DEFENCE

The correct interpretation of electrocardiograms (ECGs) is crucial for accurately diagnosing cardiac abnormalities. Current methods, both manual by physicians and computerized, have not achieved the level of accuracy comparable to cardiologists in detecting acute cardiac issues. Leveraging advancements in artificial intelligence and big data, particularly deep neural networks, offers promising avenues to improve ECG interpretation where traditional methods have fallen short. The ECG-Project develops deep learning algorithms to automate ECG interpretation, particularly focusing on areas where current methods are inadequate. Through this research, we aim to revolutionize ECG interpretation, improving diagnostic accuracy, reducing healthcare resource utilization, and ultimately enhancing patient outcomes. The Research The project objectives are: WP1: Creating an algorithm capable of accurately and swiftly triaging ECGs through transfer learning, uncovering features in diseases with unknown ECG characteristics (such as primary arrhythmia syndromes and genetic disorders). WP2: design a portable multi-lead ecg-device, suitable for use by patients at home and healthcare professionals. This device will enable high-quality ECG acquisitions for rapid diagnosis. Origin This project is funded within the Innovative Medical Devices Initiative (IMDI) program 'Heart for Sustainable Care'. The focus of this program is the development of medical technology for the earlier detection, monitoring, and better treatment of cardiovascular diseases to ensure accessible healthcare and sufficient staffing. The program has been developed and funded by the Dutch Heart Foundation, ZonMw and NWO, who collaborate within the Dutch CardioVascular Alliance.

Heart failure represents a significant healthcare challenge due to its high morbidity and mortality rates. Historically, the emphasis has been on heart failure with reduced ejection fraction characterized by left ventricular dilation. However, nearly half of heart failure patients involve diastolic dysfunction due to heart chamber stiffening, known as diastolic heart failure or HFpEF. The Focus Research conducted by our consortium indicates that impaired kidney function is an is a strong risk factor for HFpEF. Patients with chronic kidney disease are more prone to developing HFpEF and have higher mortality rates from associated complications. The specific mechanisms by which even slight declines in renal function worsen cardiovascular risk and impact the development and prognosis of HFpEF are not yet fully understood. Insights from RECONNECT highlight the pivotal role of systemic inflammation and microvasculature in this context. The Research RECONNEXT (Renal connection to microvascular disease and HFpEF: the next phase) is a multicenter consortium dedicated on advancing medical research on heart failure - particularly heart failure with preserved ejection fraction (HFpEF) - in relation to impaired kidney function. Specific pre-clinical and clinical research objectives have been defined: Identify renal drivers for HFpEF onset and progression in subgroups/clusters of HFpEF patients, taking patient-specific risk profiles into account. Deepen our understanding of the mechanistic pathways involved in the pathogenic cross-talk between renal drivers, systemic inflammation, microvasculature, and cardiac cells leading to HFpEF, using dedicated ex vivo bioassays to assess patient material and in vivo small and large animal models. Investigate the most promising therapeutic targets in newly developed and well-characterized state-of-the art rodent and porcine models of CKD-associated HFpEF, taking comorbidities into account. Investigate the most promising therapeutic, diagnostic and prognostic candidate(s) in well-defined patient-groups by taking a stratified approach. We expect that the results of this project will enhance our mechanistic insight in the renal drivers of HFpEF development and progression and will lead to the development of personalized diagnostic, prognostic and therapeutic solutions for HFpEF patients. The origin The RECONNECT consortium has provided fundamental knowledge on the connection between chronic kidney disease and HFpEF and established a translational pipeline for the discovery and evaluation of potential diagnostic, prognostic and therapeutic targets. RECONNEXT builds upon the success of RECONNECT, established in 2015 (see Figure 1 below), supported by CardioVasculair Onderzoek Nederland (CVON) and the Dutch Heart Foundation. The RECONNEXT consortium consists of nephrologists, cardiologists, general practitioners, and scientists from five leading academic centers in the Netherlands (UMC Utrecht, Erasmus MC, UMC Groningen, Amsterdam UMC, Leiden University) renowned for their expertise in heart failure, vascular biology, and chronic kidney disease.