M. Seman’s research while affiliated with Monash University (Australia) and other places

A critical factor in thrombus formation during venoarterial extracorporeal membrane oxygenation (VA ECMO) is prothrombotic flow dynamics generated by the drainage cannula’s design. This study aimed to create and evaluate a novel drainage cannula design which optimized blood flow dynamics to reduce thrombus formation. Computational fluid dynamics (CFD) was used to iteratively vary drainage cannula design parameters such as inner wall shape and side hole shape. The final novel design was then placed in an ex vivo blood circulation loop, and compared against a Bio-Medicus cannula (n = 6, each). Clot volume, hemolysis, and other parameters were measured to assess thrombus formation markers. The novel design consisted of a parabolic inner wall profile with closely spaced side holes angled at 30º to align with flow. When tested in the ex vivo loop, the novel design resulted in lower instances (two vs . four) and volumes of clot in the cannula (360.5 ± 254.8 vs . 1258.0 ± 651.7 µl) when compared to the Bio-Medicus cannula. Results from tests assessing hemolysis, platelet activation, and other thrombotic markers revealed a noninferior relationship between the novel and Bio-Medicus designs. Future work will explore the clinical applicability of these findings.

An aging population and an increasing incidence of cardiovascular risk factors form the basis for a global rising prevalence of valvular heart disease (VHD). Research to further our understanding of the pathophysiology of VHD is often confined to the clinical setting. However, in recent years, sophisticated computational models of the cardiovascular system have been increasingly used to investigate a variety of VHD states. Computational modelling provides new opportunities to gain insights into pathophysiological processes that may otherwise be difficult, or even impossible, to attain in human or animal studies. Simulations of co-existing cardiac pathologies, such as heart failure, atrial fibrillation, and mixed valvular disease, have unveiled new insights that can inform clinical research and practice. More recently, advancements have been made in using models for making patient-specific diagnostic predictions. This review showcases valuable insights gained from computational studies on VHD and their clinical implications.

Heart failure with preserved ejection fraction (HFpEF) constitutes approximately 50% of heart failure (HF) cases, and encompasses different phenotypes. Among these, most patients with HFpEF exhibit structural heart changes, often with smaller left ventricular cavities, which pose challenges for utilizing ventricular assist devices (VADs). A left atrial to aortic (LA-Ao) VAD configuration could address these challenges, potentially enhancing patient quality of life by lowering elevated mean left atrial pressure (MLAP). This study assessed the anatomical compatibility and left atrial unloading capacity using a simulated VAD-supported HFpEF patient. A HeartMate3-supported HFpEF patient in an LA-Ao configuration was simulated using a cardiovascular simulator. Hemodynamic parameters were recorded during rest and exercise at seven pump flow rates. Computed tomography scans of 14 HFpEF (NYHA II–III) and six heart failure with reduced ejection fraction patients were analysed for anatomical comparisons. HFpEF models were independently assessed for virtual anatomical fit with the HM3 in the LA-Ao configuration. Baseline MLAP was reduced from 15 to 11 mmHg with the addition of 1 L/min HM3 support in the rest condition. In an exercise simulation, 6 L/min of HM3 support was required to reduce the MLAP from 29 to 16 mmHg. The HM3 successfully accommodated six HFpEF patients without causing interference with other cardiac structures, whereas it caused impingement ranging from 4 to 14 mm in the remaining patients. This study demonstrated that the HM3 in an LA-Ao configuration may be suitable for unloading the left atrium and relieving pulmonary congestion in some HFpEF patients where size-related limitations can be addressed through pre-surgical anatomical fit analysis.

Left ventricular assist devices (LVADs) have been used off-label as long-term support of the right heart due to the lack of a clinically approved durable right VAD (RVAD). Whilst various techniques to reduce RVAD inflow cannula protrusion have been described, the implication of the protrusion length on right heart blood flow and subsequent risk of thrombosis remains poorly understood. This study investigates the influence of RVAD diaphragmatic cannulation length on right ventricular thrombosis risk using a patient-specific right ventricle in silico model validated with particle image velocimetry. Four cannulation lengths (5, 10, 15 and 25 mm) were evaluated in a one-way fluid–structure interaction simulation with boundary conditions generated from a lumped parameter model, simulating a biventricular supported condition. Simulation results demonstrated that the 25-mm cannulation length exhibited a lower thrombosis risk compared to 5-, 10- and 15-mm cannulation lengths due to improved flow energy distribution (25.2%, 24.4% and 17.8% increased), reduced stagnation volume (72%, 68% and 49% reduction), better washout rate (13.0%, 11.6% and 9.1% faster) and lower blood residence time (6% reduction). In the simulated scenario, our findings suggest that a longer RVAD diaphragmatic cannulation length may be beneficial in lowering thrombosis risk; however, further clinical studies are warranted.

Purpose: Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is a complex and high-risk life support modality used in severe cardiorespiratory failure. ECMO survival scores are used clinically for patient prognostication and outcomes risk adjustment. This study aims to create the first artificial intelligence (AI)-driven ECMO survival score to predict in-hospital mortality based on a large international patient cohort. Methods: A deep neural network, ECMO Predictive Algorithm (ECMO PAL) was trained on a retrospective cohort of 18,167 patients from the international Extracorporeal Life Support Organisation (ELSO) registry (2017-2020), and performance was measured using fivefold cross-validation. External validation was performed on all adult registry patients from 2021 (N = 5015) and compared against existing prognostication scores: SAVE, Modified SAVE, and ECMO ACCEPTS for predicting in-hospital mortality. Results: Mean age was 56.8 ± 15.1 years, with 66.7% of patients being male and 50.2% having a pre-ECMO cardiac arrest. Cross-validation demonstrated an inhospital mortality sensitivity and precision of 82.1 ± 0.2% and 77.6 ± 0.2%, respectively. Validation accuracy was only 2.8% lower than training accuracy, reducing from 75.5% to 72.7% [99% confidence interval (CI) 71.1-74.3%]. ECMO PAL accuracy outperformed the ECMO ACCEPTS (54.7%), SAVE (61.1%), and Modified SAVE (62%) scores. Conclusions: ECMO PAL is the first AI-powered ECMO survival score trained and validated on large international patient cohorts. ECMO PAL demonstrated high generalisability across ECMO regions and outperformed existing, widely used scores. Beyond ECMO, this study highlights how large international registry data can be leveraged for AI prognostication for complex critical care therapies.