Ambulatory factors influencing pulmonary artery pressure waveforms and implications for clinical practice

Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images

One-dimensional (1D) cardiovascular models offer a non-invasive method to answer medical questions, including predictions of wave-reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient-specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty in in vivo imaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image-derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three-dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high-fidelity patient-specific haemodynamics models. KEY POINTS: This study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error. Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks. Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty. Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii.

The War Against Heart Failure Hospitalizations: Remote Monitoring and the Case for Expanding Criteria

Successful remote patient monitoring depends on bidirectional interaction between patients and multidisciplinary clinical teams. Invasive pulmonary artery pressure monitoring has been shown to reduce heart failure (HF) hospitalizations, facilitate guideline-directed medical therapy optimization, and improve quality of life. Cardiac implantable electronic device-based multiparameter monitoring has shown encouraging results in predicting future HF-related events. Potential expanded indications for remote monitoring include guideline-directed medical therapy optimization, application to specific populations, and subclinical detection of HF. Voice analysis, inferior vena cava diameter monitoring, and artificial intelligence-based remote electrocardiogram show potential to gain some merit in remote patient monitoring in HF.