Rationale and design of the PACIFIC-PRESERVED (PhenomApping, ClassIFication and Innovation for Cardiac dysfunction in patients with heart failure and PRESERVED left ventricular ejection fraction) study.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome that is poorly defined, reflecting an incomplete understanding of its pathophysiology. AIM: To redefine the phenotypic spectrum of HFpEF. METHODS: The PACIFIC-PRESERVED study is a prospective multicentre cohort study designed to perform multidimensional deep phenotyping of patients diagnosed with HFpEF (left ventricular ejection fraction≥50%), patients with heart failure with reduced ejection fraction (left ventricular ejection fraction≤40%) and subjects without overt heart failure (3:2:1 ratio). The study proposes prospective investigations in patients during a 1-day hospital stay: physical examination; electrocardiogram; performance-based tests; blood samples; cardiac magnetic resonance imaging; transthoracic echocardiography (rest and low-level exercise); myocardial shear wave elastography; chest computed tomography; and non-invasive measurement of arterial stiffness. Dyspnoea, depression, general health and quality of life will be assessed by dedicated questionnaires. A biobank will be established. After the hospital stay, patients are asked to wear a connected garment (with digital sensors) to collect electrocardiography, pulmonary and activity variables in real-life conditions (for up to 14 days). Data will be centralized for machine-learning-based analyses, with the aim of reclassifying HFpEF into more distinct subgroups, improving understanding of the disease mechanisms and identifying new biological pathways and molecular targets. The study will also serve as a platform to enable the development of innovative technologies and strategies for the diagnosis and stratification of patients with HFpEF. CONCLUSIONS: PACIFIC-PRESERVED is a prospective multicentre phenomapping study, using novel analytical techniques, which will provide a unique data resource to better define HFpEF and identify new clinically meaningful subgroups of patients.

The PACIFIC ontology for heterogeneous data management in cardiology.

With the emergence of health data warehouses and major initiatives to collect and analyze multi-modal and multisource data, data organization becomes central. In the PACIFIC-PRESERVED (PhenomApping, ClassIFication, and Innovation for Cardiac Dysfunction - Heart Failure with PRESERVED LVEF Study, NCT04189029) study, a data driven research project aiming at redefining and profiling the Heart Failure with preserved Ejection Fraction (HFpEF), an ontology was developed by different data experts in cardiology to enable better data management in a complex study context (multisource, multiformat, multimodality, multipartners). The PACIFIC ontology provides a cardiac data management framework for the phenomapping of patients. It was built upon the BMS-LM (Biomedical Study -Lifecycle Management) core ontology and framework, proposed in a previous work to ensure data organization and provenance throughout the study lifecycle (specification, acquisition, analysis, publication). The BMS-LM design pattern was applied to the PACIFIC multisource variables. In addition, data was structured using a subset of MeSH headings for diseases, technical procedures, or biological processes, and using the Uberon ontology anatomical entities. A total of 1372 variables were organized and enriched with annotations and description from existing ontologies and taxonomies such as LOINC to enable later semantic interoperability. Both, data structuring using the BMS-LM framework, and its mapping with published standards, foster interoperability of multimodal cardiac phenomapping datasets.

Tracking of Blood Vessels Motion from 4D-flow MRI Data.

This paper presents a novel approach to track objects from 4D-flow MRI data. A salient feature of the proposed method is that it fully exploits the geometrical and dynamical nature of the information provided by this imaging modality. The underlying idea consists in formulating the tracking problem as a data assimilation problem, in which both position and velocity observations are extracted from the 4D-flow MRI data series. Optimal state estimation is then performed in a sequential fashion via Kalman filtering. The capabilities of the method are extensively assessed in a numerical study involving synthetic and clinical data.

Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets.

A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI.