CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. Aim: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. Methods: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. Results: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. Conclusions: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources

Haemodynamic consequences of changing potassium concentrations in haemodialysis fluids

<p>Abstract</p> <p>Background</p> <p>A rapid decrease of serum potassium concentrations during haemodialysis produces a significant increase in blood pressure parameters at the end of the session, even if effects on intra-dialysis pressure are not seen. Paradoxically, in animal models potassium is a vasodilator and decreases myocardial contractility. The purpose of this trial is to study the precise haemodynamic consequences induced by acute changes in potassium concentration during haemodialysis.</p> <p>Methods</p> <p>In 24 patients, 288 dialysis sessions, using a randomised single blind crossover design, we compared six dialysate sequences with different potassium profiles. The dialysis sessions were divided into 3 tertiles, casually modulating potassium concentration in the dialysate between the value normally used K and the two cut-off points K+1 and K-1 mmol/l. Haemodynamics were evaluated in a non-invasive manner using a finger beat-to-beat monitor.</p> <p>Results</p> <p>Comparing K-1 and K+1, differences were found within the tertiles regarding systolic (+5.3, +6.6, +2.3 mmHg, p < 0.05, < 0.05, ns) and mean blood pressure (+4.3, +6.4, -0.5 mmHg, p < 0.01, < 0.01, ns), as well as peripheral resistance (+212, +253, -4 dyne.sec.cm^-5, p < 0.05, < 0.05, ns). The stroke volume showed a non-statistically-significant inverse trend (-3.1, -5.2, -0.2 ml). 18 hypotension episodes were recorded during the course of the study. 72% with K-1, 11% with K and 17% with K+1 (p < 0.01 for comparison K-1 vs. K and K-1 vs. K+1).</p> <p>Conclusions</p> <p>A rapid decrease in the concentration of serum potassium during the initial stage of the dialysis-obtained by reducing the concentration of potassium in the dialysate-translated into a decrease of systolic and mean blood pressure mediated by a decrease in peripheral resistance. The risk of intra-dialysis hypotension inversely correlates to the potassium concentration in the dialysate.</p> <p>Trial Registration Number</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT01224314">NCT01224314</a></p

Deterministic Chaos and Fractal Complexity in the Dynamics of Cardiovascular Behavior: Perspectives on a New Frontier

Physiological systems such as the cardiovascular system are capable of five kinds of behavior: equilibrium, periodicity, quasi-periodicity, deterministic chaos and random behavior. Systems adopt one or more these behaviors depending on the function they have evolved to perform. The emerging mathematical concepts of fractal mathematics and chaos theory are extending our ability to study physiological behavior. Fractal geometry is observed in the physical structure of pathways, networks and macroscopic structures such the vasculature and the His-Purkinje network of the heart. Fractal structure is also observed in processes in time, such as heart rate variability. Chaos theory describes the underlying dynamics of the system, and chaotic behavior is also observed at many levels, from effector molecules in the cell to heart function and blood pressure. This review discusses the role of fractal structure and chaos in the cardiovascular system at the level of the heart and blood vessels, and at the cellular level. Key functional consequences of these phenomena are highlighted, and a perspective provided on the possible evolutionary origins of chaotic behavior and fractal structure. The discussion is non-mathematical with an emphasis on the key underlying concepts