Heart failure (HF) is one of the most complex chronic disorders with high prevalence, mainly due to the ageing population and better treatment of underlying diseases. Prevalence will continue to rise and is estimated to reach 3% of the population in Western countries by 2025. It is the most important cause of hospitalisation in subjects aged 65 years or more, resulting in high costs and major social impact. The current “one-size-fits-all” approach in the treatment of HF does not result in best outcome for all patients. These facts are an imminent threat to good quality management of patients with HF. An unorthodox approach from a new vision on care is required. We propose a novel predictive, preventive and personalised medicine approach where patients are truly leading their management, supported by an easily accessible online application that takes advantage of artificial intelligence. This strategy paper describes the needs in HF care, the needed paradigm shift and the elements that are required to achieve this shift. Through the inspiring collaboration of clinical and high-tech partners from North-West Europe combining state of the art HF care, artificial intelligence, serious gaming and patient coaching, a virtual doctor is being created. The results are expected to advance and personalise self-care, where standard care tasks are performed by the patients themselves, in principle without involvement of healthcare professionals, the latter being able to focus on complex conditions. This new vision on care will significantly reduce costs per patient while improving outcomes to enable long-term sustainability of top-level HF care.

Background: Cognitive impairments occur frequently in patients with chronic heart failure (CHF), resulting in worse health outcomes than expected. These impairments can remain undetected unless specifically screened. There are limited sensitive screening measures available in nursing practice to identify mild cognitive impairment (MCI). Aim: To compare the Montreal Cognitive Assessment (MoCA) with the Mini Mental State Exam (MMSE) in screening for MCI in CHF patients. Methods: The MMSE and MoCA were administered to 93 hospitalized CHF patients (70±11 years), without a history of neurocognitive problems. Patients with low MoCA scores (<26) were compared to those with low MMSE scores (<27). Two different parameters were examined between the MoCA and the MMSE: level of MCI agreement (Kappa coefficient) and task errors on assessed cognitive domains (χ² test). Results: Statistically more patients had low MoCA scores compared with low MMSE scores (66 vs. 30, p=0.02). The MoCA classified 38 (41%) patients as cognitively impaired that were not classified by the MMSE. A significantly low level of agreement was found (κ=0.25, p=0.001) between the MMSE and MoCA in identifying patients with scores suggestive of MCI. More task errors were observed on the MoCA cognitive domains compared with the MMSE cognitive domains. In 68% of patients with low cognitive scores, visuospatial task errors were observed on tasks from the MoCA compared with 22% on a similar task of the MMSE. Conclusion: The MoCA, a screening tool for MCI, identified subtle but potentially clinically relevant cognitive dysfunctions with greater frequency than MMSE.