Yield of family screening for dilated cardiomyopathy: 10-year experience at a multidisciplinary cardiogenetic outpatient clinic.

Introduction: Current family screening approaches in dilated cardiomyopathy (DCM) depend on the presence or absence of a familial genetic variant, in which variant pathogenicity (i.e. benign or pathogenic) classification drives screening recommendations.

Long-term hemodynamic responses and reverse remodeling after pharmacotherapy in HFpEF versus HFrEF: a systematic review and meta-analysis.

The acute response to therapeutic afterload reduction differs between heart failure with preserved (HFpEF) versus reduced ejection fraction (HFrEF), with larger left ventricular (LV) stroke work augmentation in HFrEF compared to HFpEF. This may (partially) explain the neutral effect of HFrEF-medication in HFpEF. It is unclear whether such differences in hemodynamic response persist and/or differentially trigger reverse remodeling in case of long-term afterload reduction.

Automated noise modelling using a triangulated terrain model.

Noise simulations are an important part of noise studies that investigate the impact of noise sources on the environment. In noise simulation, noise levels at receiver points are calculated based on the noise propagation paths between the receiver and source points. These paths are derived from the height of the terrain.

A personalised computational model of the impact of COVID-19 on lung function under mechanical ventilation.

This work proposes a modelling framework to analyse flow and pressure distributions throughout the lung of mechanically ventilated COVID-19 patients. The methodology involves: segmentation of the lungs and major airways from patient CT images; a volume filling algorithm that creates a dichotomous airway network in the remaining volume of the lung; an estimate of resistance and compliance within the lung based on Hounsfield unit values from the CT scan; and a computational fluid dynamics model to analyse flow, lung inflation, and pressure throughout the airway network. Mechanically ventilated patients with differing progression and severity of the disease were simulated.

Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models.

Computational models can be at the basis of new powerful technologies for studying and classifying disorders like pre-eclampsia, where it is difficult to distinguish pre-eclamptic patients from non-pre-eclamptic based on pressure when patients have a track record of hypertension. Computational models now enable a detailed analysis of how pregnancy affects the cardiovascular system. Therefore, new non-invasive biomarkers were developed that can aid the classification of pre-eclampsia through the integration of six different measured non-invasive cardiovascular signals.