The Evolution of Telehealth in Heart Failure Management: The Role of Large Language Models and HerzMobil as a Potential Use Case.

The burgeoning domain of telehealth has witnessed substantial transformation through the advent of advanced technologies such as Large Language Models (LLMs). This study examines the integration of LLMs in heart failure management, with a focus on HerzMobil as a pioneering telehealth program. The technical underpinnings of LLMs, their current applications in the medical field, and their potential to enhance telehealth services, have been explored. The paper highlights the benefits of LLMs in patient interaction, clinical documentation, and decision-making processes. Through the HerzMobil case study, improvements in patient self-management and reductions in hospital readmission rates have been observed, showcasing the successful application of telehealth in chronic disease management. The paper also delves into the challenges and ethical considerations of LLM integration, such as data privacy, potential biases, and regulatory compliance, underscoring the need for a balanced approach that prioritizes patient safety and ethical standards.

HerzMobil Tirol PreOp - A Multidisciplinary Telemonitoring Project for Heart Failure Patients Prior to Cardiac Surgery.

BACKGROUND: Patients with heart failure are at risk of perioperative complications with elective cardiac surgery. OBJECTIVES: Conception of a multidisciplinary telemedicine-assisted optimisation project for high-risk patients prior to elective cardiac surgery. METHODS: Multidisciplinary concept design. RESULTS: A pilot-project for 30 patients was developed. CONCLUSION: Design of the first preoperative telemonitoring-assisted optimisation project for high-risk patients undergoing cardiac surgery.

Roxadustat Attenuates Adverse Remodeling Following Myocardial Infarction in Mice.

Activation of the CXCL12/CXCR4/ACKR3 axis is known to aid myocardial repair through ischemia-triggered hypoxia-inducible factor-1α (HIF-1α). To enhance the upregulation of HIF-1α, we administered roxadustat, a novel prolyl hydroxylase inhibitor (PHI) clinically approved by the European Medicines Agency 2021 for the treatment of renal anemia, with the purpose of improving LV function and attenuating ischemic cardiomyopathy. METHODS: We evaluated roxadustat's impact on HIF-1 stimulation, cardiac remodeling, and function after MI. Therefore, we analyzed nuclear HIF-1 expression, the mRNA and protein expression of key HIF-1 target genes (RT-PCR, Western blot), inflammatory cell infiltration (immunohistochemistry), and apoptosis (TUNEL staining) 7 days after MI. Additionally, we performed echocardiography in male and female C57BL/6 mice 28 days post-MI. RESULTS: We found a substantial increase in nuclear HIF-1, associated with an upregulation of HIF-1α target genes like CXCL12/CXCR4/ACKR3 at the mRNA and protein levels. Roxadustat increased the proportion of myocardial reparative M2 CD206+ cells, suggesting beneficial alterations in immune cell migration and a trend towards reduced apoptosis. Echocardiography showed that roxadustat treatment significantly preserved ejection fraction and attenuated subsequent ventricular dilatation, thereby reducing adverse remodeling. CONCLUSIONS: Our findings suggest that roxadustat is a promising clinically approved treatment option to preserve myocardial function by attenuating adverse remodeling.

Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses.

Introduction: The potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes. Materials and methods: Our proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network. Results: In a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria's national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested. Discussion: The presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.