (LAP_mean: 25.40±8.2 vs. 6.20±1.2 mmHg, p<0.0001), and single ventricle end-diastolic pressure (SVEDP: 21.80±8.7 vs. 4.80±1.3 mmHg, p<0.0001) compared with a virtual S1P. A virtual BiVR in patients who clinically underwent BiVR (Group II, N=5) did not predict any adverse hemodynamic outcome.

A novel digital twinning framework was developed to predict hemodynamics following virtual surgeries in BLV patients. The model predictions align with the clinically adopted procedure in this retrospectively selected cohort by predicting unacceptable PAP, LAP, and SVEDP. This predictive tool may guide surgeons in determining the hemodynamically optimal surgery for BLV infants, but it needs prospective validation.