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    Computer-generated Clinical Decision-making in the Treatment of Pulmonary Atresia with Intact Ventricular Septum
    (Springer, 2025) Yildirim, Canberk; Ural, Berk; Odemis, Ender; Donmazov, Samir; Pekkan, Kerem
    Purpose Pulmonary atresia with intact ventricular septum is a multifactorial disease requiring complex surgeries. The treatment route is determined based on the right ventricle (RV) size, tricuspid annulus size and coronary circulation dependency of RV. Since multiple parameters influence the post-operative success, a personalized decision-making based on computed hemodynamics is hypothesized to improve the treatment efficacy. Methods A lumped parameter cardiovascular model is developed to calculate the hemodynamics of virtual patients which are generated by statistical distribution of circulation parameters. Four cohorts each with 30 digital patients are grouped based on RV size. For each patient, biventricular and one-and-half ventricle (1.5 V) repair were applied in silico and assessed via pressure, flow and saturations computed for every organ bed. Results Biventricular and 1.5 V repair yield significant increase in the pulmonary flow and oxygen saturation for all patients compared to the pre-operative state (p-values < 0.001). Approximately 30% of generated patients failed to meet the sufficient saturation and flow following biventricular repair and were directed to 1.5 V repair. However, 14% of these 1.5 V repair patients failed post-operatively, requiring Fontan completion. Based on the pre-determined hemodynamics criteria, this study implies that patients having RV sizes larger than 22 ml/m(2) are likely to undergo successful biventricular repair. Conclusion Pending further clinical trials, computational pre-interventional planning has the potential to screen patients that would not optimally fit to the traditional pathway prior to in vivo execution by providing personalized hemodynamic outcome. Statistical approach allows in silico clinical trials, useful for diseases with low patient numbers.