A new finite element based parameter to predict bone fracture

Dual Energy X-Ray Absorptiometry (DXA) is currently the most widely adopted non-invasive clinical technique to assess bone mineral density and bone mineral content in human research and represents the primary tool for the diagnosis of osteoporosis. DXA measures areal bone mineral density, BMD, which does not account for the three-dimensional structure of the vertebrae and for the distribution of bone mass. The result is that longitudinal DXA can only predict about 70% of vertebral fractures. This study proposes a complementary tool, based on Finite Element (FE) models, to improve the DXA accuracy. Bone is simulated as elastic and inhomogeneous material, with stiffness distribution derived from DXA greyscale images of density. The numerical procedure simulates a compressive load on each vertebra to evaluate the local minimum principal strain values. From these values, both the local average and the maximum strains are computed over the cross sections and along the height of the analysed bone region, to provide a parameter, named Strain Index of Bone (SIB), which could be considered as a bone fragility index. The procedure is initially validated on 33 cylindrical trabecular bone samples obtained from porcine lumbar vertebrae, experimentally tested under static compressive loading. Comparing the experimental mechanical parameters with the SIB, we could find a higher correlation of the ultimate stress, \u3c3ULT, with the SIB values (R2adj = 0.63) than that observed with the conventional DXA-based clinical parameters, i.e. Bone Mineral Density, BMD (R2adj = 0.34) and Trabecular Bone Score, TBS (R2adj = -0.03). The paper finally presents a few case studies of numerical simulations carried out on human lumbar vertebrae. If our results are confirmed in prospective studies, SIB could be used-together with BMD and TBS-to improve the fracture risk assessment and support the clinical decision to assume specific drugs for metabolic bone diseases

Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called "Learning Healthcare System Cycle," where healthcare practice and research are part of a unique and synergic process. In this paper we highlight how "Big Data enabled" integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrhythmogenic Diseases

Clustering Cardiovascular Risk Trajectories of Patients with Type 2 Diabetes Using Process Mining

[EN] Patients with type 2 diabetes have a higher chance of developing cardiovascular diseases and an increased odds of mortality. Reliability of randomized clinical trials is continuously judged due to selection, attrition and reporting bias. Moreover, cardiovascular risk is frequently assessed in cross-sectional studies instead of observing the evolution of risk in longitudinal cohorts. In order to correctly assess the course of cardiovascular riskinpatientswithtype 2 diabetes, weappliedprocessminingtechniquesbasedontheprinciples of evidence-based medicine. Using a validated formulation of the cardiovascular risk, process mining allowed to cluster frequent risk pathways and produced 3 major trajectories related to risk management: high risk, medium risk and low risk.This enables the extractionofmeaningful distributions, such as the gender of the patients per cluster in a human understandable manner, leading to more insights to improve themanagementofcardiovasculardiseasesintype2diabetes patients.This work was supported by European Commission Grant No 600914 (MOSAIC Project).Pebesma, J.; Martinez-Millana, A.; Sacchi, L.; Fernández Llatas, C.; De Cata, P.; Chiovato, L.; Bellazzi, R.... (2019). Clustering Cardiovascular Risk Trajectories of Patients with Type 2 Diabetes Using Process Mining. IEEE. 341-344. https://doi.org/10.1109/EMBC.2019.8856507S34134

Forecast model for the evaluation of economic resources employed in the health care of patients with HIV infection

BACKGROUND AND AIMS: The total health care cost for human immunodeficiency virus (HIV) patients has constantly grown in recent years. To date, there is no information about how this trend will behave over the next few years. The aim of the present study is to define a pharmacoeconomic model for the forecast of the costs of a group of chronically treated patients followed over the period 2004-2009.; METHODS: A pharmacoeconomics model was built to describe the probability of transition among different health states and to modify the therapy over time. A Markov model was applied to evaluate the temporal evolution of the average cost. The health care resources exploited during hospitalization were analyzed by using an "activity-based costing" method.; RESULTS: The Markov model showed that the mean total cost, after an initial increase, tended to remain stable. A total of 20 clinical records were examined. The average daily cost for each patient was EUR 484.42, with a cost for admission of EUR 6781.88.; CONCLUSION: The treatment of HIV infection in compliance with the guidelines is also effective from the payer perspective, as it allows a good health condition to be maintained and reduces the need and the costs of hospitalizations

What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project

[EN] Background To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. Methods The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. Results Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. Conclusions By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.The research leading to these results has received funding from the European Commission under the European Union's Seventh Framework Programme (FP7/2007-2013) grant agreement no 600914.Fico, G.; Hernandez, L.; Cancela, J.; Dagliati, A.; Sacchi, L.; Martinez-Millana, A.; Posada, J.... (2019). What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? 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Development of a Novel Bioinformatics Tool for In Silico Validation of Protein Interactions

Protein interactions are crucial in most biological processes. Several in silico methods have been recently developed to predict them. This paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions. Given a target protein, the approach selects the most likely interactors among the candidates revealed by experimental techniques, but not yet in vivo validated. The sequence and the structural information of the in vivo confirmed proteins and complexes are exploited to evaluate the candidate interactors. Finally, a score is calculated to suggest the most likely interactors of the target protein. As an example, we searched for GRB2 interactors. We ranked a set of 46 candidate interactors by the presented method. These candidates were then reduced to 21, through a score threshold chosen by means of a cross-validation strategy. Among them, the isoform 1 of MAPK14 was in silico confirmed as a GRB2 interactor. Finally, given a set of already confirmed interactors of GRB2, the accuracy and the precision of the approach were 75% and 86%, respectively. In conclusion, the proposed method can be conveniently exploited to select the proteins to be experimentally investigated within a set of potential interactors

Careflow Mining Techniques to Explore Type 2 Diabetes Evolution

In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view

Predicting Disease Complications Using a Stepwise Hidden Variable Approach for Learning Dynamic Bayesian Networks

Predicting Diabetes Type 2 Mellitus (T2DM) complications such as retinopathy and liver disease is still a challenge despite being a growing public health concern worldwide. This is due to the complex interactions between complications and other features, as well as between the different complications, themselves. What is more, there are likely to be many unmeasured effects that impact the disease progression of different patients. Probabilistic graphical models such as Dynamic Bayesian Networks (DBNs) have demonstrated much promise in the modeling of disease progression and they can naturally incorporate hidden (latent) variables using the EM algorithm. Unlike deep learning approaches that attempt to model complex interactions in data by using a large number of hidden variables, we adopt a different approach. We are interested in models that not only capture unmeasured effects but are also transparent in how they model data so that knowledge about disease processes can be extracted and trust in the model can be maintained by clinicians. As a result, we have developed a step-wise hidden variable structure learning process that incrementally adds hidden variables based on the IC∗ algorithm. To the best of our knowledge, this is the first study for classifying disease complication using a step-wise learning methodology for identifying hidden and T2DM features with a DBN structure from clinical data. Our extensive set of experiments show that the proposed method improves classification accuracy, identifying the correct number of hidden variables, and targeting their precise location within the network structure