Association of patient-reported outcomes and heart rate trends in heart failure. a report from the chiron project

Patient-reported outcomes (PROs) have been previously considered “soft” end-points because of the lack of association of the reported outcome to measurable biological parameters. The present study aimed to assess whether electrocardiographic measures are associated to PROs changes. We evaluated the association between heart rate (HR), QRS and QT/QTc durations and PROs, classified as “good” or “bad” according to the patients’ overall feeling of health, in patients from the Chiron project. Twenty-four chronic heart failure (HF) patients were enrolled in the study (71% male, mean age 62.9 ± 9.4 years, 42% ischemic etiology, 15 NYHA class II and 9 class III) providing 1086 days of usable physiological recordings (4 hours/day). The mean HR was significantly higher in the “bad” than in the “good” PROs class (74.0 ± 6.4 bpm vs 68.0 ± 7.2 bpm; p < 0.001). Conversely, the ratio between movement and rest activities showed significantly higher values in “good” compared to “bad” PROs. We also found significantly longer QTc and QRS durations in patients with “bad” PROs compared to patients with “good” PROs. That in patients with mild to moderate HF, higher HR, wider QRS and longer QTc, as well as a reduced HR ratio between movement and rest, were associated with “bad” PROs is clinically noteworthy because the association of worse PROs with measurable variations of biological parameters may help physicians in evaluating PROs reliability itself and in their clinical decisions. Whether a timely intervention on these biological parameters may prevent adverse outcomes is important and deserves to be investigated in further studies

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

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

Fall Detection with Unobtrusive Infrared Array Sensors

As the world’s aging population grows, fall is becoming a major problem in public health. It is one of the most vital risks to the elderly. Many technology based fall detection systems have been developed in recent years with hardware ranging from wearable devices to ambience sensors and video cameras. Several machine learning based fall detection classifiers have been developed to process sensor data with various degrees of success. In this paper, we present a fall detection system using infrared array sensors with several deep learning methods, including long-short-term-memory and gated recurrent unit models. Evaluated with fall data collected in two different sets of configurations, we show that our approach gives significant improvement over existing works using the same infrared array sensor

Fall Classification by Machine Learning Using Mobile Phones

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls

Catalyzing Transcriptomics Research in Cardiovascular Disease : The CardioRNA COST Action CA17129

Cardiovascular disease (CVD) remains the leading cause of death worldwide and, despite continuous advances, better diagnostic and prognostic tools, as well as therapy, are needed. The human transcriptome, which is the set of all RNA produced in a cell, is much more complex than previously thought and the lack of dialogue between researchers and industrials and consensus on guidelines to generate data make it harder to compare and reproduce results. This European Cooperation in Science and Technology (COST) Action aims to accelerate the understanding of transcriptomics in CVD and further the translation of experimental data into usable applications to improve personalized medicine in this field by creating an interdisciplinary network. It aims to provide opportunities for collaboration between stakeholders from complementary backgrounds, allowing the functions of different RNAs and their interactions to be more rapidly deciphered in the cardiovascular context for translation into the clinic, thus fostering personalized medicine and meeting a current public health challenge. Thus, this Action will advance studies on cardiovascular transcriptomics, generate innovative projects, and consolidate the leadership of European research groups in the field.COST (European Cooperation in Science and Technology) is a funding organization for research and innovation networks (www.cost.eu)

Medical expert support tool (MEST): a person-centric approach for healthcare management

The Medical Expert Support Tool (MEST) is aimed at helping the clinician in recognizing risk factors in the patient status by offering a multiparametric overview, and by highlighting the individual situation using meaningful colors (green, yellow and red) in order to compare the person physiological parameters with the computed profile. The medical professionals will configure the conditions (relevant parameters, thresholds, rules and alerts) setting the values to the decision support modules and receiving the risk assessment results. Finally, interventions should be done depending on the evaluation of the patient. The tool has been designed along with the clinician involved in the project and it will be fully tested and evaluated during the observational study (100 patients) starting on June 2012