Predicting the risk of post-hepatectomy portal hypertension using a digital twin: a clinical proof of concept.

Abstract Background & Aims Despite improvements of medical and surgical techniques, post-hepatectomy liver failure (PHLF) remains the leading cause of death in that context. High postoperative portal vein pressure (PPV) and portocaval gradient (PCG) are the most important determinants of PHLF, and are not predictable using current tools. Our aim was therefore to evaluate a digital twin to predict the risk of postoperative portal hypertension (PHT). Methods We prospectively included 47 patients resected for major hepatectomy. A mathematical (0D) model of the entire blood circulation was assessed and automatically calibrated from patient characteristics. Hepatic flows were obtained from preoperative flow MRI (n=9), intraoperative flowmetry (n=16), or estimated from cardiac output (n=47). Resection was then simulated in these three groups and the PPV and PCG computed were compared to intraoperative data. Results Simulated post-hepatectomy pressures did not differ between the three groups, comparing well with collected data (no significant differences). In the entire cohort, the correlation between measured and simulated PPV values was good (r=0.66, no adjustment to intraoperative events) or excellent (r=0.75) after adjustment, as well as for PCG (respectively r=0.59 and r=0.80). The difference between simulated and measured post-hepatectomy PCG was ≤3mmHg in 96% of cases. Four patients suffered from lethal PHLF for whom the model satisfactorily predicted their postoperative pressures. Conclusions We demonstrated that a 0D model could correctly anticipate postoperative PHT, even using estimated hepatic flow rates as input data. If this major conceptual step is confirmed, this algorithm could change our practice toward more tailor-made procedures, while ensuring satisfactory outcomes.