Patient-Specific Haemodynamic Modeling to Estimate the Extent of Microvascular Disease and Response to Pulmonary Endarterectomy in Chronic Thromboembolic Pulmonary Hypertension

Behdad Shaarbaf Ebrahimi, Prashanna Khwaounjoo, Ho-Fung Chan, Finbar Argus, Xiaohui Ma, Martyn P. Nash, Atsuo Doi, Misha Dagan, David M. Kaye, Timothy Joseph, David McGiffin, Merryn H. Tawhai 

https://doi.org/10.1002/pul2.70176

Abstract

Chronic thromboembolic pulmonary hypertension (CTEPH) is a form of pulmonary hypertension that is caused by persistent obstruction of the pulmonary arteries by organized thrombi and associated microvascular disease. Pulmonary endarterectomy (PEA) is the gold standard treatment, but the extent of small vessel remodeling, which strongly influences treatment outcomes, remains difficult to quantify pre-operatively. We developed a multiscale, structure-based model of the pulmonary circulation using patient-specific vascular geometries from CT pulmonary angiography (CTPA) and haemodynamic data from right heart catheterization (RHC). Eleven CTEPH patients were included. The model estimated individual remodeling burden by fitting simulated to measured preoperative mean pulmonary artery pressure (mPAP). PEA was simulated by removing flow obstructions to predict Postoperative mPAP and pulmonary vascular resistance (PVR), both under pre- and post-PEA boundary conditions. Model predictions of post-PEA mPAP and PVR were in reasonable agreement with measured outcomes, especially when Postoperative boundary conditions were applied. Predicted changes in mPAP (∆mPAP) strongly correlated with clinical values (R = 0.81, p = 0.002), improving further with post-PEA flow parameters (R = 0.84, p = 0.001). The model captured variable haemodynamic responses to PEA, even among patients with similar Postoperative mPAP. This preliminary investigation demonstrates the feasibility of personalized computational modeling to non-invasively estimate the extent of microvascular disease and simulate postsurgical haemodynamic outcomes in CTEPH. The findings support the potential for this approach to serve as a clinical decision-making tool, with future validation in larger cohorts and integration of spatial remodeling and longitudinal data.

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