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The Ebola virus disease (EVD) epidemic that occurred in West Africa between 2014-16 resulted in over 28,000 cases and 11,000 deaths -one of the deadliest to date. A generalized model of the spatiotemporal progression of EVD for Liberia, Guinea, and Sierra Leone in 2014-16 remains elusive. There is also a disconnect in the literature on which interventions are most effective in curbing disease progression. To solve these two key issues, we designed a hybrid agent-based and compartmental model that switches from one paradigm to the other on a stochastic threshold. We modeled disease progression with promising accuracy using WHO datasets.

BackgroundPersonalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFRbaseline), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines.AimsTo determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFRinvasive) as gold standard.Materials and MethodsPersonalized coronary geometries (N=50) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFRbaseline, was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFRbaseline was validated against FFRinvasive and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFRstreamlined was created by only incorporating the most sensitive inputs and FFRsemi-streamlined additionally included patient-specific distal location.ResultsFFRbaseline was validated against FFRinvasive via correlation (r=0.714, p<0.001), agreement (mean difference: 0.01±0.09), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFRsemi-streamlined provided identical diagnostic performance with FFRbaseline. Compared to FFRbaseline vs. FFRinvasive, FFRstreamlined vs. FFRinvasive had decreased correlation (r=0.64, p<0.001), improved agreement (mean difference: 0.01±0.08), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90).ConclusionStreamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement.

Purpose Peripheral arteriovenous malformations (pAVMs) are congenital lesions characterised by abnormal high-flow, low-resistance vascular connections—the so-called nidus—between arteries and veins. The mainstay treatment typically involves the embolisation of the nidus, however the complexity of pAVMs often leads to uncertain outcomes. This study aims at developing a simple, yet effective computational framework to aid the clinical decision making around the treatment of pAVMs using routinely acquired clinical data. Methods A computational model was developed to simulate the pre-, intra-, and post-intervention haemodynamics of a patient-specific pAVM. A porous medium of varying permeability was employed to simulate the sclerosant effect on the nidus haemodynamics. Results were compared against clinical data (digital subtraction angiography, DSA, images) and experimental flow-visualization results in a 3D-printed phantom of the same pAVM. Results The computational model allowed the simulation of the pAVM haemodynamics and the sclerotherapy-induced changes at different interventional stages. The predicted inlet flow rates closely matched the DSA-derived data, although the post-intervention one was overestimated, probably due to vascular system adaptations not accounted for numerically. The nidus embolization was successfully captured by varying the nidus permeability and increasing its hydraulic resistance from 0.330 to 3970 mmHg s ml−1. The nidus flow rate decreased from 71% of the inlet flow rate pre-intervention to 1%: the flow completely bypassed the nidus post-intervention confirming the success of the procedure. Conclusion The study demonstrates that the haemodynamic effects of the embolisation procedure can be simulated from routinely acquired clinical data via a porous medium with varying permeability as evidenced by the good qualitative agreement between numerical predictions and both in vivo and in vitro data. It provides a fundamental building block towards a computational treatment-planning framework for AVM embolisation.

Purpose: Peripheral arteriovenous malformations (pAVMs) are congenital lesions characterised by abnormal high-flow, low-resistance vascular connections - constituting the so-called nidus - between arteries and veins. The mainstay treatment typically involves the embolisation of the nidus with embolic and sclerosant agents, however the complexity of AVMs often leads to uncertain outcomes. This study aims at developing a simple, yet effective computational framework to aid the clinical decision making around the treatment of pAVMs. Methods: A computational model was developed to simulate the pre-, intra-, and post-intervention haemodynamics of an AVM. A porous medium of varying permeability was used to simulate the effect that the sclerosant has on the blood flow through the nidus. The computational model was informed by computed tomography (CT) scans and digital subtraction angiography (DSA) images, and the results were compared against clinical data and experimental results. Results: The computational model was able to simulate the blood flow through the AVM throughout the intervention and predict (direct and indirect) haemodynamic changes due to the embolisation. The simulated transport of the dye in the AVM was compared against DSA time-series obtained at different intervention stages, providing confidence in the results. Moreover, experimental data obtained via a mock circulatory system involving a patient specific 3D printed phantom of the same AVM provided further validation of the simulation results. Conclusion: We developed a simple computational framework to simulate AVM haemodynamics and predict the effects of the embolisation procedure. The developed model lays the foundation of a new, computationally driven treatment planning tool for AVM embolisation procedures.