Objective: To investigate whether the correlation of age and sex with the risk of recurrence and death seen in patients with previous ischemic stroke is also evident in patients with embolic stroke of undetermined source (ESUS). Methods: We pooled datasets of 11 stroke registries from Europe and America. ESUS was defined according to the Cryptogenic Stroke/ESUS International Working Group. We performed Cox regression and Kaplan-Meier product limit analyses to investigate whether age (80 years) and sex were independently associated with the risk for ischemic stroke/TIA recurrence or death. Results: Ischemic stroke/TIA recurrences and deaths per 100 patient-years were 2.46 and 1.01 in patients 80 years old, 3.53 and 3.48 in women, and 4.49 and 3.98 in men, respectively. Female sex was not associated with increased risk for recurrent ischemic stroke/TIA (hazard ratio [HR] 1.15, 95% confidence interval [CI] 0.84–1.58) or death (HR 1.35, 95% CI 0.97–1.86). Compared with the group 80-year groups had higher 10-year cumulative probability of recurrent ischemic stroke/TIA (14.0%, 47.9%, and 37.0%, respectively, p < 0.001) and death (6.4%, 40.6%, and 100%, respectively, p < 0.001) and higher risk for recurrent ischemic stroke/TIA (HR 1.90, 95% CI 1.21–2.98 and HR 2.71, 95% CI 1.57–4.70, respectively) and death (HR 4.43, 95% CI 2.32–8.44 and HR 8.01, 95% CI 3.98–16.10, respectively). Conclusions: Age, but not sex, is a strong predictor of stroke recurrence and death in ESUS. The risk is ≈3- and 8-fold higher in patients >80 years compared with those <60 years of age, respectively. The age distribution in the ongoing ESUS trials may potentially influence their power to detect a significant treatment association.

Recent studies report that cybersecurity breaches noticed in hospitals are associated with low levels of personnel’s cybersecurity awareness. This work aims to assess the cybersecurity culture in healthcare institutions from middle- to low-income EU countries. The evaluation process was designed and performed via anonymous online surveys targeting individually ICT (internet and communication technology) departments and healthcare professionals. The study was conducted in 2019 for a health region in Greece, with a significant number of hospitals and health centers, a large hospital in Portugal, and a medical clinic in Romania, with 53.6% and 6.71% response rates for the ICT and healthcare professionals, respectively. Its findings indicate the necessity of establishing individual cybersecurity departments to monitor assets and attitudes while underlying the importance of continuous security awareness training programs. The analysis of our results assists in comprehending the countermeasures, which have been implemented in the healthcare institutions, and consequently enhancing cybersecurity defense, while reducing the risk surface.

The proliferation of Internet of Things (IoT) devices for patient monitoring has gained much attention in clinical care performance, proficient chronic disease management, and home caregiving. This work presents the design of efficient medical IoT sensor nodes (SNs) in terms of low-cost, low power-consumption, and increased data accuracy based on open-source platforms. The method utilizes a Sensor Controller (SC) within the IoT SN, which is capable of performing medical checks supporting a broad coverage of medical uses. A communication protocol has been developed for data and command exchange among SC, local gateways, and physicians’ or patients’ mobile devices (tablets, smart phones). The SC supports moving average window (MAW) and principle component analysis (PCA) filtering algorithms to capture data from the attached low-cost body sensors of different sampling profiles. Significant extensions in SN’s portability is achieved through energy consumption minimization based on the idle time gaps between sensors’ activations. SN’s components are either deactivated or set to low activity operation during these idle intervals. A medical case study is presented and the evaluated results show that the proposed SN can be incorporated into e-health platforms since it achieves comparable accuracy to its certified and high-cost commercial counterparts.

The coronavirus pandemic led to an unprecedented crisis affecting all aspects of the concurrent reality. Its consequences vary from political and societal to technical and economic. These side effects provided fertile ground for a noticeable cyber-crime increase targeting critical infrastructures and, more specifically, the health sector; the domain suffering the most during the pandemic. This paper aims to assess the cybersecurity culture readiness of hospitals’ workforce during the COVID-19 crisis. Towards that end, a cybersecurity awareness webinar was held in December 2020 targeting Greek Healthcare Institutions. Concepts of cybersecurity policies, standards, best practices, and solutions were addressed. Its effectiveness was evaluated via a two-step procedure. Firstly, an anonymous questionnaire was distributed at the end of the webinar and voluntarily answered by attendees to assess the comprehension level of the presented cybersecurity aspects. Secondly, a post-evaluation phishing campaign was conducted approximately four months after the webinar, addressing non-medical employees. The main goal was to identify security awareness weaknesses and assist in drafting targeted assessment campaigns specifically tailored to the health domain needs. This paper analyses in detail the results of the aforementioned approaches while also outlining the lessons learned along with the future scientific routes deriving from this research.

Across European countries, the SHAPES Project is piloting AI-based technologies that could improve healthcare delivery for older people over 60 years old. This article aims to present a study developed inside the SHAPES Project to find a theoretical framework focused on AI-assisted technology in healthcare for older people living in the home, to assess the SHAPES AI-based technologies using the ALTAI tool, and to derive ethical recommendations regarding AI-based technologies for ageing and healthcare. The study has highlighted concerns and reservations about AI-based technologies, namely dealing with living at home, mobility, accessibility, data exchange procedures in cross-board cases, interoperability, and security. A list of recommendations is built not only for the healthcare sector, but also for other pilot studies.