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Monitoring the cardiac function often relies on non-invasive vital measurements, electrocardiograms, and low-resolution echocardiograms. The monitoring data can be augmented with numerical modeling results to support treatment-risk assessment. Often, medical images are not suitable for high-fidelity modeling due to their spatial sparsity and low resolution. In this work, we present a workflow that converts a limited number of two-dimensional (2D) echocardiogram images into analysis-suitable left-ventricle geometries for isogeometric analysis (IGA). The image data is fitted by reshaping a multi-patch NURBS template. This fitting procedure results in a smooth interpolation with a limited number of degrees of freedom. In addition, a coupled 3D-0D cardiac model is extended with a pre-stressing method to perform ventricular-cardiac-mechanic analyses based on in vivo images. The workflow is benchmarked using population-averaged imaging data, which demonstrates that both the shape of the left ventricle and its mechanical response are well predicted in sparse-data scenarios. The case of a clinical patient is considered to demonstrate the suitability of the workflow in a real-data setting.

Item does not contain fulltext, AIMS: The contribution of adverse ventricular interdependence remains undervalued in heart failure or pulmonary vascular disease, and not much is known about its dynamic nature during exercise and respiration. In this study, we evaluated ventricular interaction during exercise in patients with heart failure with preserved ejection fraction (HFpEF) and patients with chronic thromboembolic pulmonary hypertension (CTEPH) as compared with healthy controls. METHODS AND RESULTS: Forty-six subjects (10 controls, 19 CTEPH patients, and 17 HFpEF patients) underwent cardiac magnetic resonance imaging during exercise. Ventricular interaction was determined through analysis of the septal curvature (SC) of a mid-ventricular short-axis slice at end-diastole, end-systole, and early-diastole, both in expiration and inspiration. Exercise amplified ventricular interaction in CTEPH patients and to a lesser extent in HFpEF patients (P < 0.05 for decrease in SC with exercise). Adverse interaction was most profound in early-diastole and most pronounced in CTEPH patients (P < 0.05 interaction group * exercise) because of a disproportionate increase RV afterload (P < 0.05 to both controls and HFpEF) and diastolic pericardial restraint (P < 0.001 for interaction group * exercise) during exercise. Inspiration enhanced diastolic interdependence in CTEPH and HFpEF patients (P < 0.05 vs. expiration). Both at rest and during exercise, SC strongly correlated with RV volumes and pulmonary artery pressures (all P < 0.05). CONCLUSIONS: Exercise amplifies adverse right-left ventricular interactions in CTEPH, while a more moderate effect is observed in isolated post-capillary HFpEF. Given the strong link with RV function and pulmonary hemodynamic, assessing ventricular interaction with exCMR might be valuable from a diagnostic or therapeutic perspective.

Recent studies have shown that patients with pancreatic ductal adenocarcinoma (PDAC) treated with neoadjuvant chemo(radio)therapy followed by surgery have an improved outcome compared to patients treated with upfront surgery. Hence, patients with PDAC are more and more frequently treated with chemotherapy in the neoadjuvant setting. PDAC patients are at a high risk of developing venous thromboembolism (VTE), which is associated with decreased survival rates. As patients with PDAC were historically offered immediate surgical resection, data on VTE incidence and associated preoperative risk factors are scarce. Current guidelines recommend primary prophylactic anticoagulation in selected groups of patients with advanced PDAC. However, recommendations for patients with (borderline) resectable PDAC treated with chemotherapy in the neoadjuvant setting are lacking. Nevertheless, the prevention of complications is crucial to maintain the best possible condition for surgery. This narrative review summarizes current literature on VTE incidence, associated risk factors, risk assessment tools, and primary thromboprophylaxis in PDAC patients treated with neoadjuvant chemo(radio)therapy.

BACKGROUND: Exercise-induced cardiac remodelling (EICR) results from the structural, functional and electrical adaptations to exercise. Despite similar sports participation, EICR varies and some athletes develop phenotypic features that overlap with cardiomyopathies. Training load and genotype may explain some of the variation; however, exercise 'dose' has lacked rigorous quantification. Few have investigated the association between EICR and genotype. OBJECTIVES: (1) To identify the impact of training load and genotype on the variance of EICR in elite endurance athletes and (2) determine how EICR and its determinants are associated with physical performance, health benefits and cardiac pathology. METHODS: The Pro@Heart study is a multicentre prospective cohort trial. Three hundred elite endurance athletes aged 14-23 years will have comprehensive cardiovascular phenotyping using echocardiography, cardiac MRI, 12-lead ECG, exercise-ECG and 24-hour-Holter monitoring. Genotype will be determined using a custom cardiomyopathy gene panel and high-density single-nucleotide polymorphism arrays. Follow-up will include online tracking of training load. Cardiac phenotyping will be repeated at 2, 5, 10 and 20 years. RESULTS: The primary endpoint of the Pro@Heart study is the association of EICR with both training load and genotype. The latter will include rare variants in cardiomyopathy-associated genes and polygenic risk scores for cardiovascular traits. Secondary endpoints are the incidence of atrial and ventricular arrhythmias, physical performance and health benefits and their association with training load and genotype. CONCLUSION: The Pro@Heart study is the first long-term cohort study to assess the impact of training load and genotype on EICR. TRIAL REGISTRATION NUMBER: NCT05164328; ACTRN12618000716268.

Aim: The association of standard 12-lead electrocardiogram (ECG) markers with benefits of the primary prophylactic implantable cardioverter-defibrillator (ICD) has not been determined in the contemporary era. We analysed traditional and novel ECG variables in a large prospective, controlled primary prophylactic ICD population to assess the predictive value of ECG in terms of ICD benefit. Methods and results: Electrocardiograms from 1477 ICD patients and 700 control patients (EU-CERT-ICD; non-randomized, controlled, prospective multicentre study; ClinicalTrials.gov Identifier: NCT02064192), who met ICD implantation criteria but did not receive the device, were analysed. The primary outcome was all-cause mortality. In ICD patients, the co-primary outcome of first appropriate shock was used. Mean follow-up time was 2.4 ± 1.1 years to death and 2.3 ± 1.2 years to the first appropriate shock. Pathological Q waves were associated with decreased mortality in ICD patients [hazard ratio (HR) 0.54, 95% confidence interval (CI) 0.35–0.84; P < 0.01] and patients with pathological Q waves had significantly more benefit from ICD (HR 0.44, 95% CI 0.21–0.93; P = 0.03). QTc interval increase taken as a continuous variable was associated with both mortality and appropriate shock incidence, but commonly used cut-off values, were not statistically significantly associated with either of the outcomes. Conclusion: Pathological Q waves were a strong ECG predictor of ICD benefit in primary prophylactic ICD patients. Excess mortality among Q wave patients seems to be due to arrhythmic death which can be prevented by ICD., EU-CERT-ICD The EU-CERT-ICD Study Investigators: Elena Arbelo, MD, Axel Bauer MD, Frieder Braunschweig MD PhD, Josep Brugada MD, PhD, David Conen MD, Iwona Cygankiewicz MD, Michael Dommasch MD, Christian Eick, MD, Panagiota Flevari MD, Tim Friede PhD, Jan Galuszka MD, Jim Hansen MD, Robert Hatala MD, Markus Harden PhD, Katerina Hnatkova PhD, Heikki V. Huikuri MD, Juhani M. Junttila, MD PhD, Stefan Kääb MD, Gabriela Kaliska MD, Jaroslaw D. Kasprzak MD, Andreas Katsimardos MD, Milan Kozak MD, Tomasz Kuczejko MD, Andrzej Lubinski MD, Jozef Martinek PhD, Béla Merkely MD, PhD, Tomáš Novotný MD, Marek Malik PhD MD, Peter Perge MD, Burkert Pieske MD, Pyotr Platonov MD PhD, Pawel Ptaczyński, MD, Dariusz Qavoq MD, L. Rotkvić, MD, Zoltan Sallo MD, Simon Schlögl MD, Georg Schmidt MD, Moritz Sinner, MD, Rajeeva Sritharan MSc, Stefan Stefanow MD, Christian Sticherling MD PhD, Jesper Hastrup Svendsen MD DMSc, Martin Svetlosak MD, Janko Szavits-Nossan MD, Milos Taborsky MD, Anton Tuinenburg, MD, Bert Vandenberk MD, Marc A. Vos PhD, Rik Willems MD PhD, Stefan N Willich MD PhD, Christian Wolpert MD, Markus Zabel MD, Ante Anic, MD, Zoran Bakotic MD, Steffen Behrens MD, Dieter Bimmel MD, Sandro Brusich MD, Rüdiger Dissmann MD, Gerian Grönefeld, MD, Przemyzlav Guzik MD, Svetoslav Iovev MD, Zrinka Jurisic MD, Thomas Klingenheben, MD, Nikola Pavlović MD, Joachim Seegers MD, Robert H.G. Schwinger MD, Tchavdar Shalganov MD, Vassil Traykov MD, Vasil Velchev MD.

Fragmented QRS (fQRS) is an electrocardiographic (ECG) marker of myocardial conduction abnormality, characterized by additional notches in the QRS complex. The presence of fQRS has been associated with an increased risk of all-cause mortality and arrhythmia in patients with cardiovascular disease. However, current binary visual analysis is prone to intra- and inter-observer variability and different definitions are problematic in clinical practice. Therefore, objective quantification of fQRS is needed and could further improve risk stratification of these patients. We present an automated method for fQRS detection and quantification. First, a novel robust QRS complex segmentation strategy is proposed, which combines multi-lead information and excludes abnormal heartbeats automatically. Afterwards extracted features, based on variational mode decomposition (VMD), phase-rectified signal averaging (PRSA) and the number of baseline-crossings of the ECG, were used to train a machine learning classifier (Support Vector Machine) to discriminate fragmented from non-fragmented ECG-traces using multi-center data and combining different fQRS criteria used in clinical settings. The best model was trained on the combination of two independent previously annotated datasets and, compared to these visual fQRS annotations, achieved Kappa scores of 0.68 and 0.44, respectively. We also show that the algorithm might be used in both regular sinus rhythm and irregular beats during atrial fibrillation. These results demonstrate that the proposed approach could be relevant for clinical practice by objectively assessing and quantifying fQRS. The study sets the path for further clinical application of the developed automated fQRS algorithm.